i

Draft Final Evaluation Report of the Medicaid Reform Section

1115 Demonstration

Deliverable 25: Medicaid Reform Evaluation (MRE) Year 1 and 2 Final

Summary Report of Domains i – ix

July 2015October 2014

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Table of Contents

Executive Summary ............................................................................................................................................................. 1

Introduction 1

Key Findings 2

Background .......................................................................................................................................................................... 9 Purpose of Report 9

Goals and Research Questions 10

Data and Methods ............................................................................................................................................................. 15 General Findings: Domain i ................................................................................................................................................ 36

Domain i.a: Are services accessible to enrollees? Have there been changes in the accessibility of services to

enrollees over the course of the demonstration? Has the demonstration resulted in more appropriate use

of services by enrollees? 36

Domain i.b: Has the quality of care that enrollees receive improved during the demonstration? What have

managed care plans done to improve quality of care? 53

Domain i.c: How has the demonstration increased timeliness of services? 64

Domain i.d: How has the demonstration affected growth of Medicaid costs? 66

General Findings: Domain ii ............................................................................................................................................... 71 Domain ii.a: To what extent do health plans offer customized benefits? How much variation is there between

plans’ benefit packages? Are there plans whose customized benefits are geared to particular populations?71

Domain ii.b: When presented the opportunity, do plans provide additional services not previously covered by

Medicaid? If so, what types of services? To what extent do enrollees use these additional services? 71

Domain ii.c: Are there differences in enrollees’ satisfaction with and experiences with care between plans with

different benefit packages? Between plans that offer additional benefits vs. those that do not? 82

Domain ii.d: Does access to and quality of care vary between plans with different benefit packages? Between

plans that offer additional benefits vs. those that do not? 87

General Findings: Domain iii .............................................................................................................................................. 92 Domain iii.a: To what extent do enrollees earn Enhanced Benefits? To what extent do they spend their rewards?92

Domain iii .b: Is the Enhanced Benefits program associated with increased use of preventive

services by enrollees? 96

Domain iii .c: Is there a difference in services used by enrollees participating in the Enhanced

Benefits Account (EBA) program vs. enrollees who do not in Reform and Non -Reform

counties? 96

Domain iii .d: Is there variation in the likelihood of participation in certain health care

behaviors between enrollees in Reform and Non -Reform counties? 105

Domain iii .e: To what extent does participation in the Enhanced Benefits Account (EBA)

program vary by characteristics of enrollees (e.g., race/ethnicity, chronic il lness, and plan

type)? 107

Domain iii .f: Is there a difference in rates of avoidable hospitalizations and emergency

department use among Enhanced Benefits Account (EBA) users (high, med ium, low) and

non-users? 109

Domain iii.g: What is the effect of Enhanced Benefits participation on total expenditures? 111

General Findings: Domain iv ..................................................................................................................................... 113 Integrated Qualitative Analyses 113

Quantitative Analyses – Trends from Fiscal Years 2010 – 2013 119

General Findings: Domain v ...................................................................................................................................... 123

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Domain v: The effect of LIP funding on the number of uninsured and underinsured, and rate of

uninsurance 123

General Findings: Domain vi ..................................................................................................................................... 129 Domain vi: The effect of LIP funding on disparit ies in the provision of healthcare services, both

geographically and by population groups 129

General Findings: Domain vii ................................................................................................................................... 134 Domain vi i: The impact of Tier -One and Tier-Two Milestone init iatives on access to care and

quality of care (including safety, effectiveness, patient centered ness, t imeliness, efficiency,

and equity) 134

General Findings: Domain viii .................................................................................................................................. 138 Domain vi ii : The impact of Tier -One and Tier-Two Milestone initiatives on population health 138

General Findings: Domain ix ..................................................................................................................................... 141 Domain ix: The impact of Tier -One and Tier-Two Milestone initiatives on per -capita costs

(including Medicaid, uninsured, and underinsured populations) and the cost-effectiveness

of care 141

Summary and Conclusions ............................................................................................................................................... 145 Domains i and ii 145

Domain iii 151

Domain iv 154

Domains v – ix 158

Appendices: Domains i and ii ........................................................................................................................................... 162 Appendix A: CAHPS Survey Data 162

Appendix B: Description of HEDIS Measures 179

Appendix C: Claims, Eligibility, and Encounter Data: Fiscal Analyses Inclusion and Exclusion Criteria 181

Appendix D: Expanded Benefits Detailed Chart 182

Appendix E: Detailed CAHPS and HEDIS Tables and Figures by Domain 189

Appendix F: Claims, Eligibility, and Encounter Data: Analyses 256

Appendix G: Plan Benefits Detailed Tables 259

Appendices: Domain iii .................................................................................................................................................... 267 Appendix H: EBA Changes Over Time 267

Appendix I: Description of Preventive Service Category 269

Appendix: Domain iv ....................................................................................................................................................... 272 Appendix J: MED150: Florida International University, Evaluation of the Florida Medicaid Reform Demonstration

to Deter Fraud and Abuse, Final Report, Year 1 and Year 2 Evaluation Findings 272

Executive Summary ............................................................................................................................................... 275 Aims and Methods 275

Key Findings 275

Introduction .......................................................................................................................................................... 277 Literature Review .................................................................................................................................................. 278

Defining Health Care Fraud and Abuse 278

The Impact of Fraud and Abuse in Health Care 281

History of National Fraud Control Efforts 282

Florida’s Efforts to Prevent and Detect Medicaid Fraud and Abuse 282

Florida Statutory Requirements for Medicaid Managed Care Plans 283

Methods ................................................................................................................................................................ 284 Qualitative Analyses 284

Quantitative Analyses 287

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Findings ................................................................................................................................................................. 288 Integrated Qualitative Analyses 288

Quantitative Analyses – Trends from Fiscal Years 2010-2013 296

Summary and Discussion ....................................................................................................................................... 300 References ............................................................................................................................................................ 302

Appendices: Domains v - ix .............................................................................................................................................. 305 Appendix K: Summary of Approved Tier-One Milestone Initiatives: New and Enhanced 305

Appendix L: Summary of Approved Tier-Two Initiatives 306

Appendix M: State Specific Quality Measures and Federal Core Measures Detail 310

Sources ............................................................................................................................................................................ 312 References ....................................................................................................................................................................... 314

Domains i and ii 314

Domain iii 315

Domain iv 315

Domains v – ix 318

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Draft Final Evaluation Report of the Medicaid Reform Section 1115 Demonstration Deliverable 25: Medicaid Reform Evaluation (MRE) Year 1 and 2 Final Summary Report of Domains i – ix

Executive Summary

Introduction

Florida’s Section 1115 Medicaid research and demonstration project was initially planned as a five-year project extending from July 1, 2006, through June 30, 2011. The focus of the demonstration was to transition most Medicaid enrollees in five counties (Broward, Duval, Baker, Clay, and Nassau) to a managed care form of medical care delivery. The demonstration also allowed health plans to offer customized benefit packages and reduced cost-sharing. In addition, the demonstration included the introduction of the Enhanced Benefits Account (EBA) program, also known as the Enhanced Benefits Reward$ Program. Enrollees who engaged in an approved list of health behaviors would receive a credit, which could be redeemed at a Medicaid participating pharmacy. The demonstration also intended to deter fraud and abuse by moving from a fee-for-service to a managed care delivery system. Another core aspect of the demonstration was the establishment of the Low Income Pool (LIP). The LIP was intended to supply additional funding for providers with large numbers of patients who were low-income and/or with little or no insurance coverage. The LIP “Tier-One Milestones” or “Tier-One,” is broadly defined as the development and implementation of a State initiative that requires Florida to allocate $50 million in total LIP funding in DY7: State Fiscal Year (SFY) 2012–2013, and DY8: SFY 2013–2014, to establish new, or enhance existing, innovative programs that meaningfully enhance the quality of care and the health of low income populations, the timely submission of provider reconciliations and demonstration deliverables, including the annual “LIP Milestone Statistics and Findings Report” and the “Primary Care and Alternative Delivery Systems Expenditure Report.” STC #85, also referred to in this document as “Tier-Two Milestones” or “Tier-Two,” requires that the Top 15 hospitals that are allotted the largest annual distributions of LIP funds (“Top 15”) participate in initiatives that broadly derive from the three overarching goals of CMS’ Three-Part Aim which is described in more detail in the Data and Methods section of this document under the heading STC #84: Tier-One Milestones. On December 15, 2011, the Centers for Medicare and Medicaid Services (CMS) approved the Agency for Health Care Administration’s (AHCA’s) request to extend the demonstration through June 30, 2014. Contained within the Special Terms and Conditions of the waiver renewal are requirements for an evaluation of the demonstration during the renewal period. Nine domains of focus were identified for the evaluation. This report addresses research questions associated with each of the nine domains, including

Domain i: The effect of managed care on access to care, quality and efficiency of care, and the cost of care;

Domain ii: The effect of customized benefit plans on enrollees’ choice of plans, access to care, and quality of care;

Domain iii: Participation in the Enhanced Benefits Account program and its effect on participant behavior or health status;

Domain iv: The impact of the demonstration as a deterrent against Medicaid fraud and abuse;

Domain v: The effect of LIP funding on the number of uninsured and underinsured, and rate of uninsurance;

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Domain vi: The effect of LIP funding on disparities in the provision of healthcare services, both geographically and by population groups;

Domain vii: The impact of Tier-One and Tier-Two Milestone initiatives on access to care and quality of care (including safety, effectiveness, patient centeredness, timeliness, efficiency, and equity);

Domain viii: The impact of Tier-One and Tier-Two milestone initiatives on population health; and

Domain ix: The impact of Tier-One and Tier-Two milestone initiatives on per-capita costs (including Medicaid, uninsured, and underinsured populations) and the cost-effectiveness of care.

Key Findings

Domains i and ii Due to various data issues with the Medicaid eligibility and claims data, the results of the Domains i and ii expenditure and utilization data analyses should be interpreted with caution. Domain i.a: Are services accessible to enrollees? Have there been changes in the accessibility of services to enrollees over the course of the demonstration? Has the demonstration resulted in more appropriate use of services by enrollees?

Overall, respondent self-reports indicate that enrollees in the Reform counties perceive services to be accessible. There were increases across several access measures in both Reform and Non-Reform counties between Demonstration Year (DY) 6 and DY8. In general, changes, whether increases or decreases, were not statistically significant.

To some extent, there appear to be improvements in respondent self-report of obtaining health services. Over time, there was a significant increase in the percentage of enrollees having a personal doctor in urban Reform counties. Moreover, there was a statistically significant increase over time in the percentage of enrollees who saw a doctor for non-urgent care one to three times in the previous six months and a decrease in the percentage of enrollees with four or more non-urgent visits.

Enrollees in Reform counties in DY8 reported greater ease of getting specialist appointments as needed (“Always”) than in the previous year, though the change was not statistically significant.

Based on HEDIS measurement trend analysis, Reform plans have improved the appropriate utilization of services in some areas but not done as well in others. The main area of strength is with the Annual Dental Visits measure, while they have not done as well with three of the Adults’ Access to Preventive/Ambulatory Health Services measure components (Ages 20 – 44, Ages 65+, and Ages Total) and Postpartum Care.

Overall, per-member, per-month (PMPM) utilization of health services (inpatient, outpatient, medical, pharmacy, and emergency room visits) was greater in the Reform counties compared to the Control counties for both Temporary Assistance for Needy Families (TANF) and Supplemental Social Security Income (SSI) enrollees for DY5 – DY7. Furthermore, the rate of growth was greater in the Reform counties relative to the Control counties, for all services except physician office-based services (medical). Reform counties will achieve lower utilization rates over time for physician office-based services (medical) if the current trend continues in the future. However, this conclusion cannot be made regarding inpatient, outpatient, medical, and pharmacy health services.

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Domain i.b: Has the quality of care that enrollees receive improved during the demonstration? What have managed care plans done to improve quality of care?

The quality of care that enrollees receive has improved during the demonstration for 10 of 12 HEDIS chronic disease measures. The measures that declined had minimal average annual decreases.

CAHPS results indicate that in the urban Reform counties there was a significant increase over time in enrollees reporting the highest level rating for their health plans. Increases in other measures were not statistically significant. In rural Reform counties, none of the changes over time were statistically significant. However, the proportion of enrollees in rural Reform counties who reported the highest level rating decreased from DY7 to DY8 on three out of the four measures. In the Reform counties overall (both urban and rural counties), no change (whether increase or decrease) was statistically significant over time for ratings of health care, personal doctor, and specialists.

No major differences were found, in the actions carried out, between plans that had a positive or negative Average Annual Change for the HEDIS measures. Most of the health plans had similar actions for all of their measures reported on the Performance Measure Action Plans (PMAPs).

Domain i.c: How has the demonstration increased timeliness of services?

There is indication that there might be some improvement in the reports of being able to access care in a timely manner in the urban counties, where there was a statistically significant increase over time in the percentage of enrollees who were “Always” able to get urgent and non-urgent care as soon as they wanted. From DY6 to DY8 in Reform counties, there was a significant increase in the percentage of enrollees who reported “Always” getting urgent care right away and getting an appointment as soon as they needed.

Domain i.d: How has the demonstration affected the growth of Medicaid costs?

Overall, PMPM expenditures were greater in the Reform counties compared to the Control counties for SSI enrollees. However, for SSI enrollees, the rate of growth was lower in the Reform counties relative to the Control counties, suggesting that the Reform counties will achieve savings over time if the current trend continues in the future. On the other hand, for TANF enrollees, while expenditures were lower in Reform counties, the rate of growth is higher in the Reform counties compared to the Control counties. This suggests that for TANF enrollees, Reform counties will not achieve savings over time if the current trend continues in the future.

Domain ii.a: To what extent do health plans offer customized benefits? How much variation is there between plans’ benefit packages? Are there plans whose customized benefits are geared to particular populations?

Health plans offered customized benefits to a minimal extent, and the scope of expanded benefit packages was small. The number of plans that offered expanded benefits increased from 12 to 13 between 2007 and 2013. The number of plans that did not offer expanded benefits decreased from three in 2007 to one in 2013.

Plan limits did not vary greatly between 2007 and 2013, nor did they vary greatly between plans. Plans that made changes to limits between 2007 and 2013 only made slight changes, with the majority of plans changing limits related to pharmacy and vision services in all years for both the SSI and TANF eligibility groups.

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Many standard services were covered completely and the number of plans requiring copays decreased from nine in 2007 to three in 2013.

Two plans were geared towards specific populations. One plan was for children with special healthcare needs and their siblings. The second plan was for individuals who are HIV positive and their household members.

Domain ii.b: When presented the opportunity, do plans provide additional services not previously covered by Medicaid? If so, what types of services? To what extent do enrollees use these additional services?

Most plans slightly decreased the number of expanded benefits available to beneficiaries between 2007 and 2013. South Florida Community Care Network was the only plan in existence in both years that offered adult dental expanded benefits in 2007 and 2013. United Healthcare – Baker, Clay, Nassau, & Duval was the only plan in existence in both years that offered over-the-counter (OTC) pharmacy expanded benefits in both 2007 and 2013.

During the period 2007 – 2013, the number of plans providing additional services or expanded benefits decreased. There are no data that clearly indicate which additional services were used, only whether or not they were offered.

Domain ii.c: Are there differences in enrollees’ satisfaction with and experiences with care between plans with different benefit packages? Between plans that offer additional benefits vs. those that do not?

There was a significant increase over time in the percentage of enrollees who would recommend their health plan to family and friends (“Definitely Yes”), within all four groups of plans (plans that provided over-the-counter expanded benefits, plans that did not provide over-the-counter expanded benefits, plans that provided adult dental benefits, and plans that did not provide adult dental benefits). There was also a significant increase over time in the percentage of enrollees receiving adult dental benefits who gave the highest rating (Level 3) to their health plan and personal doctor. Other trends either increased or decreased over time, though most were not statistically significant.

Satisfaction ratings increased across most measures from DY6 to DY8 within plans that offered OTC expanded benefits and, to a lesser extent, within plans that offered adult dental benefits.

Across DY6 through DY8, the differences in enrollee satisfaction ratings in plans that provided additional benefits (over-the-counter and adult dental) and plans that did not were not systematic, nor were they statistically significant except for one survey item.

Domain ii.d: Does access to and quality of care vary between plans with different benefit packages? Between plans that offer additional benefits vs. those that do not?

Overall, there was an increase in access and quality of care from DY7 to DY8 within each of the four enrollee comparison groups (OTC expanded benefits, no OTC expanded benefits, adult dental benefits, and no adult dental benefits). Over time, however, most of the increases were not statistically significant.

In DY8, there was a statistically significant difference in the percentage of enrollees who reported they were “always” able to get an appointment as soon as they needed in plans that provided extra benefits (both over the counter and adult dental) compared to plans that did not provide these extra benefits.

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However, for DY6 through DY8, the differences in enrollee satisfaction ratings in plans that provided additional benefits (over-the-counter and adult dental) and plans that did not were not systematic, nor were they statistically significant.

Domain iii Due to various data issues with the Medicaid eligibility and claims data, the results of the Domain iii data analyses should be interpreted with caution. Domain iii.a: To what extent do enrollees earn Enhanced Benefits? To what extent do they spend their rewards?

During DY2 – DY7, $63,820,095 in Enhanced Benefits Account (EBA) credits were earned by enrollees, and 60% of the earned credits were spent on eligible purchases.

Preventive care for children and adults accounted for nearly half (49%) of the total credits earned. In DY3, the EBA credit earnings associated with office visits dropped to almost one fifth of the previous year’s earnings in the same category because of a policy change that reduced the dollar credit associated with office visits and capped the number of eligible annual office visits. In subsequent years, both the number of office visits and credit earnings increased but did not return to levels seen in DY2.

Cancer screening behaviors accounted for 4.4% of credit earning, while participation in disease management and diabetes management programs accounted for only 1% of all credit earnings; participation in other healthy behaviors such as smoking cessation, alcohol and substance abuse programs, and fitness programs were reported 5,352 times over six years.

DY7 was the second year when adult BMI assessment was eligible for EBA rewards and 2,783 credit-earning occurrences (up from 1,962 in DY6) were recorded.

A sizeable portion of eligible Medicaid enrollees (55 – 60% yearly) did not earn credits. Domain iii.b: Is the Enhanced Benefits program associated with increased use of preventive services by enrollees?

While preventive services accounted for a great share of the health behaviors for which credits were earned, it is not clear that such behaviors increased in frequency during the Medicaid Reform time period. In fact, the policy change to reduce the amount and frequency of rewards for office visits reflected a concern that the program was rewarding behaviors that would have occurred even in the absence of the EBA program. Furthermore, even if increased frequency could be clearly documented, it is yet another step to assess whether or not and how such an increase might be associated with the EBA program itself.

Domain iii.c: Is there a difference in services used by enrollees participating in the EBA program vs. enrollees who do not in Reform and Non-Reform counties?

Enrollees in Medicaid Reform counties had on average more claims and encounters than Medicaid enrollees in Non-Reform counties for most services. Reform enrollees who had earned more EBA credits had more claims and encounters on average than Reform enrollees who had earned fewer credits.

Within urban Medicaid Reform counties, “high” earners and non-earners in general used more outpatient and inpatient services than enrollees in the “low” and “medium” earning categories, in all years. Enrollees who did not earn any EBA credits had more claims than some EBA earners for medical, inpatient, and outpatient services.

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Domain iii.d: Is there variation in the likelihood of participation in certain healthcare behaviors between enrollees in Reform and Non-Reform counties?

During the study period, enrollees in Medicaid Reform and Non-Reform counties had a similar number of cancer screening claims and cancer screening encounters per member per year. Within urban Medicaid Reform counties, “high” EBA earners had more claims and encounters for cancer screening services than non-earners and earners in the “low” and “medium” groups. For dental screening services, enrollees in the urban Medicaid Reform counties that were “medium” earners had more dental screening encounters per member per year than “low,” “high,” and non-earners.

Domain iii.e: To what extent does participation in the EBA program vary by characteristics of enrollees (e.g., race/ethnicity, chronic illness, and plan type)?

Between DY2 and DY7, 58% of EBA enrollees were females, 46% were black, 62% resided in Broward County, and most (86%) had Medicaid eligibility through TANF.

In DY3 and DY4, enrollees had statistically significantly lower odds of earning rewards than in DY2, and in DY5 through DY7 they had significantly greater odds of earning rewards than in DY2.

TANF-eligible individuals had statistically significantly greater odds of earning rewards than SSI-eligible individuals.

Domain iii.f: Is there a difference in rates of avoidable hospitalizations and emergency department use among EBA users (high, medium, low) and non-users?

Overall, enrollees had less than one episode of avoidable hospitalization and emergency department (ED) visits per person per year.

Enrollees in the low and medium EBA earning levels had the lowest rates in each of the six years.

Domain iii.g: What is the effect of Enhanced Benefits program participation on total expenditures?

Enrollee health expenditures were highest for the “high” EBA earner level and non-earners. Expenditures for non-earners were fairly steady DY2 to DY6 and decreased by approximately half from DY6 to DY7. Expenditures for enrollees in the “high” earner category doubled from DY2 to DY3 but steadily declined DY3 to DY7. Health expenditures for “low” and “medium” earners were fairly constant at approximately $2,000 and $2,500 per member per year, respectively, throughout the demonstration.

Overall, enrollees’ total health expenditures in urban Medicaid Reform counties increased over the study years. The greatest change was noted among “high” earners, whose expenditures more than doubled from DY2 to DY3, then decreased only slightly in the next four years. “High” earners had the greatest expenditures of all four earning categories, followed by “non-earners,” “medium” earners, and “low” earners. “High” earners had the greatest expenditures of all four earning categories, followed by non-earners, “medium” earners, and “low” earners.

Domain iv

Domain iv.a: What are the program integrity-related measures employed by the health plans in the Demonstration related to: deterring fraud and abuse by network and non-network providers; deterring fraud and abuse by recipients; detecting fraud and abuse by network and non-network providers; and detecting fraud and abuse by recipients?

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The Agency requires managed care plans to adopt an anti-fraud plan addressing the detection and prevention of overpayments, abuse, and fraud, which is submitted for approval by September 1 each year.

The Agency requires Quarterly Anti-Fraud and Abuse Reports which include information on the specific detection tools utilized for each suspected fraud and abuse incident.

The Agency requires an Annual Fraud and Abuse Report which include investigations of potentially fraudulent or abusive acts during the prior fiscal year. These reports must include, at a minimum: The dollar amount of health plan losses and recoveries attributable to overpayment, abuse and fraud; and the number of health plan referrals to the Bureau of Medicaid Program Integrity (Bureau of MPI).

Within the managed care programs, a variety of internal policies and procedures were identified. Overall, there was general agreement that data analysis and the use of pre-payment and post-payment fraud detection tools were the most effective methods for both prevention and detection. The use of fraud detection software is increasing both within plans and by the Agency itself.

Domain iv.b: How often do health plan compliance officers/teams interact with providers in the health plan networks? What types of contact and interactions do the compliance officers/teams have with providers? How do plans document and track their efforts to deter fraud and abuse?

The frequency and the nature of interactions between managed care health plan compliance officers/teams and providers focused primarily on targeted educational sessions and communication of updated policies and procedures rather than being restricted to specific scheduled times. The communication frequency also depended on the needs of the providers – compliance officers indicated continuous and immediate availability to providers as needed and/or requested.

Various methods of communication between compliance officers and providers were utilized depending on the type and urgency of the issues. Communication between parties occurred by mailed correspondence, such as newsletters, as well as by phone and email.

Procedures for tracking and documenting of efforts above and beyond those required by the Agency to deter fraud and abuse vary across managed care plans. While some use manual systems, others utilize computer software programs. The use of data analysis is an increasingly important tool in detection and deterrence of fraud and abuse.

Domain iv.c: How do health plan compliance officers/teams measure the effectiveness of the health plan policies and procedures related to program integrity?

A variety of techniques and measures were identified by the compliance officers as means to gauge effectiveness including: tracking soft dollar savings, periodic reviews and updates to their policies in response to the previous year’s activities, monitoring volume of cases, time constraints on cases, amounts recovered from providers, internal audit outcomes, and monthly audits of their investigators.

While the techniques for measuring the effectiveness of policies and procedures varied among plans, there was general agreement that data analysis and the use of pre-payment and post-payment anti-fraud tools were the most effective methods for both prevention and detection.

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Domains v – ix

Domain v: The effect of LIP funding on the number of uninsured and underinsured, and rate of uninsurance.

Overall, the number of uninsured, underinsured, and Medicaid individuals served along with the types and number of outpatient services furnished by non-hospital providers have increased. For hospital providers, the number of individuals served with Medicaid has increased but the number of uninsured and underinsured individuals served has decreased.

Domain vi: The effect of LIP funding on disparities in the provision of healthcare services, both geographically and by population groups.

Overall, the Tier-One and Tier-Two initiatives decreased disparities in the provision of healthcare services in both urban and rural geographic locations and for multiple demographic, socioeconomic, and disease-specific population groups. For instance, the initiatives improved access to primary care and disease management services for individuals with chronic heart failure (CHF), chronic obstructive pulmonary disease (COPD), diabetes, patients with sickle cell disease, the uninsured, and low income individuals.

Domain vii: The impact of Tier-One and Tier-Two Milestone initiatives on access to care and quality of care (including safety, effectiveness, patient centeredness, timeliness, efficiency, and equity).

Access to care was the focus of approximately 60% of the Tier-One initiatives, and approximately 40% of the Tier-One initiatives focused on quality of care. Of the Tier-Two initiatives, 70% or more impacted both quality of care and access to care. Overall, the Tier-One and Tier-Two initiatives provided better care coordination and increased access to primary care and disease management services.

Domain viii: The impact of Tier-One and Tier-Two Milestone initiatives on population health.

Tier-One and Tier-Two Milestone initiatives affected population health by increasing access to primary care services; improving management of chronic illnesses such as diabetes, hypertension, and cardiovascular disease; and focusing on population groups including but not limited to women, children, and the homeless. Specific activities included implementation of Specialty Care Coordination Programs (SCCP) for cardiovascular and pulmonary conditions, implementation of protocols to reduce infections in neonates, and the development and use of a depression screening tool to help primary care providers (PCPs) identify low-income patients with depression.

Domain ix: The impact of Tier-One and Tier-Two Milestone initiatives on per-capita costs (including Medicaid, uninsured, and underinsured populations) and the cost-effectiveness of care.

While the impacts varied, the Tier-One and Tier-Two initiatives impacted per-capita costs and the cost-effectiveness of care by providing coordinated acute, disease management, and preventive primary care medical, dental, and behavioral health services with the goal of reducing the numbers of avoidable emergency department and inpatient visits. Initiatives included implementing Emergency Department Diversion Programs (EDD), Readmission Reduction Programs (RRP), and establishing condition-specific outpatient clinics, as well as others.

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Background

Purpose of Report

This document is part of a series of reports produced by the University of Florida (UF) in evaluating Florida’s Medicaid Reform demonstration during its renewal from July 2011 through June 2014. These evaluation studies build upon and extend research undertaken during the previous contract period. For more background and previous contract deliverables, see http://ahca.myflorida.com/Medicaid/medicaid_Reform/index.shtml and http://ahca.myflorida.com/Medicaid/quality_management/mrp/contracts/med027/index.shtml The Florida Agency for Health Care Administration (AHCA), or the Agency, contracted with UF to conduct the analyses for Domains i – iii and v – ix and with Florida International University (FIU) to conduct the analyses for Domain iv. This report is a comprehensive summary of all the analyses completed by both UF and FIU. This report is organized according to the research questions and the data sources utilized to answer each research question associated with Domains i – ix as described in the Florida Medicaid Reform Evaluation Plan: 2011 – 2014 (Florida Agency for Health Care Administration, 2012). The appendices include additional details of the analytic methodology, including tables and figures for the various data sources. Please note that the waiver amendment approved by the Centers for Medicare and Medicaid Services (CMS) on June 14, 2013, changed the name of the waiver from “Medicaid Reform” to “Managed Medical Assistance” (Centers for Medicare & Medicaid Services, 2013). The Managed Medical Assistance (MMA) program with Section 1115 waiver authority is part of the Statewide Medicaid Managed Care (SMMC) program that began operating on May 1, 2014. The SMMC program also includes a statewide long-term care program with Section 1915(b) and 1915(c) waiver authority. It is important to note that this report is an evaluation of the “Medicaid Reform” program and many of the analyses describe comparisons between “Reform and Non-Reform” or “Reform and Control” counties. So managed care plans in the Reform counties (under 1115 waiver authority) are being compared to managed care plans in the Non-Reform counties (under 1915(b) waiver authority).

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Goals and Research Questions

Domains i and ii The focus of the demonstration was to transition most Medicaid enrollees in five counties (Broward, Duval, Baker, Clay, and Nassau) to a managed care form of medical care delivery. The demonstration also allowed health plans to offer customized benefit packages and reduced cost-sharing. The Domain i analyses examine the effect of managed care on access to care, quality and efficiency of care, and the cost of care; and the Domain ii analyses assess the effect of customized benefit plans on enrollees’ choice of plans, access to care, and quality of care. Domain i Research Questions:

a. Are services accessible to enrollees? Have there been changes in the accessibility of services to enrollees over the course of the demonstration? Has the demonstration resulted in more appropriate use of services by enrollees?

b. Has the quality of care that enrollees receive improved during the demonstration? What have managed care plans done to improve quality of care?

c. How has the demonstration increased timeliness of services? d. How has the demonstration affected the growth of Medicaid costs?

Domain ii Research Questions:

a. To what extent do health plans offer customized benefits? How much variation is there between plans’ benefit packages? Are there plans whose customized benefits are geared to particular populations?

b. When presented the opportunity, do plans provide additional services not previously covered by Medicaid? If so, what types of services? To what extent do enrollees use these additional services?

c. Are there differences in enrollees’ satisfaction with and experiences with care between plans with different benefit packages? Between plans that offer additional benefits vs. those that do not?

d. Does access to and quality of care vary between plans with different benefit packages? Between plans that offer additional benefits vs. those that do not?

Domain iii

In the Waiver Special Term and Condition (STC) #40, the Enhanced Benefits Account (EBA) program is described as providing “incentives to Medicaid Reform enrollees for participating in state-defined activities that promote healthy behaviors. An individual who participates in a state-defined activity that promotes healthy behaviors earns credits that are posted to an individual’s account. Earned credits may be used for healthcare related expenditures as approved...”(Centers for Medicare & Medicaid Services, 2011, p. 17). On December 15, 2011, CMS extended the Florida Medicaid Reform Section 1115 Research and Demonstration waiver, including the EBA program, through June 30, 2014.

The Domain iii analyses provided in this report addresses research questions related to participation in

the EBA program and its effect on participant behavior or health status.

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Domain iii Research Questions: a. To what extent do enrollees earn Enhanced Benefits? To what extent do they spend their

rewards? b. Is the Enhanced Benefits program associated with increased use of preventive services by

enrollees? c. Is there a difference in services used by enrollees participating in the EBA program vs. enrollees

who do not, in Reform and Non-Reform counties? d. Is there variation in the likelihood of participation in certain healthcare behaviors between

enrollees in Reform and Non-Reform counties? e. To what extent does participation in the EBA program vary by characteristics of enrollees (e.g.,

race/ethnicity, chronic illness, and plan type)? f. Is there a difference in rates of avoidable hospitalizations and emergency department use

among EBA users (high, medium, low) and non-users? What is the effect of Enhanced Benefits program participation on total expenditures?

Domain iv One of the objectives under the Demonstration Waiver is to “serve as an effective deterrent against fraud and abuse by moving from a fee-for-service to a managed care delivery system.” In conjunction with this objective, the State engaged a research team from Florida International University (FIU) in Miami, Florida. The evaluation focused on two specific objectives of the demonstration program: 1) To serve as an effective deterrent against fraud and abuse by moving from a fee-for-service to a managed care delivery system; and 2) To maintain strict oversight of managed care plans including adapting efforts to the surveillance of fraud and abuse within the managed care system.

The Domain iv analyses measure the effectiveness of Reform in serving as a deterrent against fraud and abuse and in maintaining oversight of the managed care plan policies and procedures that deter fraud and abuse. Domain iv Research Questions:

a. What are the program integrity-related measures employed by the health plans in the Demonstration related to: deterring fraud and abuse by network and non-network providers; deterring fraud and abuse by recipients; detecting fraud and abuse by network and non-network providers; and detecting fraud and abuse by recipients?

b. How often do health plan compliance officers/teams interact with providers in the health plan networks? What types of contact and interactions do the compliance officers/teams have with providers? How do plans document and track their efforts to deter fraud and abuse?

c. How do health plan compliance officers/teams measure the effectiveness of the health plan policies and procedures related to program integrity?

Domains v – ix The Low Income Pool (LIP) program was implemented July 1, 2006, as part of a broad Medicaid Reform demonstration program which in December 2011 was extended through June 30, 2014. The objective of the LIP program is to ensure continued government support for the provision of healthcare services to Medicaid, underinsured, and uninsured populations. The LIP program consists of a capped annual allotment of $1 billion for each year of the demonstration (the “pool”), funded primarily by intergovernmental transfers from local governments matched by federal funds.

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The LIP non-federal share of the $1 billion LIP distributions is funded by local governments, such as counties, hospital taxing districts and other state agencies (e.g. Florida Department of Health). The availability of LIP funds to the State on an annual basis is subject to any penalties that are assessed by CMS for the failure to meet milestones as discussed in subject area XIV Low Income Pool Milestones of the Special Terms and Conditions (STCs). Available funds not distributed in a demonstration year (DY) may be rolled over to the next DY. In the Waiver Special Terms and Conditions (STC), STC #54 states that “LIP funds may be used for healthcare expenditures (medical care costs or premiums) that would be within the definition of medical assistance in Section 1905(a) of the Social Security Act. These healthcare expenditures may be incurred by the State, by hospitals, clinics, or by other provider types for uncompensated medical care costs of medical services for the uninsured and underinsured for which compensation is not available from other payors, including other Federal or State programs” (Centers for Medicare & Medicaid Services, 2011, p. 21). The Reimbursement and Funding Methodology document for LIP expenditures, submitted to CMS on June 26, 2006, included the definition of expenditures eligible for Federal Matching funds under the LIP and entities eligible to receive reimbursement. An Updated Final Reimbursement and Funding Methodology document, that includes a reporting methodology for the number of individuals and types of services provided through the LIP, was submitted to CMS on February 1, 2013 (Florida Agency for Health Care Administration, 2013b). The objective of these analyses is to provide a summary of how facilities receiving LIP funds met the intended goals of the program based on the specific LIP-related research questions within the evaluation domains of focus or “Domains” v-ix, listed in STC #80a, Section XVII Evaluation of the Demonstration of the CMS Special Terms and Conditions (Centers for Medicare & Medicaid Services, 2011). Domains v-ix read:

v. The effect of LIP funding on the number of uninsured and underinsured, and rate of uninsurance; vi. The effect of LIP funding on disparities in the provision of healthcare services, both

geographically and by population groups; vii. The impact of Tier-One and Tier-Two milestone initiatives on access to care and quality of care

(including safety, effectiveness, patient centeredness, timeliness, efficiency, and equity); viii. The impact of Tier-One and Tier-Two milestone initiatives on population health; and,

ix. The impact of Tier-One and Tier-Two milestone initiatives on per-capita costs (including Medicaid, uninsured, and underinsured populations) and the cost-effectiveness of care.

Low Income Pool Milestones The Centers for Medicare & Medicaid Services’ STCs for Florida’s Medicaid Reform Section 1115 Demonstration established LIP milestones that apply to the State and the Top 15 hospitals that are allocated the largest annual amounts in LIP funding (Centers for Medicare & Medicaid Services, 2011). The LIP funds available to the State at the beginning of each DY were reduced by any milestone penalties that were assessed by CMS. Two tiers of milestones, as described in STC #84 and STC #85 in the Data and Methods section of this report, must be met for the State and facilities to have access to 100% of the annual LIP funds. STC #84, also referred to in this document as “Tier-One Milestones” or “Tier-One,” is broadly defined as the development and implementation of a State initiative that requires Florida to allocate $50 million in total LIP funding in DY7: State Fiscal Year (SFY) 2012–2013, (referred to

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in this document as “DY7”) and DY8: SFY 2013–2014, (referred to in this document as “DY8”) to establish new, or enhance existing, innovative programs that meaningfully enhance the quality of care and the health of low income populations, the timely submission of provider reconciliations and demonstration deliverables, including the annual “LIP Milestone Statistics and Findings Report” and the “Primary Care and Alternative Delivery Systems Expenditure Report.” STC #85, also referred to in this document as “Tier-Two Milestones” or “Tier-Two,” requires that the Top 15 hospitals that are allotted the largest annual distributions of LIP funds (“Top 15”) participate in initiatives that broadly derive from the three overarching goals of CMS’ Three-Part Aim which is described in more detail in the Data and Methods section of this document under the heading STC #84: Tier-One Milestones. Please refer to the Data and Methods section of this document for a detailed description of the Tier-One and Tier-Two Milestones outlined in STC #84 and STC #85. Evaluation Domains v – ix relate to the LIP. The analyses provide a summary of how facilities receiving LIP funds met the intended goals of the program based on the specific LIP-related research questions within Domains v – ix, listed in STC #80a, Section XVII Evaluation of the Demonstration of the CMS Special Terms and Conditions (Centers for Medicare & Medicaid Services, 2011). Domains v – ix address the relationship between LIP funding and the provision of healthcare services to the uninsured, including the impact of Tier-One and Tier-Two Milestone initiatives. Tier-One and Tier-Two Milestone initiatives are approved, provider-designed programs intended to positively affect access to care, quality of care, population health, and per capita costs and the cost-effectiveness of care. Domain v Research Questions:

a. How has LIP funding improved access to care for uninsured/underinsured recipients? That is, how many uninsured and underinsured recipients receive services through LIP funding?

b. What types of services are being provided and in what settings? Domain vi Research Questions:

a. How does LIP funding impact access to and use of services by different population groups? b. Does it (LIP funding) increase access to services in particular areas? c. How many programs funded by LIP, including Tier-One and Tier-Two initiatives, are focused on

reducing disparities in the provision of healthcare services or health outcomes? d. What are these programs doing to reduce disparities and how successful are they?

Domain vii Research Questions:

a. What are the goals of the Tier-One Milestone programs? b. What interventions/activities are they using to enhance quality of care and the health of low-

income populations? Are they successful? c. What are the goals of the Tier-Two Milestone initiatives? d. How many of the initiatives are focused on access to care and quality of care? e. How are the Top 15 hospitals working to meet their goals? Are they successful?

Domain viii Research Questions:

a. How are the Tier-One Milestone initiatives proposing to affect population health? b. Are they targeting particular groups of recipients or health conditions? Are they successful in

achieving their objectives? c. How are the Tier-Two Milestone initiatives proposing to affect population health? d. Are they targeting particular groups of recipients or health conditions?

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e. What interventions/activities are they engaging in to impact population health? Are they successful?

Domain ix Research Questions:

a. How do expenditures for services funded through the Tier-One Milestone initiatives differ from other LIP expenditures?

b. How do the services provided under Tier-One Milestone initiatives differ from those provided under other LIP funding? That is, do Tier-One Milestone expenditures result in more preventive and outpatient care than emergency department and inpatient visits?

c. Do Tier-One Milestone initiatives result in lower expenditures for recipients who are served by them?

d. Do the Tier-Two Milestone initiatives impact expenditures for care for the uninsured/underinsured? How are expenditures affected?

e. What initiatives are successful in helping recipients to access the appropriate level of care and prevent the need for emergency or inpatient care?

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Data and Methods

Domains i and ii A variety of data sources were used to address the research questions associated with Domains i and ii. These data sources included multiple rounds of the Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys; claims, eligibility data, and encounter data; Health Care Effectiveness Data and Information Set (HEDIS) and Agency-defined performance measures data from 2008 to 2013; Performance Measure Action Plans (PMAPs); Performance Improvement Projects (PIPs); External Quality Review Organization (EQRO) Annual Florida Medicaid HEDIS Results Statewide Aggregate Reports; and EQRO External Quality Review Technical Reports. Bivariate and multivariate statistical analyses were applied to the CAHPS survey data and the claims, eligibility, and encounter data. For the HEDIS and Agency-defined performance measures, bar graphs and trend lines were created. Measures that are a part of the standard set provided by National Committee Quality Assurance (NCQA) organization were compared to the National Medicaid 50th Percentile. National HEDIS Medicaid percentiles are benchmarks from the prior year. Finally, qualitative analysis and document review were conducted of the PMAP and the Performance Improvement Project reports. The domains i and ii analyses provided in this report covers up to seven years of Medicaid Reform (indicated as DYs or State Fiscal Years SFYs*) including DY1 – DY7 of the CAHPS survey data, claims and eligibility data, AHCA quarterly and annual reports; 2007 – 2012 PMAPs, PIPs, and the EQRO Annual External Quality Review Technical Report, 2008 – 2013 HEDIS and Agency-defined annual means data; 2007 – 2012 plan benefit charts; and DY5 – DY7 of the encounter data. Each type of data is described below.

Consumer Assessment of Healthcare Providers and Systems Data (CAHPS) Beginning with a benchmark year, throughout the course of the demonstration, rounds of the CAHPS surveys have been fielded almost annually to eligible beneficiaries living in the demonstration counties. CAHPS represents a family of surveys designed by the Agency for Health Care Research and Quality (AHRQ) that ask consumers and patients to assess aspects of their care. The population for the survey was based on AHCA Member-Month and Recipient Eligibility files. Prior to each survey, Medicaid demonstration or demonstration-eligible recipients (in the case of the Benchmark Survey) who were enrolled in Medicaid for at least 6 consecutive months were randomly selected to participate in the surveys.

* Time periods in the report are reported as both DYs or SFYs. DY1 represents SFY0607, DY2 represents SFY0708, DY3 represents SFY0809, DY4 represents SFY0910, DY5 represents SFY1011, DY6 represents SFY1112, and DY7 represents SFY1213.

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To assess consumer experience with care, the CAHPS asks two kinds of questions: ratings of care and reports of care. Ratings of care ask patients to rank their care on a 0 to 10 scale along a number of dimensions, including overall rating of health care, overall rating of personal doctor, overall rating of specialty care, and overall health plan rating. For this analysis, ratings were divided into three groups representing a high score (9 – 10), a medium score (7 – 8), and a low score (6 and below), as available on the AHRQ website for health plan survey and guidance (Agency for Healthcare Research and Quality, 2011). Reports of care ask about specific healthcare experiences within the last 6 months, such as finding a personal doctor and getting care in a timely manner. Reports of care use a Likert scale response. Analyses of the CAHPS include descriptive statistics and chi-square tests for standard measures of the CAHPS survey comparing plans, plan types, benefit levels, and demonstration setting (rural or urban). The test of significance assesses whether there are statistically significant differences between the groups under comparison. It does not provide an indication of exactly where the differences occur. Regression models with year as the independent variable were used to assess whether trends observed for the CAHPS ratings are statistically significant over time. P-values less than 0.05 indicate that there is a strong likelihood that observed differences in distributions are in fact real and not due to chance. These samples are large, so it sometimes occurs that small differences can be statistically significant. Similarly, in some of the results for the rural areas, large differences may not be statistically significant. The practical and substantive importance of differences must be distinguished from their statistical significance. Table 1 provides details of the Survey Follow-Up Year and the corresponding Demonstration Year (DY). Seven rounds of the survey (DY1 [Benchmark Year], DY2, DY3, DY4, DY6, DY7, and DY8) were conducted in Broward and Duval (urban) counties. Six rounds (DY2, DY3, DY4, DY6, DY7, and DY8) were conducted in Baker, Clay, and Nassau (rural) counties. Survey fieldwork was not conducted in DY5. Starting in DY6, fielding of the CAHPS survey in the demonstration counties was combined with fielding of the survey in the non-demonstration counties using the same method; hence, DY6 is the first year that comparisons between demonstration and non-demonstration counties can be made. Additional details about the CAHPS survey methodology can be found in Appendix A. Table 1: CAHPS Field Dates for Demonstration Years

CAHPS Data Year (Current Label)

CAHPS Field Dates

Survey Follow-Up

Year

Month and Year Survey Fielded

6 months prior

Demonstration Year (DY)

Benchmark Year (Fall 06) 7/27/06 – 11/5/06 Benchmark July–November 2006 January 2006 DY1 (B)

Year 1 (Winter 07/08) 1/10/08 – 3/28/08 1 January–March 2008 July 2007 DY2

Year 2 (Spring 09) 3/13/09 – 6/28/09 2 March–June 2009 September 2008

DY3

Year 3 (Spring 10) 5/12/10 – 7/22/10 3 May–July 2010 November 2009

DY4

Year 4 (10/1/11–6/30/12) 10/1/11 – 6/30/12 4 October 2011–June 2012 April 2011 DY6

Year 5 (7/1/12–6/30/13) 7/1/12 –6/30/13 5 July 2012–June 2013 January 2012 DY7

Year 6 (7/1/13 –6/30/14) 7/1/13 – 6/30/14 6 July 2013–June 2014 January 2013 DY8

Note. Demonstration Year based on period referred for survey (6 months prior).

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HEDIS & Agency-defined Performance Measures Data, PMAPs, PIPs, EQRO Annual Florida Medicaid HEDIS Results Statewide Aggregate Reports, EQRO Annual External Quality Review Technical Reports The data used to conduct these analyses were taken from reports containing selected HEDIS measure calculations for all health plans in Reform and Non-Reform counties reported annually for the period 2008 to 2013. HEDIS measures are used as tool to compare plan performance on dimensions of care and service. A description of each HEDIS measure is provided in Appendix B. The selection of measures used for these analyses was based on two criteria. First, only HEDIS measures for which at least four years of data are available were included to have enough data to complete a trend analysis. Second, scores were chosen to provide an accurate assessment of the changes in appropriate utilization of services and quality of care during the demonstration. HEDIS measures were grouped into two categories based on the purpose of the measure. The two HEDIS measure categories are:

Preventive and wellness: Preventive and wellness HEDIS measures consist of metrics used to assess appropriate utilization of services in Domain i.a. They provide information on care in terms of annual checkups, preventive screenings, and recommended wellness visits for children and adult patients. The measures include Annual Dental Visits – Total (ADV), Adolescent Well Care (AWC), Adults’ Access to Preventive/Ambulatory Health Services – Ages 20–44 (AAP20), Adults’ Access to Preventive/Ambulatory Health Services – Ages 45–64 (AAP45), Adults’ Access to Preventive/Ambulatory Health Services – Ages 65+ (AAP65), Adults’ Access to Preventive/Ambulatory Health Services Total (AAP Total), Cervical Cancer Screening (CCS), Prenatal Care (PPC-Pre), Postpartum Care (PPC-Post), Well-Child 0 Visits (W15 0), Well-Child 6+ Visits (W15 6), and Well-Child Visits in the Third, Fourth, Fifth, & Sixth Years of Life: (W34).

Chronic Disease Management: Chronic disease management measures were used to assess quality of care in Domain i.b. They provide information on care in terms of testing and monitoring patients with diabetes, hypertension, and mental illness. The measures include Antidepressant Medication Management – Effective Acute Phase Treatment (AMM Acute), Antidepressant Medication Management – Effective Continuation Phase Treatment (AMM Continuation), Comprehensive Diabetes – HbA1C Testing (CDC HbA1C Testing), Comprehensive Diabetes – HbA1C Poor Control (INVERSE)(CDC Poor), Comprehensive Diabetes – Good Control (CDC Good), Comprehensive Diabetes – Eye Exam (CDC Eye), Comprehensive Diabetes – LDL-C Control (CDC LDLC), Comprehensive Diabetes – LDL Screening (CDC LDLS), Comprehensive Diabetes – Nephropathy (CDC Neph), Controlling Blood Pressure – Total (CBP), Follow-Up after Hospitalization for Mental Illness – 7 Day (FMH7), and Follow-Up after Hospitalization for Mental Illness – 30 Day (FMH 30).

Weighted mean averages for each individual HEDIS measure were calculated and aggregated into one separate score for Reform plans, Non-Reform plans, and all plans. The figure was derived by dividing the sum product of each health plan’s number of eligible enrollees and their HEDIS score, by the total number of eligible enrollees for the category. These calculations were completed using Microsoft Excel.

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Two types of analysis were performed:

Trend Analysis of Reform Plans: HEDIS measure scores for Reform plans were used to evaluate changes in quality of care. This was accomplished by using the Excel regression trend function, which calculates the slope of the trendline. The slope is the average annual change or trend in the HEDIS score for the period 2008 – 2013. A positive value indicates an improving trend and a negative value a declining trend. The reverse is true for inverse measures. Since the unit of measure for a HEDIS score is percent, the average annual change represents the change in percentage points from one year to the next.

Comparative Analysis of Reform and Non-Reform Plans: HEDIS measure scores were used to compare quality of care of Reform plans to Non-Reform plans. Two separate comparisons were completed.

o Comparative Trend Analysis: This analysis compares Reform plan trends to Non-Reform

plan trends. The average annual change in HEDIS score for Non-Reform plans was calculated using the same method described above for Reform plans. Then the difference in average annual change was calculated by subtracting the Non-Reform value from the Reform value. A positive value means Reform plans have a better trend than Non-Reform plans; however, there are two potential perspectives: Reform plans improved at a greater rate than Non-Reform plans, or Reform plans declined in performance at a lower rate than Non-Reform plans. A negative value means Reform plans have a downward trend compared to Non-Reform plans; however, there are two potential explanations: Reform plans improved at a slower rate than Non-Reform plans, or Reform plans declined in performance at a greater rate than Non-Reform plans. These scenarios are reversed for inverse measures where a declining trend is preferable.

o Comparative Difference Analysis: This analysis compares Reform plan scores to Non-Reform plan scores. The average annual difference in HEDIS score was derived by taking the average of the difference between the Reform HEDIS score and the Non-Reform HEDIS score for each year during the demonstration. A positive value indicates the Reform plans on average performed better than Non-Reform plans and vice versa. The reverse is true for inverse measures where a declining trend is preferable.

Performance Measure Action Plans and Performance Improvement Projects Health plans are required to submit PMAPs and PIPs to AHCA annually. PMAPs are used by health plans to manage the process of improving HEDIS measures that are below certain thresholds. PIP documents include descriptions and overall analyses of plan performance improvement projects (improvement strategies and data analyses) to improve HEDIS/Agency-defined and other measures. The annual PMAP and PIP reports for the period 2008 – 2012 were used to conduct these analyses. To understand the actions taken to improve measures, a qualitative case study analysis was performed on the strategies implemented for performance improvement of different measures throughout three health plans. The selection of health plans for this analysis was based on the size of market share and number of enrollees. Those with the largest market share and with a greater number of enrollees were selected for comparison. These were Sunshine (Health Maintenance Organization [HMO], 28.06% of

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market share), First Coast Advantage (Provider Service Network [PSN], 21.7% market share), and South Florida Community Care Network (SFCCN) (PSN, 12.02% market share). After selecting the plans for the case study, their PMAPs were reviewed and the measures where they had reported a deficiency three years prior (2010, 2011, and 2012) were selected for comparison. These measures included both chronic disease management and preventive wellness. After reviewing the measures with deficiencies in the three health plans, eleven measures that affected a wide population were selected for further analysis. For these eleven measures, the plans were placed side by side and their PMAPs and activities were compared and summarized. They were grouped by plan and HEDIS measure, and their average annual change was calculated. The three category groups created were “All three plans had positive average annual change in HEDIS Score,” “All three plans had negative average annual change in HEDIS Score,” or “Mixed results.” During the analysis it was found that PMAPs for some measures were not available in the reports. These measures were excluded from the analysis, focusing instead on comparing those that had been provided by at least two of the three health plans. The final measures were AAP20, AAP Total, CBP, and CDC-Eye. For these measures, the actions reported on the PMAPs were coded based on previous qualitative analysis of the HEDIS data. The results were placed in category tables to identify if differences in actions emerged between those measures where all three plans had a positive annual change compared with those which had a negative or mixed result.

Claims, Eligibility, and Encounter Data Analysis Caveats

Expenditure Analysis Caveats

Before presenting results of the fiscal analyses, several issues with the Medicaid eligibility and claims data need to be acknowledged. The analyses presented here used newly extracted Medicaid claims and eligibility data. Although the same inclusion and exclusion criteria were used for the current analyses, the data extracted for this current analysis did not have a one-to-one match with the previous data for those years that overlapped between the previous and current data extractions (SFY0607 – SFY0910) (Duncan, Hall, Harman, et al., 2011; Harman & Duncan, 2009; Harman, Hall, Lemak, & Duncan, 2013; Harman, Lemak, Al-Amin, Hall, & Paul Duncan, 2011). The reasons for differences in these data extractions could not be determined. Apart from the issue of the non-matching data extractions, several other concerns with the data are noted. In order to focus the analyses exclusively on individuals who were required to enroll in the managed care Reform program, enrollees who were not mandatory for the demonstration (e.g., those in waiver programs) were excluded. Despite that exclusion step, some of the observations included individuals who were coded as being in the MediPass program. Analysis from previous reports for SFY1011 (DY5) and SFY1112 (DY6) included these individuals because we had determined that program code was a more reliable indicator of Reform status than plan type, given other issues with the data described below. However, based on AHCA’s specific request, individuals in the Reform counties that had a plan type code of MediPass are not included in this analysis for the entire demonstration period (SFY0708 – SFY1213). Therefore the assumption for these analyses is that the plan type code is more reliable than the program code.

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Additionally, while there were no PSNs operating in the Control counties (Hillsborough and Orange), the data files contain individuals identified as being residents of those counties and enrolled in a PSN—a circumstance that should be impossible. These observations were removed from the analyses. Also, in the previous data extractions, enrollees with zero expenditures in any particular month were relatively rare. For example, there were zero expenditures during a month for 5 – 7% of all person-month observations, but in the current data extraction, approximately 25% of person-month observations had zero expenditures. The reason for these discrepancies could not be determined. Finally, it was not possible to obtain data from AHCA for the two fiscal years prior to the demonstration (SFY0405, SFY0506) or the first year of the demonstration (SFY0607). Because baseline data are no longer available, a different approach to the analyses that has limited causal inference compared to the previously used difference-in-difference approach was necessary.

Utilization Analysis Caveats

All caveats outlined above for the expenditure analysis apply for the utilization analyses as well. Further, the utilization analyses only use data from SFY1011 – SFY1213. This is because to calculate utilization, encounter data for enrollees in HMO plans had to be merged with claims data for enrollees in PSNs and MediPass. The encounter data only covered the period SFY1011 – SFY1213. Therefore, even though claims and capitation payment data that were used in the expenditure analysis covered SFY0708 – SFY1213, the utilization analyses were limited to SFY1011 – SFY1213 as both encounter and claims data are needed to calculate accurate health service utilization. Furthermore, as with the expenditure analysis, enrollees in the Reform counties with a plan type of MediPass were removed from the study sample. Because of these various issues, the results of the Domains i and ii expenditure and utilization data analyses should be interpreted with caution.

Utilization Analyses Univariate Analyses

To calculate Reform health service utilization for Medicaid recipients enrolled in PSNs, all inpatient, outpatient, medical, and pharmacy claims were obtained for enrollees who lived at least one month in Broward or Duval county from SFY1011 through SFY1213. Since HMOs operate under a capitated payment system, inpatient, outpatient, medical, and pharmacy encounters were used to obtain utilization counts. Emergency room visits were obtained from hospital discharge data regardless of plan type.

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For the fiscal and utilization analysis, three eligibility categories referred to as Medicaid Eligibility Groups (MEGs) were established. MEG #1 includes individuals with eligibility based on Supplemental Social Security Income (SSI), MEG #2 includes Children and Families with eligibility through Temporary Assistance for Needy Families (TANF), and MEG #3 refers to the LIP program (Florida Agency for Health Care Administration, 2011).* The same selection criteria used in the fiscal analysis were used to calculate health service utilization. Further details of the inclusion and exclusion criteria utilized are available in Appendix C. This resulted in a final Reform sample of 8,837,302 member months for SFY1011 – SFY1213. The same selection criteria for enrollees and services used for the calculation of health service utilization in the demonstration counties (Broward and Duval) (e.g., MEG #1 or MEG #2, no waiver services, no CMS, etc.) were used to calculate utilization for enrollees in the Control counties (Hillsborough and Orange). This resulted in a final control sample of 11,118,063 member months for SFY1011 – SFY1213. A detailed description of the sample selection and exclusion criteria is provided in Appendix C. The analysis used a person-month approach, meaning each observation corresponds to health service utilization by a person in a month (or member months). Average per-member, per-month (PMPM) utilization was calculated by fiscal year (SFY1011 through SFY1213) and MEG category. Differences in average PMPM utilization were calculated by fiscal year for MEG #1 and MEG #2 between Reform and Non-Reform counties. The analysis was based on data for all Medicaid enrollees who met the inclusion and exclusion criteria as described in Appendix C. Hence, the subjects included here represent a complete database of the eligible enrollees germane to this analytic question, as distinct from a random sample. Multivariate Analyses

A series of multivariate analyses were conducted to better understand the pattern of changes in utilization of emergency room, inpatient, outpatient, medical, and pharmacy health services, as well as to control for any differences in age, race, or gender between the Control and Reform counties. As with the univariate analyses, the multivariate analyses examined trends in PMPM utilization over time and whether these trends significantly differed between the Reform and Control counties. Because health services utilization was calculated on a PMPM basis, this analysis used a person-month observation (one observation per person per month). Thus, an individual could provide up to thirty-six observations to the analyses. The models were estimated using generalized estimating equations (GEEs) assuming a negative binomial distribution with a log link to account for the correlation of observations

* MEG #3 does not include any specific eligibility group as it refers to the payments distributed through the Low-Income Pool program. For detailed analysis of the Low-Income Pool program, see Duncan, R. P., Hall, A. G., Harman, J. S., Bell, L. L., & Kinsell, H. K. (2013). Low Income Pool milestone statistics and findings report for DY6: SFY 2011 – 12. Gainesville, FL: University of Florida, Department of Health Services Research, Management and Policy.

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over time. Moreover, the measures of utilization are counts of encounters/visits. Most individuals had zero or one encounter during a month, but some people had a large number of encounters/visits, thus the models used represented this type of data appropriately. First, the impact of Reform was assessed by including an indicator for whether or not the observation was from an individual in one of the Reform counties during the utilization analysis study period (SFY1011 – SFY1213 in Broward and Duval counties). This showed the shift in the intercept associated with Reform (i.e., the average difference in the mean amount of utilization between the Reform and Control counties during the study period). Additionally, the analyses included a variable for month that modeled the overall time trend over the study period. Finally, an interaction of Reform and month was included to assess whether the trend in utilization of health services significantly differed between the Reform and Control counties. Furthermore, to obtain an estimate of the likely difference in health service utilization due to Reform, average PMPM health services utilized were predicted assuming all enrollees were in Control counties using the multivariate models, and then average PMPM health services utilized were calculated again to determine what PMPM health services utilized would have been if the trend in utilization had instead followed the trend observed in the Reform counties. These equations (and all other equations) were estimated separately for enrollees in MEG #1 (SSI) and MEG #2 (TANF) and for each claim/encounter type (emergency room, inpatient, outpatient, medical, and pharmacy health services). See Appendix F for more detailed information.

Fiscal Analyses Univariate Analyses

To calculate Reform expenditures, all facility, medical, and pharmacy claims or analogous HMO capitation payment amounts were obtained for all Medicaid enrollees who lived at least one month in Broward or Duval county during SFY0708 through SFY1213. According to the special terms and conditions of the waiver, three eligibility categories referred to as MEGs were established. MEG #1 includes individuals with eligibility based on SSI, MEG #2 includes Children and Families with eligibility through TANF, and MEG #3 refers to the LIP program (Florida Agency for Health Care Administration, 2011).* Additional details of the inclusion and exclusion criteria utilized are available in Appendix C. Individuals categorized as either MEG #1 or MEG #2 were considered mandatory participants in the demonstration pilot. Certain individuals, including dually eligible individuals (participating in both Medicaid and Medicare) and pregnant women above the MEG #2 eligibility level, could voluntarily

* MEG #3 does not include any specific eligibility group as it refers to the payments distributed through the Low-Income Pool program. For a detailed analysis of the Low-Income Pool program, see Duncan, R. P., Hall, A. G., Harman, J. S., Bell, L. L., & Kinsell, H. K. (2013). Low Income Pool milestone statistics and findings report for DY6: SFY 2011 – 12. Gainesville, FL: University of Florida, Department of Health Services Research, Management and Policy.

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participate. To ensure genuine comparability, those enrollee months where individuals were voluntarily eligible for the waiver and/or special services (e.g., AIDS waiver, Statewide Inpatient Psychiatric Program [SIPP] services, etc.) or included retroactive eligibility were not included in the calculations. In addition, children who received services through CMS were excluded from the calculations. Because many individuals moved in and out of Duval and Broward counties and/or changed eligibility during this time, only those months where the individual lived in one of the pilot counties and was in a Reform eligible category were used to calculate expenditures. This resulted in a final Reform sample of 14,615,650 member months for SFY0708 – SFY1213. The same selection criteria for enrollees and services used for the calculation of expenditures in the demonstration counties (Broward and Duval) (e.g., MEG #1 or MEG #2, no waiver services, no CMS, etc.) were used to calculate expenditures for enrollees in the Control counties (Hillsborough and Orange). This resulted in a final control sample of 19,176,028 member months for SFY0708 – SFY1213. A detailed description of the sample selection/exclusion criteria is provided in Appendix C. The analysis used a person-month approach, meaning each observation corresponds to expenditures by a person in a month (or member months). Average PMPM expenditures were calculated by fiscal year (SFY0708 through SFY1213) and MEG category. Differences in average PMPM expenditures were calculated by fiscal year for MEG #1 and MEG #2 between Reform and Non-Reform counties. The analysis was based on data for all Medicaid enrollees who met the inclusion and exclusion criteria as described in Appendix C. Hence, the subjects included here represent a complete database of the eligible enrollees germane to this analytic question, as distinct from a random sample. Multivariate Analyses

A series of multivariate analyses were conducted to better understand the pattern of changes in expenditures, as well as to control for any differences in age, race, or gender between the Control and Reform counties. As with the univariate analyses, the multivariate analyses examined trends in PMPM expenditures over time and whether these trends significantly differed between the Reform and Control counties. These models were estimated using GEEs that account for the correlation of observations over time. Because expenditures were calculated on a PMPM basis, this analysis used a person-month observation (one observation per person per month). Thus, an individual could provide up to seventy-two observations to the analyses. Additionally, because approximately 25% of person-months had zero expenditures, a two-part model, as used in the Rand Health Insurance Experiment, was used to obtain unbiased estimates. If an ordinary least squares regression is used with data that has clustering at zero expenditures, the estimated impact of Reform would be biased towards zero. A Rand Two-Part model produces unbiased estimates by first examining the impact of Reform on the probability of having any expenditures using a GEE logistic regression model and then by estimating the impact of Reform on total expenditures on only those observations with expenditures that were greater than zero using a GEE gamma regression, which accounts for the skewed distribution of the expenditures data. First, the impact of Reform was assessed by including an indicator for whether or not the observation was from an individual in one of the Reform counties during the study period (SFY0708 – SFY1213 in Broward and Duval counties). This showed the average difference associated with the Reform counties during the Reform period in either the probability of having any PMPM expenditures or total expenditures given that there were non-zero expenditures. Moreover, the analyses included a variable for month that modeled the overall time trend over the study period. Finally, an interaction of Reform

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and month was included to assess whether the trend in the probability of any expenditures or total expenditures significantly differed between the Reform and Control counties. Because expenditure data were highly skewed, and thus not normally distributed, a gamma distribution was assumed for the equation examining total expenditures. Additionally, to obtain an estimate of the likely difference in expenditures due to Reform, average PMPM expenditures were predicted assuming all enrollees were in Control counties using the multivariate models, and then average PMPM expenditures were calculated again to determine what PMPM expenditures would have been if the trend in expenditures had instead followed the trend observed in the Reform counties. These equations (and all other equations) were estimated separately for enrollees in MEG #1 (SSI) and MEG #2 (TANF). See Appendix F for more detailed information.

Plan Benefits Data Plans were able to customize benefit packages by varying specific services for non-pregnant adults, varying cost sharing, and providing expanded benefits. The benefit charts were used to examine differences in benefits. The charts include a list of standard benefits, co-pays, plan limits, and extra services. The charts were used to draw comparisons of plan benefit designs in 2007, 2011, 2012, and 2013. The specific plan benefit charts available for the period which this report covers include October 2007, April 2011, April 2012, and September 2013. The year 2007 was the first year in which plans were operational in all Reform counties. In 2013, there were fifteen plans that were operational in the demonstration counties. However, only five of these plans were operational throughout the 2007, 2011, 2012, and 2013 period being analyzed. To obtain a true comparison, a subanalysis was done of only plans that were operational in this period. Plans operational throughout include Children’s Medical Services, First Coast Advantage, Humana, South Florida Community Care Network, and United Healthcare.

Domain iii

Analysis Caveats With one exception (the exclusion of MediPass enrollees), these analyses are constrained by many of the same issues described above with reference to the fiscal and utilization studies. In addition, these analyses measure change in the observed rates of using EBA credit-earning services during the Medicaid Reform program. Comparisons were made between utilization rates of Reform enrollees and Non-Reform Florida Medicaid enrollees using similar services. Two types of preventive service claims using hospital discharge data were compared: those resulting from the use of medical facilities and those from outpatient settings (DY2 – DY7). Additionally, utilization rates were calculated using encounter data, comparing Reform enrollees and Non-Reform Medicaid enrollees, as well as Reform enrollees by EBA earning levels (DY5 – DY7). Results are available only for DY5 – DY7 because to calculate utilization, encounter data for enrollees in HMO plans have to be merged with claims data for Enrollees in PSNs and MediPass. The encounter data utilization analyses are for DY5 – DY7 (SFY1011 – SFY1213) since those are the only years pertaining to this report for which validated encounter data are available. So, even though claims, eligibility, and hospital discharge data that were used in the hospital discharge data utilization analysis covered DY2 – DY7, the encounter data utilization analysis was limited

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to DY5 through DY7 as both encounter and claims data are needed to calculate accurate health service utilization. Because of these various issues, the results of the EBA analyses should be interpreted with caution.

Analytic Methodology The basic analytic strategy of the Enhanced Benefits Account (EBA) analyses was a comparison over time between two urban Medicaid Reform counties (Broward and Duval) and two urban Non-Reform counties (Orange and Hillsborough) representing the Non-Reform Medicaid population in Florida. For this sub-study, the data came from five primary sources: (1) the Enhanced Benefits Information System (EBIS), (2) Medicaid claims and eligibility data, (3) hospital discharge data, (4) encounter data, and (5) AHCA quarterly and annual reports. The core analytic dataset combined EBIS, claims, and eligibility data. General descriptive statistics regarding active participation rates (comparison of dollar amounts of credits earned and credits spent) were assessed using EBIS data. Bivariate and multivariate analyses of program use that control for age, gender, eligibility category, race/ethnicity, length of time in Medicaid, plan type, and residence in Reform vs. Non-Reform counties were conducted. Additional data analyses, using (a) claims and eligibility data, and (b) encounter data, were used to compare the likelihood of certain preventive behaviors between enrollees in Reform and Non-Reform counties. The preventive behaviors of primary interest were those used to earn EBA credits (e.g., office visits, adult/child preventive care visits, dental preventive services, vision exams, pap smears, mammograms, colorectal screenings, and Prostate Specific Antigen [PSA] tests). Claims, eligibility, and EBIS data were also used to compare demographic and health status characteristics of high, medium, and low credit earners, as well as individuals who did not earn credits. A “high” earner was defined as someone who earned $125 in rewards per year (maximum amount allowed by the program). A “medium” earner was categorized as someone who earned $51 – $124 in rewards per year. A “low” earner was defined as someone who earned $1 – $50 per year. The fourth category was “non-earners,” defined as enrollees who were eligible to participate in the EBA program but did not earn credits for a particular year. The EBA analyses included in this report cover up to six years of Medicaid Reform including DY1 – DY7 of claims and eligibility data and AHCA quarterly and annual reports; DY2 – DY7 of EBIS data and hospital discharge data; and DY5 – DY7 of the encounter data.

Domain iv The evaluation utilized a mixed-methods approach (Driscoll, Appiah-Yeboah, Salib and Rupert, 2007; Lieber, 2009), that is, the design and analyses include both qualitative and quantitative methods. The qualitative study included: 1) content analysis of the fraud and abuse plans submitted to the Bureau of Medicaid Program Integrity (MPI) by managed care plans and 2) analyses and integration of supplemental interview data. The quantitative analyses utilized secondary data obtained through the 1) Annual Anti-Fraud and Abuse Activity Report (AFAAR) and 2) the Quarterly Anti-Fraud and Abuse Activity Report (QFAAR) that are required from each managed care plan by the Bureau of MPI. A full copy of this report is provided in Appendix J.

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Qualitative Analyses The qualitative study focused on obtaining a comprehensive comparative analysis of the anti-fraud, waste and abuse plan policies and procedures for Medicaid managed care plans. Specific perceptions of efficiency and effectiveness as well as insights into the value of specific strategies were obtained through semi-structured interviews. Content Analysis

Content analysis is a set of procedures for transforming non-structured information into a format that allows analysis (U.S. General Accounting Office, 1996). The intent of this method was to summarize and list the major themes or categories contained within the written anti-fraud plans in a manner that allowed comparison of fraud and abuse measures across the managed care plans. Anti-fraud policies from 24 Medicaid managed care plans were provided to the research team via a secure password-protected SharePoint portal in Year 1. In Year 1, the research team conducted a preliminary qualitative comparative content analysis of four managed care plans and provided a Year 1 Preliminary Content Analysis Report to the Agency in June 2013. In Year 2, five additional managed care plans were chosen for review. The research team conducted qualitative comparative content analysis and provided a Year 2 Preliminary Content Analysis report to the Agency in April 2014. The anti-fraud plans from the selected managed care plans for each year were uploaded into Dedoose, a secured qualitative analysis software program for content analysis (Dedoose, 2013). The anti-fraud policy documents were then separately coded and analyzed in Dedoose by members of the evaluation team following accepted and recognized qualitative methods (King, 1998; Stemler, 2001; U.S. General Accounting Office, 1996). The reliance on coding and categorizing of data makes content analysis a particularly rich and meaningful tool to find critical information (Stemler, 2001). These methods involve a number of steps and a robust iterative process to ensure reliability and validity of the data analysis. Coding is the process through which text can be categorized. First, a preliminary coding template is established and the coding is applied to the data. The coding template is then modified multiple times through several iterations. Revisions are made as necessary, and the categories are tightened to maximize mutual exclusivity and exhaustiveness (Weber, 1990). Codes are eventually grouped into themes or categories, and the data are summarized in tables that facilitate the summary of findings into a narrative form. The researchers created a preliminary coding template after an initial read of the documents. The initial coding template developed through this process in Year 1 also served as the initial guide for the qualitative analysis of the fraud and abuse plans reviewed in Year 2. All steps following this initial review were repeated in both years. Fraud and abuse policies from each of the selected managed care plans were independently coded line-by-line by at least three members of the evaluation team. The three members assigned to independently code each plan were randomly selected so that the mix of independent coders varied across the plans. Reconciliation of the independent coding across all team members is performed to assure reliability, i.e., to assure that individual team members coded the same text in the same way (Weber, 1990). All four evaluation team members met to systematically review the coding and discuss any areas of disagreement, stopping when agreement was reached on all codes. All documents were then re-coded based on the revised coding template.

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The four members of the evaluation team iteratively reviewed and reconciled coding numerous times as a reliability and quality control check (Stemler, 2001). Agreement of codes was facilitated by analysis tools available within the Dedoose software program that allow compilation and comparison of the findings by independent coders. Documents and coding were then reviewed again to isolate broad themes or categories to capture the best representation of the data. The coding excerpts were then compiled within Dedoose and downloaded to Excel. Finally, a comparative table was developed by managed care plan (columns) and major themes (rows) to provide ease in examining similarities and differences in the anti-fraud and abuse plans. This comparison table was the foundation for the qualitative findings reported below. To assure confidentiality, the plans reviewed were not identified and all reference to managed care plan names in the excerpts was removed. The plans were denoted by letters (A-I) only. Supplemental Semi-Structured Interviews

To obtain more specific perceptions and details regarding topics incorporated by managed care plan policies and procedures, a semi-structured interview protocol was developed for telephone interviews of Compliance Officers. The questions largely focused on measures requested by CMS and the Agency in conjunction with the demonstration waiver evaluation request, e.g., frequency and types of interactions with providers, documentation and tracking of efforts to deter fraud and abuse, and methods to measure effectiveness of such efforts. The respondents for October 2013 pilot interviews were selected from the list of Compliance Officers of Medicaid managed care plans provided by AHCA. Respondents were scheduled for phone interviews that were recorded and then responses were transcribed to assist in analyses and synthesis of all of the interviews. Respondents to the pilot interviews suggested that the interview questions be provided in advance so that the respondents could prepare in advance and assure accuracy of responses. This suggestion was incorporated into the protocol for future interviews and respondents were provided an option to send written responses to the interview questions in order to minimize the time required to complete the interview. Two additional questions were also suggested and added to the interview: 1) use of commercially available anti-fraud software and 2) use of organizationally developed software or analytical tools. For respondents providing written responses, the phone time was minimized as the interviewer sought clarification of responses rather than obtaining all the information on the phone. A request for interviews was emailed to Compliance Officers and seven additional interviews were conducted in January and February of 2014. The total number of semi-structured interviews was 11, including the four selected for the pilot. The evaluation team listened to the recorded interviews, reviewed the written responses, and transcribed the responses into a table to allow synthesis of results. These results were then incorporated into the content analysis themes, where appropriate, to provide more comprehensive information. Thus, the narrative of the integrated comparative qualitative analysis presented in the findings below includes both a summary of the themes/categories identified in the content analysis, describes key differentiating factors, and provides illustrative examples as well as the more detailed information from the semi-structured interviews.

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Quantitative Analyses The quantitative analyses utilized data from the annual and quarterly reports submitted to the Bureau of MPI. The aim was to examine trends in overpayments and recoveries, fraud and abuse as well as the utilization of specific detection tools for suspected fraud and abuse incidents reported to the Bureau of MPI by the sample of managed care plans. The qualitative analysis did not identify any significant differences in fraud and abuse policies and procedures across the plans, with the exception of potential differences based on sophistication of analytic software that coincides with the size of the plans. Due to the resources required to develop and obtain more sophisticated software as well as indications in the qualitative analysis, the use of such tools appears to be more prevalent in medium and large sized managed care plans. Therefore, the sample of nine managed care plans included in the qualitative analysis was divided into three categories, small, medium and large, according to their total Medicaid enrollment in Florida provided by the Agency (AHCA, n.d., b) for the analyses. The small category included plans with Medicaid managed care enrollment under 10,000 members; the medium category included plans with enrollment greater than 10,000 but less than 100,000; and the large category included plans with enrollment greater than 100,000. Annual Fraud and Abuse Reports (AFAAR)

The AFAAR report must include, at a minimum: the dollar amount of health plan losses and recoveries attributable to overpayment, abuse and fraud; and the number of health plan referrals to the Bureau of MPI (2012-2015 Health Plan Model Contract). The AFAAR includes plan-specific data on various measures including total overpayments identified for recovery, total overpayments recovered, total dollars identified as lost to fraud and abuse, total dollars identified as lost to fraud and abuse that were recovered, and the total number of referrals. These data are also identified by Medicaid contract type, i.e., Reform versus Non-Reform. AHCA provided AFAAR data for three fiscal years, 2010 – 2011, 2011 – 2012, and 2012 – 2013, for quantitative analyses to explore potential trends and relationships to differential policies and procedures. Even though data collection efforts have improved over time, complete data are not available over the three fiscal years considered for all nine plans. The incomplete data is due to several reasons including: a) some plans participated in Medicaid only in some fiscal years but not in all three; and b) some plans did not operate under both Reform and Non-Reform contracts in each of the three fiscal years. As a result, plans A and I were excluded from the AFAAR plan-specific analyses due to the inability to track trends over time for these two plans. Using the available data, the researchers examined the trends over time as the Reform efforts were expanded. Quarterly Fraud and Abuse Reports (QFAAR)

The QFAAR reports include information on the specific detection tools utilized for each suspected fraud and abuse incident reported. AHCA provided multiple Excel files containing QFAAR data which spanned the third quarter of 2009 through the second quarter of 2013. Prior to performing analyses, all identifying information associated with the specific managed care plans was removed and relabeled by letters (consistent with the qualitative analyses) for the purpose of confidentiality. Comparative analysis was performed on the QFAAR data which includes Medicaid contract type (Reform versus Non-Reform) as well as detection tools for the eight quarters corresponding to SFY 2010 – 2011 and SFY 2012 – 2013.

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This subset yielded a total of 1,927 fraud and abuse cases. As explained above, two plans were excluded from the analysis due to insufficient data points. Analysis of the QFAAR data examined trends over time in terms of the primary detection tools reported utilized by the managed care plans in identifying the reported fraud and abuse cases. Detection tools were grouped into categories by studying the narrative elements of cases. Specifically, the researchers examined whether cases were identified using data analysis methods as the primary detection tool or not. These techniques include data mining, data analytics, data matching, pre- and post-payment reviews, sometimes with the use of specialized software, etc. The research team then compared the frequency of their use over time for both Reform and Non-Reform contract types. Percentage changes over time are reported rather than the specific number of incidents in each fiscal year in an effort to provide better comparability and protect confidentiality of the managed care plans.

Domains v – ix AHCA provided data on the facilities, awardees, approved initiatives, and reporting documents that were used in this document for reporting on the impact of LIP funding on access to care, the provision of healthcare services to underserved populations, population health, reductions in disparities, and the cost-effectiveness of care. The basic analytic approach used for these domains was an independent review and analysis of documentation related to the DY6 and DY7 LIP Milestone Statistics and Findings Report, the Tier-One initiatives funded under STC #84, the Tier-Two Milestone initiative proposals, and the Tier-Two Milestone initiative quarterly reports for DY7: SFY 2012–2013, (also referred to in this document as “DY7”). Each of the data sources is based on data for hospital and non-hospital providers that received LIP funding in DY6: SFY 2011–2012, (referred to in this document as “DY6”) or DY7. Thus, data for this report came from the following sources:

the Centers for Medicare & Medicaid Services Special Terms and Conditions (STCs) for Florida’s Medicaid Reform Section 1115 Demonstration, which defines and outlines the Low Income Pool Program and its elements, including the LIP Tier-One and Tier-Two Milestones;

the LIP Milestone Statistics and Findings Report for DY6: SFY 2011–12; the LIP Milestone Statistics and Findings Report for DY7: SFY 2012–13; the 2012–2013 Low Income Pool (LIP) Tier-One Milestone (STC #84) Approved Initiative

Proposals, also referred to in this document as “Applications”; the Primary Care and Alternative Delivery Systems Expenditures Report for DY7; the 2012–2013 Low Income Pool (LIP) Tier-Two Milestones (STC #85), Top 15 Approved Initiative

Proposals; and the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports.

The following narrative includes sections from STC #84 and STC #85 of the Florida Medicaid Reform Section 1115 Demonstration STCs.

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STC #84: Tier-One Milestones “Tier-One milestones are defined as:

a. Development and implementation of a State initiative that requires Florida to allocate $50 million in total LIP funding in DY7 and DY8 to establish new, or enhance existing, innovative programs that meaningfully enhance the quality of care and the health of low income populations. Initiatives must broadly drive from the three overarching goals of CMS’ Three-Part Aim.

i. Better care for individuals, including safety, effectiveness, patient centeredness, timeliness, efficiency, and equity;

ii. Better health for populations by addressing areas such as poor nutrition, physical inactivity, and substance abuse; and,

iii. Reducing per-capita costs.

Expenditures incurred under this program must be permissible LIP expenditures as defined under Section XIII, Low Income Pool. The State will utilize DY6 to develop the program. The program must be implemented with LIP funds allocated and expenditures incurred in DYs 7 and 8” (Centers for Medicare & Medicaid Services, 2011, pp. 22-23). Recipients of the LIP grant award for Tier-One Milestones were required to report qualitative and quantitative data relating to the various initiatives, which included the following:

The effect of LIP funding on disparities in the provision of healthcare services, both geographically and by population groups;

The impact on access to care and quality of care (including safety, effectiveness, patient centeredness, timeliness, efficiency, and equity);

The impact on population health; and The impact on per-capita costs (including Medicaid, uninsured, and underinsured populations)

and the cost-effectiveness of care.

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2012–2013 Low Income Pool (LIP) Tier-One Milestone (STC #84) Initiative: $35 Million Primary Care Award

The 2012 Florida legislature appropriated $35 million in LIP funds to support initiatives (also referred to in this document as “projects”) designed to start up new primary care programs or enhance existing programs (“$35 Million Primary Care Award”). The 2011-2012, $35 Million Primary Care Award consisted of a total of $20 million to be allocated in DY7 and DY8 for the start-up of new primary care programs, and a total of $15 million that was designated to be used to meaningfully enhance existing primary care programs. This appropriation was based on STC #84 found in the 1115 Medicaid Reform Waiver approved by CMS on December 15, 2011. Each new or enhanced project award, representing a combined total of state and federal matching dollars, was determined via a competitive solicitation. The solicitation was based on each applicant’s ability to provide Primary Care Access Programs as defined in the General Appropriations Act:

The CMS Tier-one Milestones are for the establishment of new, or enhancement of existing, innovative primary care programs that meaningfully enhance the quality of care and the health of low income populations. The new or enhanced primary care programs must broadly drive from the three overarching goals of CMS’s Three-Part Aim. Within these broad goals, the agency will establish further requirements for new or enhanced primary care programs to provide the services most needed by the local community, such as needed physician, dental, nurse practitioner or pharmaceutical services; expand local capacity to treat patients; and provide for extended service hours. Additionally, reduction of the unnecessary emergency room visits and preventable hospitalization will be components of new or enhanced primary care programs (2012–13 General Appropriations Act).

The $15 million for meaningfully enhanced existing primary care programs was an open competitive solicitation with a maximum of $4 million dollars awarded per project. The cap was determined based on a combination of the amount awarded through this bid and any amount received via the separate $34 million enhanced primary care funding awarded in SFY 2010–11. The initiatives were selected based on the program’s capability to achieve the following goals:

Reduce potentially avoidable emergency room visits by developing initiatives to identify persons inappropriately using hospital emergency rooms or other emergency care services and provide care coordination and referral to primary care providers.

Reduce potentially avoidable hospitalizations for ambulatory care sensitive conditions, which involve admissions that evidence suggests could have been avoided.

Expansion of primary care infrastructure to treat patients. Expansion of primary care through expanded service hours (e.g., evening or weekend hours). Provide the services most needed by the local community, such as the following:

o Additional physicians o Dental care o Nurse practitioners o Pharmaceutical services

Figure 1 illustrates the distribution of funds by provider type for new projects. The $20 million new project funding was distributed with a $4 million maximum per project, for a total of $8.7 million awarded to hospital projects, $4.3 million to Community Health Department (CHD) projects and $7.0

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million to Federally Qualified Health Centers (FQHCs) and other projects. For new hospital projects, the maximum amount distributed was $4 million and the minimum was approximately $144,700. The maximum distributed for a new FQHC or other provider project was approximately $3.7 million and the minimum was $660,900. Finally, for new CHD projects, the maximum amount distributed was around $3.4 million and the minimum was $406,000. Figure 1: Primary Care Award New Project Distribution

Note. CHD= County Health Department; FQHC=Federally Qualified Health Center

Figure 2 illustrates the distribution of funds by provider type for enhanced projects. The $15 million enhanced project funding was distributed with a $4 million maximum per project, for a total of approximately $7.7 million awarded to hospital projects, $2.2 million to CHD projects, and $5.1 million to FQHC and other projects. The project awards for the enhanced existing programs ranged from a maximum of approximately $3.3 million for a hospital to a minimum of $59,000 for a CHD program. Figure 2: Primary Care Award Enhanced Project Distribution

Note. CHD=County Health Department; FQHC=Federally Qualified Health Center

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STC #85: Tier-Two Milestones

Tier-Two Milestones applied to facilities that received the largest annual allocations of LIP funds and put at risk 3.5% of each of these facilities’ annual LIP allocation. The milestones applied to the 15 hospitals that were allocated the largest annual amounts in LIP funding (Tier-Two Top 15 Hospital). If the total annual LIP funds allocated for the 15 hospitals did not total at least $700 million, the population of hospitals must be expanded until $700 million is reached. Once a facility was identified as a Tier-Two Top 15 Hospital, it must continue to achieve milestones to receive future DY LIP funding regardless of whether it drops out of the Top 15 category. Exceptions to this requirement may be considered by CMS. Hospitals entering the Top 15 category in future DYs will be subject to timelines similar to program planning/success and execution timelines for the other Top 15 hospitals. Tier-Two Milestone initiatives were to be driven by the three overarching goals of the Three-Part Aim described in the STC #84 Tier-One Milestone section in this document. Tier-Two Milestone initiatives focused on infrastructure development, innovation and redesign, and population-focused improvements. Participating facilities were to implement new, or enhance existing, healthcare initiatives, investments, or activities with the goal of meaningfully improving the quality of care and the health of populations served (including low-income populations) and must meet established hospital specific targets, to receive 100% of allocated LIP funding. Hospitals were each required to select and participate in three initiatives. Depending on the breadth of healthcare activities undertaken by a facility, CMS considered exceptions to the requirement that three initiatives must be implemented. Hospital initiatives that could be implemented under the Tier-Two Milestone initiative, which are tied to the Three-Part Aim, included the following types of projects which were drawn from recent demonstration experiences:

a. Infrastructure Development—Investments in technology, tools, and human resources that will strengthen the organization’s ability to serve its population and continuously improve its services. Examples of such initiatives are:

i. Increase in Primary Care capacity including residency programs and externships; ii. Introduction of Telemedicine;

iii. Enhanced Interpretation Services and Culturally Competent Care; and iv. Enhanced Performance Improvement Capacity.

b. Innovation and Redesign—Investments in new and innovative models of care delivery that have

the potential to make significant, demonstrated improvements in patient experience, cost, and disease management. Examples of such initiatives are:

i. Expansion of Medical Homes; ii. Primary Care Redesign; and

iii. Redesign for Efficiencies (e.g., Program Integrity).

c. Population-Focused Improvement—Investments in enhancing care delivery for the 5 – 10 highest burden (morbidity, cost, prevalence, etc.) conditions/services present for the population in question. Examples of such initiatives are:

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i. Improved Diabetes Care Management and Outcomes; ii. Improved Chronic Care Management and Outcomes;

iii. Reduction of Readmissions; iv. Improved Quality (with attention to reliability and effectiveness, and targeted to

particular conditions or high-burden problems); v. Emergency Department Utilization and Diversion;

vi. Reductions in Elective Preterm Births; and vii. Pediatric intensive care unit (PICU) and neonatal intensive care unit (NICU) Quality and

Safety (e.g., pediatric catheter–associated blood stream infection rates)” (Centers for Medicare & Medicaid Services, 2011, pp. 23-24).

“Subsequent year LIP funds allocated to the Top 15 hospitals will be made available based upon the successful execution of the facility’s targeted healthcare initiatives. The State must assess a penalty of 3.5% of a facility’s annual LIP allocation for failing to meet Tier-Two Milestone or components of Tier-Two Milestones. Penalties, if applicable, will be determined by December 31st of each DY. LIP dollars that are not paid out as a result of Tier-Two Milestones not being met are surrendered by the facility and State” (Florida Agency for Health Care Administration, 2012a, p. 3). The Tier-Two facilities that received funds in DY6 were required to submit milestone plans and quarterly reports to meet the Tier-Two Milestone requirements. Hospitals were required to submit quarterly reports to AHCA no later than the middle of the month following the last date of the preceding quarter. The initial quarterly reports were due to AHCA on October 15, 2012. Milestone accomplishments were to be measured based on the quarterly reports through September 30 of each DY. At a minimum, the hospitals were required to include a cumulative listing of baseline targets and their expected results, compared to results from each of the previous quarters, achievements (as defined by metrics) based on the submitted timelines, and a cumulative listing of measures and calculated metrics for each quarter (Florida Agency for Health Care Administration, 2012a). The Tier-Two Top 15 Hospitals (also referred to as “Top 15” in this document) based on LIP funding received in DY6, as defined in the Tier-Two Milestones are found in Table 2 below.

Tier-Two Milestone “Top 15” Initiatives STC #85 “Top 15” Hospitals

LIP Milestone Statistics and Findings Report for DY6: SFY 2011 –2012 The Top 15 hospitals (that received the most funding from the LIP program as of November 15, 2011) were identified in DY6. The Total LIP payments they received in DY6 are provided in Table 2: Top 15 Hospitals.

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Table 2: Top 15 Hospitals Medicaid Number

Provider Name Total Provider LIP Payments in

DY6

County

100421 Jackson Memorial Hospital $384,039,787 Miami-Dade

100129 Broward Health – Broward General Medical Center $106,588,173 Broward

102521 Memorial Hospital West $37,657,288 Broward

100676 Shands At Jacksonville $37,303,367 Duval

100200 Memorial Regional Hospital $36,694,665 Broward

108219 Broward Health – Imperial Point Hospital $35,390,473 Broward

101842 Halifax Health $33,428,018 Volusia

100994 Tampa General Hospital $33,156,702 Hillsborough

102229 Memorial Hospital Pembroke $32,638,844 Broward

101761 Sarasota Memorial Hospital $23,090,450 Sarasota

101109 Lee Memorial Hospital $20,733,475 Lee

100218 Broward Health – North Broward Medical Center $19,485,864 Broward

103454 Memorial Hospital Miramar $19,198,271 Broward

120405 Broward Health – Coral Springs Medical Center $13,592,310 Broward

101044 Indian River Medical Center $10,703,387 Indian River

The Top 15 hospitals were required to submit milestone plans and quarterly reports for the approved initiatives to AHCA. Fourteen of the Top 15 hospitals that received the largest amount of LIP funding in DY6 submitted three milestone plans each, one for each of the three required initiatives to meet the DY6 Tier-Two Milestone as listed in STC #85. One facility, the fifteenth hospital, submitted two plans for initiatives and an exemption request to CMS, for a total of 44 initiatives implemented by the Top 15 hospitals.

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General Findings: Domain i

The basic analytic strategy of Domain i is a comparison of the changes in measures over time and between the five Reform counties and comparison counties that may include the 62 Non-Reform counties representing the entire Non-Reform Medicaid population in Florida. For this domain, data were analyzed from three primary sources: (1) the annual CAHPS survey; (2) the HEDIS data sources: HEDIS data & Agency-defined performance measures, PMAPs, PIPs, EQRO Reports (Statewide HEDIS Analysis Reports, and Annual External Quality Review Technical Reports); and (3) Medicaid claims, eligibility, and encounter data.

Domain i.a: Are services accessible to enrollees ? Have there been changes in the accessibility of services to enrollees over the course of the demonstration ? Has the demonstration resulted in more appropriate use of services by enrollees?

CAHPS Survey Data

Counties Over Time Table 3 provides percentages for Broward and Duval separate from the rural counties for four CAHPS survey measures in each of the seven survey years (statistical comparisons were calculated only for DY2 through DY8). While positive and negative changes were observed from year to year, the primary objective was to examine the trend over the entire six-year period. The coefficient shown in the table indicates the direction of the overall trend (upward or downward, depending on its sign), as well as the level of statistical significance. If the coefficient is not statistically significant, the change across time is not statistically significant, though it may have practical significance. In Broward and Duval counties, trends over time (DY2 – DY8) showed no statistically significant change in the proportion of enrollees responding “Always” to the ease of getting appointments with specialists or seeing a doctor for non-urgent care in the previous six months. There was a statistically significant (p<.05) increase in the proportion of enrollees responding “Always” to the ease of getting care, tests, or treatments they thought they needed and to having a personal doctor (Table 3). In rural counties, trends over time (DY2 – DY8) showed no statistically significant difference in any of the four survey measures discussed here. However, it may be of practical significance to note that 62.4% of enrollees reported it was always easy to get appointments with a specialist in DY8, compared to only 31.1% of enrollees in DY7 (Table 3). Similarly, 71.1% reported it was always easy to get care, tests, or treatment through their health plan in DY8 compared to only 53.6% of enrollees in DY7.

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Table 3: Domain i.a: CAHPS Survey Data, Counties Over Time, DY1 – DY8 Domain i.a: Are services accessible to enrollees? Have there been changes in the accessibility of services to enrollees

over the course of the demonstration? Has the demonstration resulted in more appropriate use of services by enrollees?

Questions DY1a DY2

b DY3 DY4 DY6 DY7 DY8 Coefficient Trend

In the last 6 months, how often was it easy to get appointments for you (your child) with specialists? [Always]

Broward/ Duval

N/A 45.9% 48.9% 45.6% 48.4% 49.9% 55.8% 0.0489 ↔c

Rural N/A 52.3% 45.6% 47.5% 42.2% 31.1% 62.4% -0.1612 ↔c

In the last 6 months, how often was it easy to get the care, tests, or treatment you thought you (your child) needed through your health plan? [Always]

Broward/ Duval*

N/A 54.0% 52.4% 49.0% 57.3% 60.2% 67.7% 0.1088 ↑*

Rural N/A 56.2% 55.0% 52.6% 47.0% 53.6% 71.1% 0.0361 ↔c

A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt. Do you (your child) have a personal doctor? [Yes]

Broward/ Duval*

79.2% 88.2% 87.4% 87.3% 90.1% 91.6% 91.0% 0.1683 ↑*

Rural N/A 89.8% 89.9% 89.0% 93.8% 91.5% 94.8% 0.1084 ↔c

In the last 6 months, not counting the times you went to an emergency room, how many times did you (your child) go to a doctor’s office or clinic to get health care for yourself (himself or herself)? [0 times]

d

Broward/ Duval

20.5% 19.5% 20.0% 22.3% 18.6% 19.0% 20.9% -0.0166 ↔c

Rural N/A 17.2% 20.4% 20.5% 13.3% 12.1% 18.5% -0.1227 ↔c

In the last 6 months, not counting the times you went to an emergency room, how many times did you (your child) go to a doctor’s office or clinic to get health care for yourself (himself or herself)? [1 – 3 times]

d

Broward/ Duval

60.5% 55.2% 54.1% 53.9% 54.9% 59.4% 60.1% 0.008 ↔c

Rural N/A 50.3% 49.8% 48.6% 51.6% 59.0% 48.0% 0.0754 ↔c

In the last 6 months, not counting the times you went to an emergency room, how many times did you (your child) go to a doctor’s office or clinic to get health care for yourself (himself or herself)? [4 or more times]

d

Broward/ Duval

19.0% 25.3% 26.0% 23.8% 26.5% 21.6% 19.1% 0.0034 ↔c

Rural N/A 32.5% 29.8% 30.9% 35.0% 28.9% 33.4% -0.0121 ↔c

Note. Statistical tests are based on weighted data. *Trends were statistically significant at p<0.05. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

cThe average annual change was not statistically significant, so there was no change across the years.

dResponses for this question were categorized into three levels (0 times, 1 – 3 times, and 4 or more times). All three levels are

reported.

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Reform vs. Non-Reform Counties Table 4 compares survey findings regarding enrollees’ access to care in DY6, DY7, and DY8. Enrollees in Reform counties in DY8 reported significantly greater ease of getting care, tests, or treatment as needed (“Always”) than in the previous year (68% versus 59.8%). There was a statistically significant increasing trend in the percentage of enrollees seeing a doctor for non-urgent care one to three times in the previous six months, coupled with a significant decrease in the percentage of those seeking non-urgent care four or more times. More enrollees in DY8 reported it was always easy to get an appointment with a specialist than in DY7 (56.3% versus 47.9%), though the change for this variable was not statistically significant. In Non-Reform counties, enrollees in DY8 reported significantly greater ease of always getting a specialist appointment than in DY7 (59% versus 52.4%) and of getting care, tests, or treatment as needed (67.3% versus 53.9%). Similarly to Reform counties, in Non-Reform counties there was a statistically significant decrease over time in the percentage of enrollees having sought non-urgent care four or more times in the previous six months (Table 4). Table 4: Domain i.a: CAHPS Survey Data, Reform vs. Non-Reform Counties, DY6 – DY8

Domain i.a: Are services accessible to enrollees? Have there been changes in the accessibility of services to enrollees over the course of the demonstration? Has the demonstration resulted in more appropriate use of services by

enrollees?

Questions DY6 DY7 DY8 Coefficient Trend In the last 6 months, how often was it easy to get appointment with specialists? [Always]

Reform 47.9% 47.9% 56.3% 0.0766 ↔b

Non-Reform 49.8% 52.4% 59.0% 0.1551 ↑*

In the last 6 months, how often was it easy to get the care, tests, or treatment you thought you (your child) needed through your health plan? [Always]

Reform 56.4% 59.8% 68.0% 0.2074 ↑*

Non-Reform 55.3% 53.9% 67.3% 0.1906 ↑*

A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt. Do you (your child) have a personal doctor? [Yes]

Reform 90.4% 91.5% 91.3% 0.1003 ↔b

Non-Reform 87.4% 87.0% 87.9% 0.0035 ↔b

In the last 6 months, not counting the times you went to an emergency room, how many times did you (your child) go to a doctor’s office or clinic to get health care for yourself (himself or herself)? [0 times]

a

Reform 18.1% 18.6% 20.7% 0.0543 ↔b

Non-Reform 20.3% 20.9% 20.1% 0.0071 ↔b

In the last 6 months, not counting the times you went to an emergency room, how many times did you (your child) go to a doctor’s office or clinic to get health care for yourself (himself or herself)? [1 – 3 times]

a

Reform 54.7% 59.3% 59.2% 0.1428 ↑*

Non-Reform 54.6% 55.8% 58.2% 0.0638 ↔b

In the last 6 months, not counting the times you went to an emergency room, how many times did you (your child) go to a doctor’s office or clinic to get health care for yourself (himself or herself)? [4 or more times]

a

Reform 27.1% 22.1% 20.1% -0.2385 ↓*

Non-Reform 25.0% 23.3% 21.7% -0.0938 ↓*

Note. Statistical tests are based on weighted data. *Trends were statistically significant at p<0.05. aResponses for this question were categorized into three levels (0 times, 1 – 3 times, and 4 or more times). All three levels are

reported. bThe average annual change was not statistically significant, so there was no change across the years.

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HEDIS Data

Reform Plans Over Time Reform plans improved performance in eight out of the twelve measures that were used to assess appropriate utilization of services. The measure with the best trend was Annual Dentist Visits – Total, with Reform plans increasing the percentage of enrollees receiving this service by 4.2 percentage points annually. Reform plans also continued to improve the quality of care for children as seen by positive trends in all three Well-Child measures (Well-Child 0 Visits has a negative average annual change, indicating improvement as it is an inverse measure) (Table 5). Reform plan performance declined in four HEDIS measures. The proportion of adults with access to preventive and ambulatory health services declined over time. The measure component with the sharpest declining trend was Adults’ Access to Preventive/Ambulatory Health Services – Ages 65+. Mixed results were found for measures that assess performance in providing care to women. Reform plans reported an average annual increase of 1.9 percentage points for Cervical Cancer Screening. HEDIS reports for timeliness of Prenatal Care services improved by 0.3 percentage points. However, scores declined for Postpartum Care by -0.2 percentage points.

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Table 5: Domain i.a: HEDIS Data, Reform Plans Over Time, Trend Analysis Improving Trends 2008 2009 2010 2011 2012 2013 Average

Annual Change

a

Annual Dentist Visits (ADV) 15.2% 28.5% 33.4% 34.0% 35.3% 40.4% 4.2%

Well-Child 6+ Visits (W15 6) 44.4% 49.3% 35.4% 46.5% 58.4% 55.6% 2.7%

Cervical Cancer Screening (CCS)

48.2% 52.2% 50.8% 53.2% 56.8% 58.2% 1.9%

Adolescent Well Care (AWC) 44.2% 46.5% 46.3% 46.2% 47.6% 48.5% 0.7%

Well-Child Visits in the Third, Fourth, Fifth, and Sixth Years of Life (W34)

71.3% 75.7% 72.7% 75.0% 75.6% 75.6% 0.7%

Well-Child 0 Visits INVERSE (W15 0)

4.9% 1.6% 6.0% 3.0% 2.1% 1.6% -0.5%

Prenatal Care (PPC-Pre) 66.6% 67.4% 75.2% 68.4% 72.1% 67.2% 0.3%

Adults’ Access to Preventive/Ambulatory Health Services – Ages 45–64 (AAP45)

84.7% 84.9% 85.5% 84.9% 85.0% 0.1%

Declining Trends 2008 2009 2010 2011 2012 2013 Average Annual Change

a

Adults’ Access to Preventive/Ambulatory Health Services – Ages 65+ (AAP65)

83.6% 83.7% 84.2% 74.0% 76.2% -2.5%

Adults’ Access to Preventive/Ambulatory Health Services – Ages Total (AAP Total)

77.2% 77.6% 77.1% 75.0% 74.7% -0.8%

Adults’ Access to Preventive/Ambulatory Health Services – Ages 20–44 (AAP20)

71.8% 71.2% 71.2% 69.8% 69.2% -0.7%

Postpartum Care (PPC-Post) 53.0% 51.5% 52.1% 49.3% 52.9% 51.4% -0.2% aAverage Annual Change is the slope of the trend line derived using Excel functionality to regress the annual HEDIS scores.

Example interpretation: On average, the ADV score increased by 4.2 percentage points annually. Please note that the Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

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Reform vs. Non-Reform Plans Comparative Trend Analysis

Reform plans had more positive trends compared to Non-Reform plans in eight out of twelve measures used to assess appropriate utilization of services. The most significant differences were found in three scores: Cervical Cancer Screening, Annual Dental Visits – Total, and Postpartum Care. There were five measures where both Reform and Non-Reform plans showed improving trends, but Reform plans had greater rates of annual change (Annual Dentist Visits, Cervical Cancer Screening, Well-Child 0 Visits INVERSE, Well-Child 6+ Visits, and Well-Child Visits in the Third, Fourth, Fifth, and Sixth Years of Life). However, there were two measures where both Reform and Non-Reform plans declined, but Reform plans had lower rates of change (Postpartum Care and Adults’ Access to Preventive/Ambulatory Health Services – Ages 20 – 44). These last figures indicate that like Reform plans, Non-Reform plans did not do as well with the Adults’ Access to Preventive/Ambulatory Health Services measures. However, in general, Reform plans performed similarly to Non-Reform plans for this measure (Table 6). Reform plans were improving at a slower rate than Non-Reform plans in four out of the twelve measures (Adults’ Access to Preventive/Ambulatory Health Services - Ages 65+, Adolescent Wellness Care, Prenatal Care, and Adults’ Access to Preventive/Ambulatory Health Services - Ages Total) used to assess quality of care. For instance, in Reform plans, Adolescent Well Care had an average annual change of 0.7 percentage points. However, Non-Reform plans improved at an average annual rate of 1.5 percentage points. So, the difference in the average annual change between Reform and Non-Reform is negative (-0.7).

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Table 6: Domain i.a: HEDIS Data, Reform vs. Non-Reform Plans, Comparative Trend Analysis Reform Trend Improves Compared to Non-Reform

Average Annual Change

(Reform)

Average Annual Change (Non-

Reform)

Difference (Reform –

Non-Reform)

a

Cervical Cancer Screening (CCS) 1.9% 0.1% 1.8%

Annual Dentist Visits (ADV) 4.2% 2.6% 1.6%

Postpartum Care (PPC-Post) -0.2% -0.7% 0.5%

Well-Child 0 Visits INVERSE (W15 0) -0.5% 0.0% -0.5%

Well-Child 6+ Visits (W15 6) 2.7% 2.4% 0.4%

Adults’ Access to Preventive/Ambulatory Health Services – Ages 45–64 (AAP45) 0.1% -0.2% 0.3%

Well-Child Visits in the Third, Fourth, Fifth, and Sixth Years of Life (W34) 0.7% 0.6% 0.1%

Adults’ Access to Preventive/Ambulatory Health Services – Ages 20–44 (AAP20) -0.7% -0.7% 0.1%

Reform Trend Improves at a Slower Rate Compared to Non-Reform Average Annual Change

(Reform)

Average Annual Change (Non-

Reform)

Difference (Reform –

Non-Reform)

a

Adults’ Access to Preventive/Ambulatory Health Services – Ages 65+ (AAP65) -2.5% -1.3% -1.2%

Adolescent Well Care (AWC) 0.7% 1.5% -0.7%

Prenatal Care (PPC-Pre) 0.3% 0.6% -0.3%

Adults’ Access to Preventive/Ambulatory Health Services – Ages Total (AAP Total) -0.8% -0.7% -0.1%

aDifference is the difference in the Average Annual Change of Reform plans and Non-Reform plans. Example Interpretation:

The average annual change in Reform plans’ ADV HEDIS Scores is 1.6 percentage points higher than Non-Reform plans’ HEDIS Scores.

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Comparative Difference Analysis

Reform plans had better scores than Non-Reform plans on seven out of the twelve measures that were used to assess appropriate utilization of services. They performed better in all four Adults’ Access to Preventive/Ambulatory Health Services measures. Further, the magnitude of the difference is noticeable for each measure. Adults’ Access to Preventive/Ambulatory Health Services – ages 65+ had the highest Average Annual Difference with 11.3 percentage points, followed by Annual Dentist Visits – Total with a 11.1% Average Annual Difference. The exceptions were Well-Child Visits in the Third, Fourth, Fifth, & Sixth Years of Life that had a less than 1 percentage point difference and Well-Child 0 Visits INVERSE that experienced no change (Table 7). Reform plans had declining scores compared to Non-Reform plans on five out of the twelve measures that were used to assess appropriate utilization of services. These were measures assessing care for children, adolescents, women, and expectant and new mothers.

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Table 7: Domain i.a: HEDIS Data, Reform vs Non-Reform Plans, Comparative Difference Analysis Reform Trend Improves

Annually Compared to Non-Reform

2008 (Reform–

Non-Reform)

2009 (Reform–

Non-Reform)

2010 (Reform–

Non-Reform)

2011 (Reform–

Non-Reform)

2012 (Reform–

Non-Reform)

2013 (Reform–

Non-Reform)

Average Annual

Difference (Reform–

Non-Reform)

a

Adults’ Access to Preventive/Ambulatory Health Services – Ages 65+ (AAP65)

8.9% 16.8% 14.3% 9.9% 6.4% 11.3%

Annual Dentist Visits (ADV) 3.9% 10.7% 7.9% 17.9% 17.7% 8.8% 11.1%

Adults’ Access to Preventive/Ambulatory Health Services – Ages Total (AAP Total)

3.6% 6.1% 5.1% 5.1% 3.8% 4.7%

Adults’ Access to Preventive/Ambulatory Health Services – Ages 45–64 (AAP45)

2.5% 3.7% 4.0% 4.4% 3.6% 3.6%

Adults’ Access to Preventive/Ambulatory Health Services – Ages 20–44 (AAP20)

2.7% 3.4% 3.1% 3.6% 2.9% 3.1%

Well-Child Visits in the Third, Fourth, Fifth, and Sixth Years of Life (W34)

0.2% 3.2% -2.2% 0.3% -0.1% 2.4% 0.6%

Well-Child 0 Visits INVERSE (W15 0)

2.1% -1.4% 1.7% -0.3% -1.1% -1.1% 0.0%

Reform Trend Declines Annually Compared to Non-

Reform

2008 (Reform–

Non-Reform)

2009 (Reform–

Non-Reform)

2010 (Reform–

Non-Reform)

2011 (Reform–

Non-Reform)

2012 (Reform–

Non-Reform)

2013 (Reform–

Non-Reform)

Average Annual

Difference (Reform–

Non-Reform)

a

Well-Child 6+ Visits (W15 6) 0.4% -1.7% -10.6% -4.7% 2.2% -0.7% -2.5%

Cervical Cancer Screening (CCS) -8.4% -1.6% -4.5% -2.5% 1.7% 1.7% -2.3%

Prenatal Care (PPC-Pre) -5.1% -1.7% 5.6% -3.3% -1.0% -6.0% -1.9%

Postpartum Care (PPC-Post) -5.5% 1.3% -0.6% -5.3% 1.1% -0.8% -1.6%

Adolescent Well Care (AWC) 2.3% 0.5% 0.6% -2.9% -0.6% -1.6% -0.3% aAverage Annual Difference is the average of the difference between the Reform plans’ HEDIS Score and the Non-Reform plans’

HEDIS Score for each year. Example interpretation: On average, Reform plans’ AAP65 HEDIS Scores were 11.3 percentage points higher than Non-Reform plans’ AAP65 HEDIS Scores.

45

Claims, Eligibility, and Encounter Data: Utilization Analyses

Univariate Analyses MEG #1 Enrollees

As can been seen in Table 8, for Reform counties, average Per Member Per Month (PMPM) utilization of emergency room services by MEG #1 enrollees was steady for the first two years of the study period (SFY1011 – SFY1213); however, there was a marked reduction in SFY1213. For inpatient utilization, there was a marked reduction between SFY1011 and SFY1112, but it remained steady between SFY1112 and SFY1213. Utilization for outpatient, medical, and pharmacy services fluctuated over the study period. However, the fluctuations were minimal for outpatient and pharmacy services. For Control counties, utilization of emergency room visits was steady for the first two fiscal years; however, there was a marked decrease in SFY1213. Utilization of medical services fluctuated over the study period and pharmacy utilization remained steady. Compared to Control counties, utilization in Reform counties was higher for all years and service types with the exception of medical services in SFY1112. Moreover, the differences increased for inpatient, outpatient, and pharmacy services. Differences decreased for emergency room use over time, while the differential trend fluctuated for medical services. MEG #2 Enrollees

As can been seen in Table 8, for Reform counties, average PMPM utilization of emergency room services by MEG #2 enrollees was steady for the first two years of the study period; however, there was a marked reduction in SFY1213. Utilization of inpatient services remained steady over the study period. Outpatient and medical service utilization fluctuated over time, while pharmacy utilization remained stable. For Control counties, emergency room visits decreased over the study period, while medical and pharmacy use fluctuated. Inpatient and outpatient utilization showed slight decreases. Compared to Control counties, utilization in the Reform was higher for all years and services types with the exception of medical services in SFY1112. Moreover, the differences increased for pharmacy services. Overall trends in differences fluctuated over time for the remaining service types; however, in general the magnitude of the fluctuation was small.

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Table 8: Average PMPM Health Service Utilization for All Enrollees Mean PMPM Total Utilization SFY1011 – SFY1213 (Without MediPass in Reform)

Reform Control Difference

Broward/Duval Hillsborough/Orange (Reform – Control)

State Fiscal Year MEG #1 MEG #2 MEG #1 MEG #2 MEG #1 MEG #2 SFY1011

Emergency Room 0.090 0.045 0.075 0.043 0.015 0.002

Inpatient 0.274 0.039 0.183 0.028 0.091 0.012

Outpatient 0.670 0.192 0.395 0.108 0.275 0.084

Medical 4.158 0.962 2.814 0.736 1.344 0.226

Pharmacy 2.122 0.434 1.648 0.276 0.474 0.159

SFY1112

Emergency Room 0.091 0.045 0.076 0.041 0.015 0.004

Inpatient 0.242 0.036 0.134 0.025 0.108 0.011

Outpatient 0.657 0.181 0.348 0.100 0.309 0.081

Medical 2.486 0.530 3.341 0.882 -0.855 -0.352

Pharmacy 2.097 0.445 1.588 0.267 0.509 0.178

SFY1213

Emergency Room 0.042 0.019 0.036 0.018 0.007 0.001

Inpatient 0.245 0.038 0.133 0.025 0.112 0.013

Outpatient 0.665 0.190 0.314 0.099 0.351 0.091

Medical 3.510 0.836 2.689 0.712 0.820 0.125

Pharmacy 2.166 0.483 1.575 0.273 0.591 0.210

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Multivariate Analyses

The use of multivariate analysis controls for the impact of differences in socio-demographic factors such as age, gender, and race between the Reform and Control counties on enrollee expenditures. Thus, the overall effect of Reform on utilization for MEG #1 and MEG #2 enrollees can be more precisely assessed. As illustrated in equation (1) in the multivariate analyses data and methods section, the multivariate analysis generated three coefficients relevant to assess the impact of Reform on utilization of services. These incidence rate ratios (IRRs) are those associated with “Month,” “Reform,” and “Reform*Month.” The IRR provides a relative measure of the effect of Reform on utilization of services and is the incidence rate for Reform divided by the incidence rate for program Control counties. The IRR can be interpreted as the percent change in the rate of utilizing a specific service or the percent change in total PMPM utilization. The IRR “Month” indicates the overall time trend for the study period (SFY1011 – SFY1213) and represents the overall time trend in the Reform and Control counties combined.

The IRR “Reform” is the shift in the intercept associated with Reform or the average difference in the rate of utilization associated with Reform counties during the Reform period (SYF1011 – SFY1213). The IRR “Reform*Month” indicates the difference in the time trend between the Reform and Control counties. An IRR less than one means that Reform is associated with lower rates of utilization over the study period compared to the Control counties. Conversely, an IRR with a value greater than one indicates an increase in the rate of utilization over the study period due to Reform.

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MEG #1 Enrollees

Table 9 provides the adjusted mean use for PMPM inpatient, outpatient, medical, pharmacy, and emergency room utilization for MEG #1 enrollees. The adjusted mean is the mean after adjusting for differences in the rate of PMPM utilization that can be attributed to differences in age, gender, race, and risk score between the Reform and Control counties. Overall, the predicted means of the number of services utilized PMPM were greater in the Reform counties compared to the Control counties. Table 9: Predicted Mean Use of Inpatient, Outpatient, Medical, Pharmacy, and Emergency Room Utilization for MEG #1 Enrollees Adjusting for Sociodemographic Factors (Age, Gender, Race)

Mean Min – Max

Inpatient Number of Inpatient Service Visits 0.2168 0.2016 – 1.2597

Reform: Number of Inpatient Service Visits 0.2690 0.0348 – 1.3485

Control: Number of Inpatient Service Visits 0.1765 0.0202 – 0.9877

Outpatient Number of Outpatient Service Visits 0.4917 0.1292 – 1.2310

Reform: Number of Outpatient Service Visits 0.6601 0.2543 – 1.2310

Control: Number of Outpatient Service Visits 0.3610 0.1292 – 0.7556

Medical Number of Medical Service Visits 2.9271 1.8024 – 6.9487

Reform: Number of Medical Service Visits 3.1501 1.9633 – 8.0703

Control: Number of Medical Service Visits 2.7697 1.8024 – 6.7317

Pharmacy Number of Prescriptions 1.4632 0.4267 – 3.8572

Reform: Number of Prescriptions 1.6664 0.5271 – 3.8572

Control: Number of Prescriptions 1.3190 0.4267 – 2.9936

Emergency Room Number of Emergency Room Visits 0.0654 0.1204 – 0.1704

Reform: Number of Emergency Room Visits 0.0709 0.1380 – 0.1704

Control: Number of Emergency Room Visits 0.0615 0.1204 – 0.1468

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Table 10 provides the IRR for PMPM inpatient, outpatient, medical, pharmacy, and emergency room utilization for MEG #1 enrollees for the study period (SFY1011 – SFY1213). For inpatient, outpatient, and emergency room visits, overall there is a decrease in the rate of PMPM utilization over the study period. Overall, medical service and pharmacy rates of utilization increased during the study period. When looking at the Reform counties compared to the Control counties, Reform counties had higher rates of utilization for all service types. Furthermore, Reform counties’ rate of utilization of inpatient, outpatient, and pharmacy services increased over time at a greater rate compared to Control counties. This finding is represented by the IRR for the interaction term Reform*Month. For example, Reform counties’ increase in the rate of utilization (IRR=1.0067) over each month was .67% higher than Control Counties. However, Reform counties’ rate of utilization decreased over time for Medical services. The results for the interaction of the Reform and time (Reform*Month) for emergency room visits were not statistically significant, suggesting the emergency room services are used similarly in Reform and Control counties. Table 10: Incidence Rate Ratio (IRR) of PMPM Inpatient, Outpatient, Medical, Pharmacy, and Emergency Room Utilization for MEG #1 Enrollees

IRR 95% CI P-valuea

Inpatient Month 0.9841 0.9837, 0.9845 <.0001

Reform 1.0067 1.0313, 1.1030 <.0001

Reform*Month 1.0067 1.0061, 1.0073 <.0001

Outpatient Month 0.9887 0.9884, 0.9890 <.0000

Reform 1.3344 1.3023, 1.3673 <.0000

Reform*Month 1.0054 1.0050, 1.0058 <.0001

Medical Month 1.0013 1.0011, 1.0015 <.0001

Reform 1.5034 1.4794, 1.5277 <.0001

Reform*Month 0.9949 0.9947, 0.9952 <.0001

Pharmacy

Month 1.0013 1.0011, 1.0014 <.0001

Reform 1.1853 1.1649, 1.2060 <.0001

Reform*Month 1.0012 1.0001, 1.0014 <.0001

Emergency Room Month 0.9831 0.9824, 0.9837 <.0001

Reform 1.1765 1.1142, 1.2421 <.0001

Reform*Month 0.9996 0.9986, 1.0006 0.4720

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MEG #2 Enrollees

Table 11 provides the adjusted mean for PMPM inpatient, outpatient, medical, pharmacy, and emergency room utilization for MEG #2 enrollees. The adjusted mean is the mean after adjusting for differences in the rate of PMPM utilization that can be attributed to differences in age, gender, race, and risk score between the Reform and Control counties. Overall, the predicted means of the number of services utilized PMPM were greater in the Reform counties compared to the Control counties.

Table 11: Predicted Mean Use of Inpatient, Outpatient, Medical, Pharmacy, and Emergency Room Utilization for MEG #2 Enrollees Adjusting for Sociodemographic Factors (Age, Gender, Race)

Mean Min – Max

Inpatient Number of Inpatient Service Visits 0.0318 0.0026 – 0.2947

Reform: Number of Inpatient Service Visits 0.0320 0.0043 – 0.2947

Control: Number of Inpatient Service Visits 0.0260 0.0226 – 0.2110

Outpatient Number of Outpatient Service Visits 0.1350 0.0377 – 0.4935

Reform: Number of Outpatient Service Visits 0.1810 0.0712 – 0.4935

Control: Number of Outpatient Service Visits 0.0986 0.0377 – 0.2769

Medical Number of Medical Service Visits 0.7258 0.4450 – 3.3535

Reform: Number of Medical Service Visits 0.7549 0.4450 – 3.8139

Control: Number of Medical Service Visits 0.7030 0.4584 – 3.2138

Pharmacy Number of Prescriptions 0.2992 0.1434 – 1.8719

Reform: Number of Prescriptions 0.3887 0.2360 – 1.9681

Control: Number of Prescriptions 0.2282 0.1434 – 1.1502

Emergency Room

Number of Emergency Room Visits 0.0328 0.0074 – 0.1227

Reform: Number of Emergency Room Visits 0.0338 0.0079 – 0.1227

Control: Number of Emergency Room Visits 0.0320 0.0074 – 0.1167

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Table 12 provides the IRR for inpatient, outpatient, medical, pharmacy, and emergency room utilization for MEG #2 enrollees. For inpatient, outpatient, medical, and emergency room visits, overall there is a decrease in the rate of PMPM utilization over the study period. Overall, pharmacy rate of utilization increased during the study period. When looking at the Reform counties compared to the Control counties, Reform had higher rates of utilization for all service types. Based on the interaction term (Reform*Month), Reform counties’ rate of utilization is increasing faster when compared to Control counties for inpatient, outpatient, and pharmacy services. However, the utilization is increasing at a slower rate compared to Control counties for medical services. The results for the interaction of the Reform and time (Reform*Month) for emergency room visits were not statistically significant, suggesting the emergency room services are used similarly in Reform and Control counties. Table 12: Incidence Rate Ratio (IRR) of PMPM Inpatient, Outpatient, Medical, Pharmacy, and Emergency Room Utilization for MEG #2 Enrollees

IRR 95% CI P-valuea

Inpatient Month 0.9824 0.9820, 0.9828 <.0001

Reform 1.1766 1.1426, 1.2115 <.0001

Reform*Month 1.0046 1.0041, 1.0052 <.0001

Outpatient Month 0.9940 0.9938, 0.9942 <.0001

Reform 1.6763 1.6501, 1.7030 <.0001

Reform*Month 1.0020 1.0014, 1.0019 <.0001

Medical Month 0.9979 0.9978, 0.9980 <.0001

Reform 1.4673 1.4553, 1.4795 <.0001

Reform*Month 0.9953 0.9941, 0.9944 <.0001

Pharmacy Month 1.0032 1.0030, 1.0033 <.0001

Reform 1.5446 1.5269, 1.5625 <.0001

Reform*Month 1.0017 1.0015, 1.0019 <.0001

Emergency Room Month 0.9780 0.9978, 0.9784 <.0001

Reform 1.0343 1.0051, 1.0643 0.0210

Reform*Month 1.0004 0.9999, 1.0010 0.1030

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Summary: Domain i.a With a few exceptions, respondent self-reports indicate that, overall, health-related services are accessible to Medicaid enrollees. Differences over time are mixed, though more statistically significant increases (improvements) were noted. For example, between DY2 and DY8 there was a substantial increase in the percentage of enrollees who reported it was “Always” easy to get specialist appointments, both in Broward/Duval and in rural counties (though the change was not statistically significant). Significantly more enrollees over time have a personal doctor and thought it was always easy to get care, tests, and treatment through their health plan, though the increases were statistically significant only in Broward and Duval counties (p<0.05). In Reform counties, there was a significant increase over time in the ease of getting care, tests, or treatment as needed (“Always”) (p<.05). There was also a statistically significant increasing trend in the percentage of enrollees seeing a doctor for non-urgent care one to three times in the previous six months, coupled with a significant decrease in the percentage of those seeking non-urgent care four or more times.

Based on the trend analysis of the HEDIS measures, Reform plans have improved the appropriate utilization of services in some areas but struggle in others. Comparative trend analysis reveals Reform plans are improving at a faster rate than Non-Reform plans in the Preventive and Wellness measures, with only four out of the twelve measures having a smaller rate of change than Non-Reform plans (AAP65, AWC, PPC-Pre, and AAP Total). When comparing measures in absolute terms, results are mixed. Reform plans had superior performance compared to Non-Reform plans in several areas. Reform plans had better scores for Preventive and Wellness measures associated with children (W15 6, W34, W15 0), adolescents (AWC), women (CCS), and Annual Dentist Visits – Total (ADV). However, they were outperformed in almost all Adults’ Access to Preventive/Ambulatory Health Services measure components with the exception of Adults’ Access to Preventive/Ambulatory Health Services – Ages 45–64 (AAP45). In the case of the scores relating to mothers (PPC-Pre and PPC-Post), Reform plans had superior performance when compared to Non-Reform plans in PPC-Pre and were outperformed by Non-Reform plans in the PPC-Post measure. Overall, PMPM utilization of health services (inpatient, outpatient, medical, and pharmacy) was greater in the Reform counties compared to the Control counties for both TANF and SSI enrollees. For emergency room visits, this was the case only for TANF enrollees. However, the rate of growth was lower in the Reform counties relative to the Control counties, particularly for inpatient services and physician office-based services (medical). The rate of growth for hospital outpatient and pharmacy services, on the other hand, was greater in the Reform counties. Reform counties will achieve lower utilization rates over time for inpatient services if the current trend continues in the future.

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Domain i.b: Has the quality of care that enrollees receive improved during the demonstration? What have managed care plans done to improve quality of care?

CAHPS SURVEY DATA

Counties Over Time Table 13 provides percentages for Broward and Duval separate from the rural counties for four CAHPS survey measures in each of the seven survey years (statistical comparisons were calculated only for DY2 through DY8). While positive and negative changes are observed from year to year, the primary objective was to examine the trend over the full six-year period. The coefficient shown in the table indicates the direction of the overall trend (upward or downward, depending on its sign), as well as the level of statistical significance. If the coefficient is not statistically significant, the change across time is not statistically significant, though it may have practical significance. The numbers presented below represent the percentage of enrollees who gave the highest ratings to their doctors and health plans (ratings of 9 and 10, or Level 3, on a scale from 0 to 10). In Broward and Duval counties, trends over time (DY2 – DY8) showed a statistically significant increase (p<.05) in enrollees reporting the highest level rating (Level 3) for their health plans. The percentage of enrollees who rated their overall health care highly increased in DY8 compared to DY7 (65.6% versus 61.8%), though the change was not statistically significant (Table 13). In rural counties, trends over time (DY2 – DY8) did not show any statistically significant changes. However, there was a recent increase in the percentage of enrollees who reported the highest level rating (Level 3) for their specialist doctor, but a decrease in the percentage of enrollees who gave a similar rating to their overall health care, health plan, and personal doctor (Table 13).

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Table 13: Domain i.b: CAHPS Survey Data, Counties Over Time, DY1 – DY8 Domain i.b: Has the quality of care that enrollees receive improved during the demonstration? What have managed

care plans done to improve quality of care?

Questions DY1a DY2

b DY3 DY4 DY6 DY7 DY8 Coefficient Trend

Using any number from 0 – 10, where 0 is the worst and 10 is the best health care possible, what number would you use to rate all your health care in the last 6 months? [Level 3 (9–10)]

Broward/ Duval

66.5% 59.6% 60.5% 60.1% 63.2% 61.8% 65.6% -0.0026 ↔c

Rural N/A 57.6% 59.2% 55.8% 62.6% 65.6% 58.2% 0.0795 ↔c

Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would you use to rate your (your child’s) health plan? [Level 3 (9–10)]

Broward/ Duval*

58.1% 57.4% 54.1% 50.2% 59.0% 61.0% 59.7% 0.0373 ↑*

Rural N/A 51.3% 52.5% 45.2% 52.7% 56.3% 51.5% 0.0566 ↔c

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor? [Level 3 (9–10)]

Broward/ Duval

70.2% 73.4% 73.0% 73.1% 73.7% 73.0% 74.4% 0.0184 ↔c

Rural N/A 67.9% 70.3% 66.9% 71.9% 72.2% 61.1% 0.0261 ↔c

Using any number from 0 – 10, where 0 is the worst and 10 is the best specialist possible, what number would you use to rate your (your child’s) specialist? [Level 3 (9–10)]

Broward/ Duval

60.4% 63.3% 63.1% 62.8% 67.1% 64.1% 63.2% 0.0319 ↔c

Rural N/A 68.0% 67.3% 68.5% 53.8% 54.5% 77.8% -0.1217 ↔c

Note. Statistical tests are based on weighted data. *Trend was statistically significant at <0.05. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

cThe average annual change was not statistically significant, so there was no change across the years.

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Reform vs. Non-Reform Counties In Reform counties in DY8 compared to DY7, a greater proportion of enrollees reported the highest level of satisfaction (Level 3) with regard to their health care overall and their specialist doctor. However, the proportion of enrollees who had the highest level of satisfaction (Level 3) with their health plan declined slightly. None of the changes were statistically significant. In Non-Reform counties, a greater proportion of enrollees reported the highest level of satisfaction (Level 3) with regard to their health care in the last 6 months, their specialist, and their personal doctor in DY8 compared to DY7. Only the change with regard to specialist doctor was statistically significant (p<.05) (Table 14). Table 14: Domain i.b: CAHPS Survey Data, Reform vs. Non-Reform, DY6 – DY8

Domain i.b: Has the quality of care that enrollees receive improved during the demonstration? What have managed care plans done to improve quality of care?

Questions DY6 DY7 DY8 Coefficient Trend

Using any number from 0 – 10, where 0 is the worst and 10 is the best health care possible, what number would you use to rate all your health care in the last 6 months? [Level 3 (9–10)]

Reform 63.2% 62.0% 65.1% -0.0069 ↔a

Non-Reform

60.5% 60.7% 64.3% 0.0561 ↔a

Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would you use to rate your (your child’s) health plan? [Level 3 (9–10)]

Reform 58.5% 60.7% 59.1% 0.0536 ↔a

Non-Reform

58.4% 59.5% 59.1% 0.0249 ↔a

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor? [Level 3 (9–10)]

Reform 73.5% 73.0% 73.4% -0.0173 ↔a

Non-Reform

73.2% 70.3% 74.1% -0.0358 ↔a

Using any number from 0 – 10, where 0 is the worst and 10 is the best specialist possible, what number would you use to rate your (your child’s) specialist? [Level 3 (9–10)]

Reform 66.1% 63.2% 64.2% -0.0891 ↔a

Non-Reform*

66.6% 69.6% 74.0% 0.1622 ↑*

Note. Statistical tests are based on weighted data. *Trend was statistically significant at <0.05. aThe average annual change was not statistically significant, so there was no change across the years.

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HEDIS Data

Reform Plans Over Time Reform plans improved performance over time in all but two of the quality of care measures. The measure with the most positive trend was Comprehensive Diabetes-Eye Exam (CDC-Eye), which increased by 2.5 percentage points annually. Reform plans improved for each measure related to Comprehensive Diabetes Care. The most significant improvements were for the percentage of enrollees obtaining eye exams, good control of HbA1C, and LDL control (Table 15). Reform plan performance declined in Controlling Blood Pressure and Follow-up After Hospitalization for Mental Illness – 7 Day. However, the decline was minimal with less than a 1 percentage point decrease in both cases. Table 15: Domain i.b: HEDIS Data, Reform Plans Over Time, Trend Analysis

Improving Trends 2008 2009 2010 2011 2012 2013 Average Annual

Changea

Comprehensive Diabetes – Eye Exam (CDC Eye) 35.7% 44.0% 45.4% 49.3% 50.2% 48.7% 2.5%

Anti-Depressant Medication Management – Effective Continuation Phase Treatment (AMM Continuation)

N/A 29.8% 43.8% 44.0% 43.1% 41.7% 2.3%

Comprehensive Diabetes – HbA1C Good Control (CDC HbA1C Good)

32.2% 48.1% 47.5% 43.7% 47.9% 43.6% 1.5%

Anti-Depressant Medication Management – Effective Acute Phase Treatment (AMM Acute)

N/A 52.0% 56.3% 56.4% 57.4% 55.1% 0.7%

Comprehensive Diabetes – LDL-C Control (CDC LDLC)

29.3% 35.5% 36.1% 36.9% 37.8% 32.1% 0.6%

Follow-up After Hospitalization for Mental Illness – 30 Day (FMH 30)

35.5% 46.7% 41.3% 44.3% 41.2% 40.8% 0.4%

Comprehensive Diabetes – Nephropathy (CDC Neph),

79.3% 80.3% 81.9% 83.1% 82.3% 80.2% 0.4%

Comprehensive Diabetes – HbA1C Testing (CDC HbA1C Testing)

78.9% 80.1% 82.8% 81.9% 82.2% 79.5% 0.2%

Comprehensive Diabetes – LDL Screening (CDC LDLS)

80.0% 80.2% 83.5% 81.8% 81.9% 80.1% 0.1%

Comprehensive Diabetes – HbA1C Poor Control INVERSE (CDC HbA1C Poor)

48.4% 46.8% 44.9% 48.6% 43.6% 48.9% -0.1%

Declining Trend 2008 2009 2010 2011 2012 2013 Average Annual

Changea

Controlling Blood Pressure – Total (CBP) 46.3% 55.9% 53.4% 46.3% 52.9% 45.4% -0.6%

Follow-up After Hospitalization for Mental Illness – 7 Day (FMH7)

20.6% 29.3% 25.4% 23.1% 22.7% 23.5% -0.2%

Note. Data for AMM Continuation and AMM Acute are not available for 2008. a

Average Annual Change is the slope of the trend line derived using Excel functionality to regress the annual HEDIS scores. Example interpretation: On average, the AMM Continuation score increased by 2.3 percentage points annually. Please note that the Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

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Reform vs. Non-Reform Plans Comparative Trend Analysis

Reform plans had more favorable trends compared to Non-Reform plans in two out of the twelve measures used to assess quality of care. The measure where the difference in trends was the highest was for Antidepressant Medication Management – Effective Continuation Phase Treatment. Reform plans reported an average annual change of 2.5 percentage points in this measure. This average annual change is 0.8 percentage points higher than the change reported by Non-Reform plans. The average annual change for Reform plans also improved annually at a greater rate in Comprehensive Diabetes – Eye Exam, at 2.5 percentage points. However, for these measures in which the Reform plans had better trends, the differences from Non-Reform plans were minimal and less than one percentage point (Table 16). Reform plans improved at a slower rate than Non-Reform plans in ten out of the twelve measures (Comprehensive Diabetes – HbA1C Good Control, Follow-up After Hospitalization for Mental Illness – 7 Day, Follow-up After Hospitalization for Mental Illness – 30 Day, Comprehensive Diabetes – HbA1C Poor Control INVERSE, Anti-Depressant Medication Management – Effective Acute Phase Treatment, Controlling Blood Pressure- Total, Comprehensive Diabetes – LDL Screening, Comprehensive Diabetes – HbA1C Testing, Comprehensive Diabetes LDLC Control, and Comprehensive Diabetes-Nephropathy) used to assess quality of care. For instance, the Comprehensive Diabetes – Good Control measure for Reform plans had an average annual change of 1.5 percentage points. However, Non-Reform plans improved at an average annual rate of 2.8 percentage points. So, the difference in the average annual change between Reform and Non-Reform is negative (-1.2%).

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Table 16: Domain i.b: HEDIS Data, Reform vs Non-Reform Plans, Comparative Trend Analysis Reform Trend Improves at a Higher Rate Compared to

Non-Reform* Average Annual

Change (Reform)

Average Annual Change

(Non-Reform)

Difference (Reform –

Non-Reform)a

Anti-Depressant Medication Management – Effective Continuation Phase Treatment (AMM Continuation)

2.3% 1.5% 0.8%

Comprehensive Diabetes – Eye Exam (CDC Eye) 2.5% 1.8% 0.7%

Reform Trend Improves at a Slower Rate Compared to

Non-Reformb

Average Annual Change

(Reform)

Average Annual Change

(Non-Reform)

Difference (Reform –

Non-Reform)a

Comprehensive Diabetes – HbA1C Good Control (CDC HbA1C Good)

1.5% 2.8% -1.2%

Follow-up After Hospitalization for Mental Illness – 7 Day (FMH7)

-0.2% 1.0% -1.2%

Follow-up After Hospitalization for Mental Illness – 30 Day (FMH30)

0.4% 1.5% -1.2%

Comprehensive Diabetes – HbA1C Poor Control INVERSE (CDC HbA1C Poor)

-0.1% -1.2% 1.1%

Anti-Depressant Medication Management – Effective Acute Phase Treatment (AMM Acute)

0.7% 1.6% -0.9%

Comprehensive Diabetes – HbA1C Testing (CDC HbA1C Testing)

0.2% 1.0% -0.7%

Controlling Blood Pressure – Total (CBP) -0.6% 0.1% -0.7%

Comprehensive Diabetes – LDL Screening (CDC LDLS) 0.1% 0.7% -0.6%

Comprehensive Diabetes – LDL-C Control (CDC LDLC) 0.6% 1.2% -0.6%

Comprehensive Diabetes – Nephropathy (CDC Neph) 0.4% 0.6% -0.2% aDifference is the difference in the Average Annual Change of Reform plans and Non-Reform plans. Example interpretation:

The average annual change in Reform plans’ AMM Continuation HEDIS Scores is 0.8 percentage points higher than Non-Reform plans’ HEDIS Scores. *Reform plans are improving annually at a greater rate than Non-Reform plans.

bReform plans are

improving annually at a slower rate than Non-Reform plans.

Comparative Difference Analysis

Reform plans had better scores compared to Non-Reform plans on eight out of the twelve measures used to assess quality of care. Performance was superior on almost all Comprehensive Diabetes Care scores as well as both Antidepressant Medication Management scores. (Table 17). Both Reform and Non-Reform plans had declining trends for four out of the twelve measures used to assess quality of care, but the slope of the decline for Reform plans was steeper when compared to the slope of the decline for Non-Reform plans. These measures included Follow-Up after Hospitalization for Mental Illness – 7 Day with an average annual difference between Reform and Non-Reform plans of -8.2%, and Follow-Up after Hospitalization for Mental Illness – 30 Day with an average annual difference of -8.1%. Reform plans were also outperformed by Non-Reform plans on the Controlling Blood Pressure – Total score with an average annual difference of -2.6% as well as in the Comprehensive Diabetes – HbA1C Poor Control INVERSE with an annual difference of 0.3%.

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Table 17: Domain i.b: HEDIS Data, Reform vs Non-Reform Plans, Comparative Difference Analysis Reform Trend Improves

Annually Compared to Non-Reform

2008 (Reform –

Non-Reform)

2009 (Reform –

Non-Reform)

2010 (Reform –

Non-Reform)

2011 (Reform –

Non-Reform)

2012 (Reform –

Non-Reform)

2013 (Reform –

Non-Reform)

Average Annual

Difference (Reform –

Non-Reform)

a

Anti-Depressant Medication Management – Effective Continuation Phase Treatment (AMM Continuation)

N/A -1.3% 14.5% 12.5% 9.5% 5.2% 8.1%

Anti-Depressant Medication Management – Effective Acute Phase Treatment (AMM Acute)

N/A 6.4% 9.5% 9.4% 7.0% 3.2% 7.1%

Comprehensive Diabetes – HbA1C Testing (CDC HbA1C Testing)

4.2% 5.1% 6.4% 2.3% 5.0% -0.1% 3.8%

Comprehensive Diabetes – LDL Screening (CDC LDLS)

4.4% 3.8% 5.6% 1.8% 4.5% 0.8% 3.5%

Comprehensive Diabetes – Nephropathy (CDC Neph)

2.1% 4.3% 4.8% 4.1% 4.7% 0.4% 3.4%

Comprehensive Diabetes – LDL-C Control (CDC LDLC)

-0.2% 6.1% 2.3% 4.1% 3.6% -3.0% 2.2%

Comprehensive Diabetes –Eye Exam (CDC Eye)

-0.6% 2.0% -2.9% -2.8% 4.9% 2.6% 0.5%

Comprehensive Diabetes – HbA1C Good Control (CDC Good)

0.5% 6.7% 2.8% -5.9% 2.5% -3.9% 0.4%

Reform Trend Declines Annually Compared to

Non-Reform

2008

(Reform – Non-

Reform)

2009

(Reform – Non-

Reform)

2010

(Reform – Non-

Reform)

2011

(Reform – Non-

Reform)

2012

(Reform – Non-

Reform)

2013 (Reform –

Non-Reform)

Average Annual

Difference (Reform –

Non-Reform)

a

Follow-up After Hospitalization for Mental Illness – 7 Day (FMH7)

-9.9% -7.8% 1.2% -5.3% -14.9% -12.9% -8.2%

Follow-up After Hospitalization for Mental Illness – 30 Day (FMH30)

-11.4% -5.3% -0.1% -3.6% -15.3% -12.8% -8.1%

Controlling Blood Pressure – Total (CBP)

-6.4% 4.3% 0.4% -8.1% 1.4% -7.5% -2.6%

Comprehensive Diabetes – HbA1C Poor Control INVERSE (CDC Poor)

-0.1% -4.9% -1.5% 6.2% -3.0% 4.9% 0.3%

Note. Data for AMM Continuation and AMM Acute are not available for 2008 aAverage Annual Difference is the average of the

difference between the Reform plans’ HEDIS Score and the Non-Reform Plans’ HEDIS Score for each year. Example interpretation: On average, Reform plans’ AMM Continuation Scores were 8.1 percentage points higher than Non-Reform plans’ HEDIS Scores.

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Performance Measure Action Plans (PMAPs) Data This section is a presentation of findings from the qualitative case study analysis of the 2010 – 2012 PMAP reports submitted by the largest market share health plans represented in the demonstration. PMAPs are used by health plans to manage the process of improving HEDIS measures that have fallen below certain thresholds. The analysis of the PMAPs was conducted in order to identify possible differences among and within health plans in the improvement of HEDIS measures, through the implementation of several types of activities. The selected measures were categorized by Average Annual Change results and grouped according to health plan and whether these had collective positive, mixed, or negative Average Annual Changes. Table 18 provides an overview of the plans selected and their measures grouped by whether the trends were positive, mixed, or declining over time. The specific findings for each of these categories are listed in Tables 19 – 21.

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Table 18: Initial HEDIS Measures Categorized by Average Annual Change Plan Name Measure 2010 2011 2012 Average Annual Change

Plans with Positive Trends Over Time

Sunshine CBP

N/A 44% 45% 0.96%

First Coast Advantage 50% 44% 57% 3.08%

SFCCN* 55% 53% 61% 2.98%

Sunshine CDC Eye

N/A 53% 63% 10.11%

First Coast Advantage 50% 49% 52% 1.23%

SFCCN 47% 46% 50% 1.65%

Sunshine CDC LDLC

N/A 30% 37% 7.06%

First Coast Advantage 38% 37% 41% 1.33%

SFCCN 39% 42% 41% 1.09%

Sunshine CDC Good

N/A 40% 47% 6.88%

First Coast Advantage 47% 39% 47% 0.33%

SFCCN 47% 48% 53% 2.99%

Plans with Mixed Trends Over Time

Sunshine AAP45

N/A 86% 85% -0.19%

First Coast Advantage 89% 89% 90% 0.25%

SFCCN 84% 85% 82% -1.04%

Sunshine CDC LDLS

N/A 81% 85% 3.60%

First Coast Advantage 82% 82% 83% 0.88%

SFCCN 87% 84% 85% -1.18%

Sunshine CDC NEPH

N/A 83% 78% -4.56%

First Coast Advantage 85% 84% 83% -0.77%

SFCCN 81% 81% 84% 1.52%

Sunshine CDC HbA1C Testing

N/A 83% 82% -0.70%

First Coast Advantage 81% 82% 84% 1.33%

SFCCN 85% 83% 83% -0.64%

Plans with Declining Trends Over Time

Sunshine AAP20

N/A 74% 72% -2.38%

First Coast Advantage 79% 76% 74% -2.22%

SFCCN 65% 66% 63% -1.47%

Sunshine AAP65

N/A 79% 76% -3.42%

First Coast Advantage 89% 92% 87% -0.88%

SFCCN 78% 77% 72% -3.07%

Sunshine AAP Total

N/A 77% 76% -1.84%

First Coast Advantage 84% 80% 80% -1.91%

SFCCN 78% 76% 72% -2.64%

Note. No data from Sunshine State Health Plan are available in 2010 for these measures. *South Florida Community Care Network

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Not all measures that were recorded as being present in the different health plan PMAPs were found within their reports; therefore, these were not included in the theme analysis. Only those measures presented within PMAPs of two or more health plans were analyzed in this+ case study. Table 19: Plan Themes and Activities for Measures With Positive Average Annual Change over Time

Measures Global Theme Example of Activities CBP, CDC Eye

Patient Outreach – Phone Calls

Enrollee phone calls for initial assessments and for reminders about appointments.

Enrollee phone calls about information on importance of check-ups, transport services available, and for information on incentives for keeping appointments or Comp Benefits services (CDC Eye).

CBP, CDC Eye Patient Outreach – Mail Enrollee postcards to remind them to establish a PCP.

CDC Eye Patient Outreach – Mail Enrollee birthday card with monetary incentive insert.

CBP, CDC Eye Patient Outreach – Mail Enrollee education flyer on medication management.

CBP, CDC Eye Provider Outreach – In person Provider education dinners.

CBP, CDC Eye

Provider Outreach – Mail

Enrollee/member identified with the measure specific disease list shared with providers in order to provide education or keep track of disease management.

Provider Newsletter articles.

Provider education on importance of preventive care for this category and on their comparative quality ranking.

CBP, CDC Eye Data gathering and Analysis Contract vendors for collection and analysis of supplemental data needed.

CDC Eye Data gathering and Analysis Quarterly CDC Eye rate review.

CBP, CDC Eye Community Outreach – In person Community health fairs.

CDC Good None of the health plans reported this measure on the PMAP so it was excluded from comparison analysis.

CDC LDLC Only one health plan reported this measure on the PMAP so it was excluded from comparison analysis.

As noted, reporting of mixed results was not possible as there were not any instances in which more than one health plan presented PMAPs for each of these measures. In some instances (CDC LDLS) none of the plans had it in their report and in other instances (AAP45, HBA1C Testing and CDC NEPH) only one of the health plans reported any actions regarding the measures presented, so it was excluded from comparison analysis (Table 20). Table 20: Plan Themes for Measures With Mixed Average Annual Change over Time

Measures Global Theme AAP45 Only one health plan reported this measure on the PMAP so it was excluded from comparison

analysis.

CDC HBA1C Testing Only one health plan reported this measure on the PMAP so it was excluded from comparison analysis.

CDC LDLS None of the health plans reported this measure on the PMAP so it was excluded from comparison analysis.

CDC NEPH Only one health plan reported this measure on the PMAP so it was excluded from comparison analysis.

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No substantial differences in the types of activities were found among the health plan actions of the measures that were positive when compared to those that were negative. A very similar pattern was found and similar activities were presented throughout when looking at the specific health plans (Table 19, Table 21). Table 21: Plan Themes and Activities for Measures With Negative Average Annual Change over Time

Measures Global Theme Examples of Activities AAP20, AAP Total

Patient Outreach – Phone Calls

Enrollee phone calls for initial assessments and for reminders about appointments.

Enrollee phone calls about information on importance of check-ups, transport services available, and for information on incentives for keeping appointments.

AAP20 Patient Outreach – Mail Enrollee postcards to remind them to establish a PCP.

AAP Total Patient Outreach – Mail Enrollee birthday card with monetary incentive insert.

AAP20 Provider Outreach – In person Provider education dinners.

AAP20 Provider Outreach – Mail Quarterly member lists shared with providers.

AAP Total Provider Outreach – Mail Enrollee/member list shared with providers in order to keep track of noncompliance.

AAP20 Provider Outreach – Mail Provider Newsletter articles.

AAP20, AAP Total Provider Outreach – Mail Provider education on importance of preventive care for this category and on their comparative quality ranking.

AAP20, AAP Total Data gathering and Analysis Contract vendors for collection and analysis of supplemental data needed.

AAP Total Data gathering and Analysis Quarterly AAP rate review.

AAP20 Community Outreach – In person Community health fairs.

AAP65 Only one health plan reported this measure on the PMAP so it was excluded from comparison analysis

Summary: Domain i.b Enrollee self-reports indicate that, in several measures, quality of care improved during the demonstration period. For instance, trends over time showed an increase in the proportion of respondents in Broward and Duval counties reporting the highest level rating (Level 3) for their personal doctors, specialists, and health plans. In rural counties, there was an increase in the proportion of respondents reporting the highest rating level with regard to their health care in the last 6 months and their health plans. All these increases were statistically significant. Changes in the proportion of respondents providing a Level 3 rating for overall care in both Reform and Non-Reform counties were not statistically significant. Based on the trend analysis of HEDIS measures, Reform plans improved quality of care throughout the demonstration. The main areas of improvement were with Comprehensive Diabetes Care and Antidepressant Medication Management measures. Trends were not improving in the Controlling Blood Pressure (CBP) and Follow-up After Hospitalization for Mental Illness – 7 Day measure (FMH7). However, the decline in performance in these measures was minimal and less than one percentage point. The comparative difference analysis demonstrates that Reform plans performed better than Non-Reform plans on almost all Comprehensive Diabetes measures, with the exception of Comprehensive Diabetes – HbA1C Poor Control INVERSE (CDC Poor). However, the mental health

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service measures were split in terms of better performance with Reform plans improving in Antidepressant Medication Management (AMM Continuation and AMM Acute) and declining in Follow-Up after Hospitalization for Mental Illness (FHM7 and FHM 30). Based on the comparative PMAP qualitative case study analysis, the three largest market share health plans had similar action steps reported on their PMAPs. Therefore, specific positive or negative inferences could not be made from the qualitative analysis. The case study of health plan measures with negative and positive Average Annual Changes indicates more similarities than differences in the actions taken to improve performance of these measures by the different health plans.

Domain i.c: How has the demonstration increased timeliness of services?

CAHPS Survey Data

Counties Over Time Table 22 provides percentages for Broward and Duval separate from the rural counties for two CAHPS survey measures in each of the seven survey years (statistical comparisons were calculated only for DY2 through DY8). While positive and negative changes are observed year to year, the primary objective was to examine the trend over the full six-year period. The coefficient shown in the table indicates the direction of the overall trend (upward or downward, depending on its sign), as well as the level of statistical significance. If the coefficient is not statistically significant, the change across time is not statistically significant, though it may have practical significance. In Broward and Duval counties, trends over time (DY2 – DY8) showed a statistically significant increase (p<.05) in the percentage of enrollees who reported “Always” getting needed care right away or as soon as they wanted and getting a doctor’s appointment as soon as they needed (Table 22). In the rural counties, trends over time (DY2 – DY7) showed that there was not a significant change in the percentage of enrollees who always got care right away or as soon as they wanted (Table 22).

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Table 22: Domain i.c: CAHPS Survey Data, Counties Over Time, DY1 – DY8

Domain i.c: How has the demonstration increased timeliness of services?

Questions DY1a DY2

b DY3 DY4 DY6 DY7 DY8 Coefficient Trend

In the last 6 months, when you needed care right away, how often did you get care as soon as you needed? [Always]

d

Broward/ Duval*

67.1% 72.6% 74.5% 72.0% 73.8% 77.9% 80.9% 0.0864 ↑*

Rural N/A 76.4% 74.5% 74.8% 70.1% 83.7% 83.0% 0.09720 ↔c

In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor’s office or clinic as soon as you needed? [Always]

d

Broward/ Duval*

62.6% 66.7% 69.0% 68.5% 66.4% 69.8% 68.2% 0.0352 ↑*

Rural N/A 71.7% 75.9% 73.3% 57.4% 76.1% 83.9% 0.0212 ↔c

Note. Statistical tests are based on weighted data. *Trend was statistically significant at <0.05. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

cThe average annual change was not statistically significant, so there was no change across the years.

dWording of question is

different than in previous years.

Reform vs. Non-Reform Counties In Reform counties, there was a statistically significant (p<.05) increase over time in the percentage of enrollees who reported “Always” getting urgent care right away and getting an appointment as soon as they needed (Table 23). In Non-Reform counties from DY7 to DY8, there was also an increase in the percentage of enrollees who reported “Always” getting urgent care right away and non-urgent care as soon as they wanted, through the trends were not statistically significant (Table 23). Table 23: Domain i.c: CAHPS Survey Data, Reform vs. Non-Reform, DY6 – DY8

Domain i.c: How has the demonstration increased timeliness of services?

Questions DY6 DY7 DY8 Coefficient Trend In the last 6 months, when you needed care right away, how often did you get care as soon as you needed? [Always]

a

Reform* 74.0% 74.6% 82.2% 0.2536 ↑*

Non-Reform 73.5% 70.9% 74.1% -0.0806 ↔b

In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor’s office or clinic as soon as you needed? [Always]

a

Reform* 65.6% 70.3% 69.4% 0.1572 ↑*

Non-Reform 65.6% 64.0% 68.8% 0.0263 ↔b

Note. Statistical tests are based on weighted data. *Trend was statistically significant at <0.05. aWording of question is different than in previous years.

bThe average annual change was not statistically

significant, so there was no change across the years.

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Summary: Domain i.c In both the urban and rural Reform counties, the trends appear to indicate improvement in reports of being able to access urgent and non-urgent care in a timely manner, though only the changes in Broward/Duval were statistically significant. There was also a statistically significant increase in timely access to care, both urgent and non-urgent, in the Reform counties.

Domain i.d: How has the demonstration affected growth of Medicaid costs?

Claims, Eligibility, and Encounter Data: Fiscal Analyses

Univariate Analyses MEG #1 Enrollees

As can been seen in Table 24, average PMPM expenditures for MEG #1 enrollees in Reform counties decreased over the study period, with an exception coming in SFY1011. SFY1011 had a sharp increase compared to previous years. However, after that year, the downward trend returned. Average PMPM expenditures in Control counties increased over the first three fiscal years (SFY0708 – SFY0910) but have steadily declined since then. However, the expenditures in the last year, SFY1213, are greater than that of the first year in the study (SFY0708). When comparing Reform counties to Control counties, differences in average PMPM expenditures have fluctuated over time in absolute value. However, while Reform counties expenditures were initially higher, the difference has decreased over time to the final year, where Reform county expenditures fell below Control counties by $143.84. (Table 24) MEG #2 Enrollees

For MEG #2 enrollees in Reform counties, PMPM expenditures decreased over the study period (SFY0708 – SFY1213) with the exception of SFY1011. SFY1011 shows an isolated increase in expenditures compared to previous years. However, the declining trend returns in SFY1112 and continues to SFY1213. PMPM expenditures in Control counties increased over the entire study period (SFY0708 – SFY1213). Compared to the Control counties, the Reform counties have had lower average PMPM expenditures for the last two fiscal years of the study period (SFY1112 – SFY1213). (Table 24) Table 24: Average PMPM Expenditures for All Enrollees

Mean PMPM Total Expenditures in Dollars, SFY0708 – SFY1213

Reform Control Difference

Broward/Duval Hillsborough/Orange (Reform – Control)

State Fiscal Year MEG #1 MEG #2 MEG #1 MEG #2 MEG #1 MEG #2 SFY0708 930.99 122.45 696.52 102.80 234.47 19.65

SFY0809 915.62 116.53 807.16 113.15 108.46 3.38

SFY0910 882.31 114.52 918.78 123.62 -36.47 -9.10

SFY1011 942.58 126.17 860.47 124.24 82.11 1.93

SFY1112 833.79 115.88 823.72 128.31 10.07 -12.43

SFY1213 593.71 86.94 737.55 128.77 -143.84 -41.83

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Multivariate Analyses

The use of multivariate analysis controls for the impact of differences in socio-demographic factors such as age, gender, and race between the Reform and Control counties on enrollee expenditures. Thus, the overall effect of Reform on expenditures for MEG #1 and MEG #2 enrollees can be more precisely assessed. As illustrated in equation (2) in the multivariate analyses data and methods description in Appendix F, the multivariate analysis generated three coefficients relevant to assess the impact of Reform in both the first equation examining the odds of having any expenditures and the second equation that assessed total expenditures given that expenditures were greater than zero. These coefficients are those associated with “Month,” “Reform,” and “Reform*Month.” As noted earlier, coefficients can be interpreted as the percent change in the odds of having any expenditures or the percent change in total PMPM expenditures. The coefficient “Month” indicates the overall time trend for the study period (SFY00708 – SFY1213) and represents the overall time trend in the Reform and Control counties combined. The coefficient “Reform” is the shift in the intercept associated with Reform or the average difference in the odds of either having any expenditures or total expenditures associated with Reform counties during the Reform period (SYF0708 – SFY1213). The coefficient “Reform*Month” indicates the difference in the time trend between the Reform and Control counties. A negative coefficient means that Reform is associated with lower odds of having any expenditures or lower total expenditures over the study period compared to the Control counties. Conversely, a positive coefficient indicates an increase in the odds of having any expenditures or higher total expenditures over the study period due to Reform. MEG #1 Enrollees

Table 25 shows the results for the odds of having any expenditures for MEG #1 enrollees. The coefficient for Month indicates that the odds of having any expenditures increased by 0.5% each month for all enrollees in both the Reform and Control counties over the course of the study period. The coefficient for the Reform indicator variable shows Reform enrollees have 4.509 the odds (greater odds) of having any expenditures compared to Control enrollees. This means the odds of having any expenditures were 350% greater among Reform enrollees. However, the coefficient for the interaction of Reform and Month shows that the odds of Reform enrollees having any expenditures were .9691 the odds of Control enrollees. This means the odds of having any expenditures were further reduced by 3.1% each additional month in the Reform counties relative to the Control counties.

Table 25: Probability of Having any Expenditures for MEG #1 Enrollees Coefficient 95% CI P-value

a

Month 1.0051 1.0050, 1.0052 <.0001

Reform 4.5088 4.4186, 4.6008 <.0001

Reform*Month 0.9691 0.9689, 0.9693 <.0001

Note. Model controls for age, race, and gender. aThe F-test addresses the null hypothesis that expenditures are the same in the Control and Reform counties. We use a

significance level of 0.05; therefore, P-values (Pr>F) that are less than 0.05 lead to a rejection of the null hypothesis in favor of the alternative hypothesis, which is that expenditures are different in the Control and Reform counties.

Next, the association of Reform with total expenditures among those person-months where some spending occurred was examined (Table 26). These results show that expenditures increased by 0.22% each month among all enrollees in both the Reform and Control counties. This is represented by the coefficient for Month. The coefficient for Reform shows that average expenditures were 22.94% greater

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for enrollees in the Reform counties over the study period. However, when considering the effect of time, Reform county expenditures are increasing by 0.21 percentage points less each month compared to Control counties. This differential trend is represented by the coefficient for Reform*Month. Thus, although average expenditures are greater in the Reform counties over the course of the study period, the slower rate of increase in expenditures over time in the Reform counties suggests that savings will be achieved over time. Table 26: Total Expenditures for MEG #1 Enrollees

Coefficient 95% CI P-value a

Month 0.0022 0.0020, 0.0023 <.0001

Reform 0.2294 0.2072, 0.2520 <.0001

Reform*Month -0.0021 -0.0023,-0.0019 <.0001

Note. Model controls for age, race, and gender. aThe F-test addresses the null hypothesis that expenditures are the same in the Control and Reform counties. We use a

significance level of 0.05; therefore, P-values (Pr>F) that are less than 0.05 lead to a rejection of the null hypothesis in favor of the alternative hypothesis, which is that expenditures are different in the Control and Reform counties.

To simulate the potential reduction in expenditures, we used the models to predict what expenditures would have been in the Control counties if the Control counties experienced the same trend in expenditures as the Reform counties (Table 27). Adjusted PMPM expenditures were an average of $684.93 in the Control counties; however, if Control county PMPM expenditures had followed the same trend as the Reform counties, PMPM expenditures would have been $825.93. While these predictions indicate a negative projected savings from the Reform ($141.15), assuming a continuation of the trends identified in the previous two models, the projected savings from the Reform could potentially become positive in future years.

Table 27: Projected Savings from Reform for MEG #1 Enrollees PMPM, in Dollars

Control Expenditures 684.93

Reform Trend Expenditures 825.93

Projected Savings from Reform -141.15

Note. Model controls for age, race, and gender.

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MEG #2 Enrollees

Table 28 shows the results for the odds of having any expenditures for MEG #2 enrollees. The coefficient for Month indicates that the odds of having any expenditures increased by 1.45% each month for all enrollees in both the Reform and Control counties over the course of the study period. The coefficient for the Reform indicator variable shows that the odds of having any expenditures were 321% greater among Reform enrollees. However, the coefficient for the interaction of Reform and Month shows that the odds of having any expenditures were .9670 the odds of Control enrollees. This means the odds of having any expenditures was reduced by 3.3% each additional month in the Reform counties relative to the Control counties.

Table 28: Probability of Having any Expenditures for MEG #2 Enrollees Coefficient 95% CI P-value

a

Month 1.0145 1.0145, 1.0146 <.0001

Reform 4.2122 4.1872, 4.2372 <.0001

Reform*Month 0.9670 0.9670, 0.9671 <.0001

Note. Model controls for age, race, and gender. aThe F-test addresses the null hypothesis that expenditures are the same in the Control and Reform counties. We use a

significance level of 0.05; therefore, P-values (Pr>F) that are less than 0.05 lead to a rejection of the null hypothesis in favor of the alternative hypothesis, which is that expenditures are different in the Control and Reform counties.

Next, the association of Reform with total expenditures among those person-months where some spending occurred was examined (Table 29). The coefficient for Month shows that average expenditures were 0.05% greater for each month for both Reform and Control counties. The coefficient for Reform shows that average expenditures were 6.1% lower for enrollees in the Reform counties, but that the trend in expenditures was greater in the Reform counties compared to the Control counties, with expenditures increasing by 0.15 percentage points each month in the Reform counties relative to Control counties. This differential trend is represented by the coefficient for Reform*Month. Table 29: Total Expenditures for MEG #2 Enrollees

Coefficient 95% CI P-valuea Month 0.0005 0.0004, 0.0007 <.0001

Reform -0.0608 -0.0685, -0.0531 <.0001

Reform*Month 0.0015 0.0013, 0.0016 <.0001

Note. Model controls for age, race, and gender. aThe F-test addresses the null hypothesis that expenditures are the same in the Control and Reform counties. We use a

significance level of 0.05; therefore, P-values (Pr>F) that are less than 0.05 lead to a rejection of the null hypothesis in favor of the alternative hypothesis, which is that expenditures are different in the Control and Reform counties.

As with the model for MEG #1, to simulate the potential reduction in expenditures, we used the model to predict what expenditures would have been in the Control counties if the Control counties experienced the same trend in expenditures as the Reform counties (Table 30). Adjusted PMPM expenditures were an average of $98.79 in the Control counties, but if PMPM expenditures had followed the same trend in the Control counties as the Reform counties, PMPM expenditures would have been $99.81, representing a “negative savings” of ($1.02) PMPM. Because Reform counties appear to have a rate of increase in expenditures greater than Control counties over time, these findings suggest positive savings should not be expected in the future for MEG #2 Reform enrollees.

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Table 30: Projected Savings from Reform for MEG #2 Enrollees PMPM, in Dollars

Control Expenditures 98.79

Reform Trend Expenditures 99.81

Projected Savings from Reform -1.02

Note. Model controls for age, race, and gender.

Summary: Domain i.d The primary goal of the fiscal analysis was to assess the impact of the demonstration on PMPM Medicaid expenditures. To accomplish that goal, the average PMPM expenditures for Reform counties were compared to those of Control counties for SFY0708 through SFY1213. Comparisons to Control counties were made to account for the possibility that other factors may have influenced the time trend in PMPM expenditures across Florida’s Medicaid program. Specifically, the average expenditures were calculated separately for enrollees in the urban demonstration counties (Broward and Duval) and the Control counties (Orange and Hillsborough). Additionally, because the medical needs of the Medicaid population varied significantly based on eligibility category, PMPM expenditures were calculated separately for enrollees eligible through SSI and enrollees eligible through TANF. Because there were differences by county in the mix of age, race, and gender of Medicaid enrollees which may affect PMPM expenditures independent of the demonstration, multivariate regression analyses were conducted to control for these enrollee characteristics. Overall, a total of 14,615,650 person-months of observations were used in the calculations of PMPM expenditures for the demonstration counties and 19,176,028 person-months of observations were used in the Control county calculations. These calculations excluded all individuals in home and community-based waiver programs, those who received services through the SIPP program, or those who were eligible for Medicaid through a means other than TANF or SSI.

Overall, it appears that PMPM expenditures were greater in the Reform counties compared to the Control counties for SSI enrollees, but the rate of growth in expenditures was lower in the Reform counties relative to the Control counties, suggesting that the Reform counties will achieve savings over time if the current trend continues in the future for SSI enrollees. However, for TANF enrollees, PMPM expenditures were less for Reform counties compared to Control counties, but the rate of growth is greater in Reform counties relative to the Control counties. This suggests Reform counties will not achieve savings for TANF enrollees over time if the current trend continues in the future. These results should be interpreted with extreme caution. As mentioned earlier, there are significant concerns regarding the Medicaid claims and eligibility data given that both the total number of Medicaid enrollees and the expenditure amounts displayed significant differences between data extracted for this report vs. the data extracted for a prior report for the period that overlapped (SFY0607 – SFY0910). Results for SSI enrollees are generally consistent with our previous findings which show that the expenditures for the first five fiscal years of Reform (the overlapping time period) were increasing at a lower rate in the Reform counties relative to the Non-Reform counties. However, we now see a change in direction for TANF enrollees. It is possible this change is due to the new exclusion method employed in this analysis per AHCA’s request which removed any Reform county observations with a plan type code in the eligibility file equal to MediPass. However, since it is unknown whether making this change is reflective of actual cohort membership, we are unable to draw conclusions about the validity of the results.

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General Findings: Domain ii

The basic analytic strategy for this section was a comparison of the changes in plan limits, co-pays, and plan benefits for all plans in 2007 and all plans in 2013. A sub-analysis for only those plans that were in operation in all four years (highlighted plans in Table 31 were operational both years) was also completed. For this section, data was derived from Reform health plan benefit charts and other AHCA reports (quarterly and annual demonstration reports submitted to CMS).

Domain ii.a: To what extent do health plans offer customized benefits ? How much variation is there between plans’ benefit packages ? Are there plans whose customized benefits are geared to particular populations? Domain ii.b: When presented the opportunity, do plans provide additional services not previously covered by Medicaid? If so, what types of services? To what extent do enrollees use these additional services?

Plan Benefits

Plan Participation For purposes of this section, health plans that were located in more than one county were counted as one plan. As seen in Table 31, sixteen plans were operational in the Reform counties in 2007, and in 2013, there were fifteen plans operating in the Reform counties. For the 2007, 2011, 2012, and 2013 period, the majority of plans were operational in Broward County; plans operating in the remaining Reform counties were limited. Of the plans participating in 2013, four were PSNs and the remaining eleven were HMOs. The plans observed during the 2007, 2011, 2012, and 2013 period consisted of three PSNs and two HMOs. There were two specialty plans in the demonstration in the 2007, 2011, 2012, and 2013 period: Positive Health Care and Children’s Medical Services. Positive Health Care is an HMO that became operational in Broward County in 2010, and serves only individuals with HIV or AIDS and their household members. Children’s Medical Services is a PSN that provides specialized pediatric-only plans (POPs) for children with special needs (those with chronic conditions as determined by a clinical screening process, children alleged to have been abused or neglected, and children who are developmentally behind their peers).

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Table 31: Plan Participation by County and Year Plan Name Baker Broward Clay Duval Nassau Type Broward &

Duval First Enrollment

2007 2011 2012 2013

Access Health Solutions X X X X X PSN 09/01/06 X

Amerigroup Florida, Inc. X HMO 09/01/06 X

Better Health X PSN 05/01/10 X X X

Buena Vista X HMO 07/01/06 X

CareFlorida X HMO 02/01/11 X X X

Children’s Medical Services – Broward

X PSN 12/01/06 X X X X

Children’s Medical Services – Duval

X PSN 05/01/07 X X X

Clear Health Alliance X HMO 04/01/13 X

First Coast Advantage – Duval

X PSN 09/01/06 X X X X

First Coast Advantage – Baker/Clay/Nassau

X X X PSN 12/1/10 X X X

Florida NetPASS X PSN 09/01/06 X

Freedom Health Plan X HMO 12/01/07 X X X

HealthEase X X HMO 09/01/06 X

Humana X HMO 09/01/06 X X X X

Magellan Complete Care X HMO 07/01/13 X

Medica Health Plans of Florida

X HMO 12/01/09 X X X

Molina Healthcare X HMO 05/01/09 X X X

Pediatric Associates X PSN 10/01/06 X

Positive Health Care X HMO 05/01/10 X X X

Preferred Medical Plan, Inc. X HMO 09/01/06 X

South Florida Community Care Network

X PSN 09/01/06 X X X X

Staywell X X HMO 09/01/06 X

Staywell Health Plan of Florida

X HMO 01/01/13 X

Sunshine State Health Plan – Broward

X HMO 08/01/09 X X X

Sunshine State Health Plan – Clay/Duval

X X HMO 08/01/09 X X X

Total Health Choice, Inc. X HMO 09/01/06 X

United Healthcare – Baker/Clay/Nassau/Duval

X X X X HMO 09/01/06 X X X X

United Healthcare – Broward

X HMO 07/01/06 X

Universal Health Care X X HMO 01/01/07 X X X

Vista Health Plan of S. Florida, Inc.

X HMO 09/01/06 X

Vista Health Plan: Buena Vista

X HMO 09/01/06 X

Note. Adapted from AHCA Reform Year 7 Annual Report. Plans highlighted were operational throughout the demonstration for the 2007, 2011, 2012, and 2013 time period.

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Reform Market Share There were five plans that were operational during the 2007, 2011, 2012, and 2013 period. Tables 32 through 34 compare market share for those five plans. Each table is broken down by county. In Broward County, three plans were available in the 2007, 2011, 2012, and 2013 period: one HMO, Humana Family, and two PSNs, Children’s Medical Services and South Florida Community Care Network (SFCCN). Average market share percentage for Humana Family decreased from 8.1% in 2007 to 6.4% in 2013. However, from 2012 to 2013, Humana’s average market share increased by approximately 3%. For the PSNs, the average market share for Children’s Medical Services and SFCCN increased between the 2007, 2011, 2012, and 2013 period. However, from 2012 to 2013, SFCCN saw a two percent decrease in average market share. During the time period, the minimum monthly market share percentage for Humana Family was 2.3% in April 2012. The highest monthly market share Humana Family experienced was 10.1% in March of 2007. Both PSNs had the smallest monthly market share percentages in 2007. The lowest monthly market share for Children’s Medical Services was 1.1% during the last three months of 2007, and the highest was 5.2% in March 2012. The smallest monthly market share percentage SFCCN experienced was 3.2% in December 2007. The largest monthly market share percentage SFCCN saw was 33.7%, which occurred in March of 2012. Table 32: Medicaid Reform Market Share, Broward County, Plans Existent in both 2007 and 2013

Broward County Plans

Type Year Average Market

Share

Minimum Monthly Market

Share

Maximum Monthly Market

Share Humana Family HMO 2007 8.1% 5.1% 10.1%

2011 3.3% 2.6% 4.2%

2012 3.5% 2.3% 5.2%

2013 6.4% 5.5% 6.9%

Children’s Medical Services

PSN 2007 1.7% 1.1% 2.5%

2011 3.2% 3.1% 3.3%

2012 3.4% 1.6% 5.2%

2013 3.5% 3.4% 3.8%

South Florida Community Care Network

PSN 2007 5.9% 3.2% 9.2%

2011 22.3% 22.0% 22.6%

2012 22.3% 20.5% 33.7%

2013 20.1% 19.6% 21.1%

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In Duval County, as seen in Table 33, two plans were operational in the 2007, 2011, 2012, and 2013 period: one HMO, United Healthcare, and one PSN, First Coast Advantage. The average market share percentage for United Healthcare decreased from 8.8% in 2007 to 2.9% in 2013. The minimum monthly market share percentage for United Healthcare was 1.8% which occurred in August, September, and October of 2011. The highest monthly market share United Healthcare experienced was 11.4% in August of 2007. For the PSN, First Coast Advantage, the average market share increased from 18% in 2007 to approximately 50% in 2013. Moreover, between 2011 and 2013, First Coast’s average market share remained fairly stable around 49%. The minimum monthly market share percentage for First Coast was 7.8% in December 2007. The maximum monthly market share percentage for First Coast was 52.6% in April 2013.

Table 33: Medicaid Reform Market Share, Duval County, Plans Existent in both 2007 and 2013 Duval County

Plans Type Year

Average Market Share

Minimum Market Share

Maximum Market Share

United Healthcare HMO 2007 8.8% 4.5% 11.4%

2011 2.2% 1.8% 3.3%

2012 3.4% 2.6% 4.2%

2013 2.9% 2.5% 3.7%

First Coast Advantage

PSN 2007 18.0% 7.8% 27.7%

2011 47.9% 45.4% 49.7%

2012 50.2% 49.1% 50.9%

2013 49.9% 49.0% 52.6%

Baker, Clay, and Nassau do not have market share numbers for January through August of 2007, because, although plans became operational in 2007, beneficiaries were not enrolled until later that year (Florida Agency for Health Care Administration, 2008). However, in Baker, Clay, and Nassau Counties, as seen in Table 34, one plan was operational in the 2007, 2011, 2012, and 2013 period: an HMO, United Healthcare. The average market share percentage for United Healthcare increased significantly in all counties in the 2007, 2011, 2012, and 2013 period. The minimum monthly market share percentage for United Healthcare in all three counties was less than one percent in 2007. The highest monthly market share United Healthcare experienced each of the counties was 31.3% in Baker County in February 2011, 28.2% in Clay County in May 2011, and 36.7% in Nassau County in March of 2011. Table 34: Medicaid Reform Market Share, Baker County, Clay County, and Nassau County, Plan Existent in both 2007 and 2013

Baker County Clay County Nassau County

Plan Type Year Average Market Share

Min Market Share

Max Market Share

Average Market Share

Min Market Share

Max Market Share

Average Market Share

Min Market Share

Max Market Share

United Healthcare

HMO 2007 0.2% 0.1% 0.3% 0.8% 0.3% 1.1% 0.3% 0.1% 0.4%

2011 30.1% 28.2% 31.3% 24.3% 17.9% 28.2% 33.3% 29.8% 36.7%

2012 24.0% 18.2% 29.1% 17.7% 13.9% 22.1% 27.4% 26.0% 29.7%

2013 19.0% 17.5% 21.2% 21.4% 20.8% 21.9% 25.0% 23.3% 26.0%

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Cost Sharing All plan comparison 2007, 2011, 2012, and 2013

The number of plans requiring copays decreased in the 2007, 2011, 2012, and 2013 period (Table 35). In 2013, there were two HMOs and one PSN that required copays for certain standard services for either the aged and/or disabled eligibility group (eligibility through SSI) or the children and families eligibility group (eligibility through TANF). Table 35: Number of Plans Requiring Copays, 2007, 2011, 2012, and 2013

2007 2011 2012 2013 9 6 7 3

Note. Adapted from the AHCA Reform Year 2 Annual Report and the AHCA Reform Year 7 Annual Report.

Plan Comparison for Plans That Were in Operation in 2007 – 2013

Tables 36 and 37 provide a summary of the number of plans by population eligibility groups that required copays for specific standard services for plans operational in 2007, 2011, 2012, and 2013. Three of the five plans operational in the 2007, 2011, 2012, and 2013 period required copays for certain services. Two of the PSNs, Children’s Medical Services and First Coast Advantage – Duval did not require any copays for any standard service during the time period. Approximately fourteen of the twenty-five standard services required copays by at least one of the three plans. The plans that required copays included two HMOs, Humana Family – Broward and United Healthcare – Baker, Clay, Nassau, & Duval, and one PSN, the South Florida Community Care Network – Broward. For both the SSI and TANF eligibility groups, the PSN required copays for the services listed in Tables 36 and 37 in all years except non-emergency transportation services. As shown in Tables 36 and 37, in 2007, one HMO required a copayment for only one of the 14 listed services for the SSI eligibility group. For the TANF group, a copayment was required by at least one of the HMOs for seven of the 14 listed services. For both eligibility groups, the PSN plan required a copay for clinic services, primary care physician/ARNP/PA services, and specialty physician services across all years but the HMO plans did not. The only service in which an HMO plan required a copay but the PSN did not was for non-emergency transportation. For the SSI eligibility group, from 2011 to 2013, both HMOs required copays for hospital inpatient and mental health services. For the TANF eligibility group, both HMO plans required copays for chiropractor services, home health services, hospital inpatient services, and podiatrist services in 2011, 2012, and 2013.

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Table 36: Number of Plans that Required Copays for Specific Services for the Aged and/or Disabled (SSI) by Plan Type and Year HMOs (n=2 ) PSNs (n=1)

Service 2007 2011 2012 2013 2007 2011 2012 2013 Chiropractor 0 1 1 1 1 1 1 1

Clinic (FQHC/RHC) 0 0 0 0 1 1 1 1

Dental Services 0 1 1 1 1 1 1 1

Home Health Services 0 1 1 1 1 1 1 1

Hospital Inpatient 0 2 2 2 1 1 1 1

Hospital Outpatient Surgery 0 1 1 1 1 1 1 1

Lab/X-ray 0 1 1 1 1 1 1 1

Mental Health Services 1 2 2 2 1 1 1 1

Outpatient Hospital Services (non-emergency) 0 1 1 1 1 1 1 1

Podiatrist 0 1 1 1 1 1 1 1

Primary Care Physician/ARNP/PA 0 0 0 0 1 1 1 1

Specialty Physician 0 0 0 0 1 1 1 1

Transportation (non-emergency) 0 1 1 1 0 0 0 0

Vision 0 1 1 1 1 1 1 1

HMOs: Humana Family – Broward and United Healthcare – Baker, Clay, Nassau, & Duval. PSN: South Florida Community Care Network – Broward

Table 37: Number of Plans that Required Copays for Specific Services for Children and Families (TANF) by Plan Type and Year

HMOs (n=2 ) PSNs (n=1)

Service 2007 2011 2012 2013 2007 2011 2012 2013 Chiropractor 0 2 2 2 1 1 1 1

Clinic (FQHC/RHC) 0 0 0 0 1 1 1 1

Dental Services 0 1 1 1 1 1 1 1

Home Health Services 0 2 2 2 1 1 1 1

Hospital Inpatient 1 2 2 2 1 1 1 1

Hospital Outpatient Surgery 1 1 1 1 1 1 1 1

Lab/X-ray 1 1 1 1 1 1 1 1

Mental Health Services 1 1 1 1 1 1 1 1

Outpatient Hospital Services (non-emergency) 1 1 1 1 1 1 1 1

Podiatrist 0 2 2 2 1 1 1 1

Primary Care Physician/ARNP/PA 0 0 0 0 1 1 1 1

Specialty Physician 1 0 0 0 1 1 1 1

Transportation (non-emergency) 1 1 1 1 0 0 0 0

Vision 0 1 1 1 1 1 1 1

Plan Limits All Plan Comparison 2007, 2011, 2012, and 2013

Plan limits did not vary greatly between 2007 and 2011, 2012, or 2013. Nor did they vary greatly between plans (Table 38). Many standard services were covered completely with notation that prior authorizations and limits/exceptions may apply; these services have been identified [in Table 38] by a “^”. A few services, such as durable medical equipment and home health services, varied across plans, but the differences were generally not large. For example, in 2007 most plans capped chiropractor services at 24 visits per year, but one plan opted to cap chiropractor services at 35 visits per year. Plans that made changes to limits between 2007 and 2013 only made slight changes, and the changes tended to be more restrictive limits in 2013 than in 2007. An example of this is the plan limit for vision services

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that was chosen by a majority of the plans. In 2007, vision services included two pairs of glasses, but in 2013 beneficiaries were limited to a single pair of glasses every two years and an eyeglass lens replacement every year, which reflects a legislative policy change that took effect through the Medicaid State Plan in 2010.

Table 38: Changes in Plan Limits, All Plan Comparison 2007 vs. 2011, 2012, and 2013

Note. ^standard services covered (prior authorizations and limits/exceptions may apply), * limits the same across plans.

Standard Services 2007 2011 2012 2013 Ambulance ^ ^ ^ ^

Ambulatory Surgery ^ ^ ^ ^

Chemotherapy Services ^ ^ ^ ^

Chiropractor Variation in limits Variation in limits Variation in limits Variation in limits

Clinic (FQHC, RHC) * * * *

Dental Services * Variation in limits Variation in limits Variation in limits

Dialysis Services ^ ^ ^ ^

Durable Medical Equipment

Variation in limits; limits do not apply to orthotics and prosthetics over $3,000 and motorized wheelchairs

Variation in limits; limits do not apply to orthotics and prosthetics over $3,000 and motorized wheelchairs

Variation in limits; limits do not apply to orthotics and prosthetics over $3,000 and motorized wheelchairs

Variation in limits; limits do not apply to orthotics and prosthetics over $3,000 and motorized wheelchairs

Emergency Room ^ ^ ^ ^

Hearing Services * Variation in limits Variation in limits Variation in limits

Home Health Services Variation in limits Variation in limits Variation in limits Variation in limits

Hospital Inpatient ^ ^ * *

Hospital Outpatient Surgery

^ ^ ^ ^

Lab/X-ray ^ ^ ^ ^

Maternity/Family Planning Services

^ ^ ^ ^

Mental Health Services Same limits as Medicaid fee for service program

Same limits as Medicaid fee-for-service program

*Same limits as Medicaid fee for service program

*Same limits as with Medicaid fee for service program

Outpatient Hospital Services (non-emergency)

* Variation in limits * *

Outpatient Therapy (physical/respiratory)

Variation in limits Variation in limits * *

Pharmacy Variation in limits; limits do not apply to chemotherapy or HIV/AIDS drugs

Variation in limits; limits do not apply to chemotherapy or HIV/AIDS drugs

Variation in limits; limits do not apply to chemotherapy or HIV/AIDS drugs

Variation in limits; limits do not apply to chemotherapy or HIV/AIDS drugs

Podiatrist Variation in limits Variation in limits Variation in limits Variation in limits

Primary Care Physician/ARNP/PA

* * * Variation in limits

Specialty Physician * * * Variation in limits

Transplant Services ^ ^ ^ ^

Transportation Non-emergency

^ ^ ^ ^

Vision Services ^ Variation in limits Variation in limits Variation in limits; 1 pair of eyeglass frames/2 years; eyeglass lens replacement/year

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Plan Comparison for Plans That Were in Operation in 2007, 2011, 2012, and 2013

Table 39 provides a summary of the number of plans that had changes in plan limits for specific standard services for plans operational in the 2007, 2011, 2012, and 2013 period. Four of the five plans operational in the period experienced changes in limits with the majority of plans changing limits related to pharmacy and vision services. Children’s Medical Services did not have any changes to the limits for services during the time period. Approximately eleven of the twenty-five standard services saw changes to the plan limits by at least one of the four plans. The plans that had changes to limits for services included the two HMOs, Humana Family – Broward and United Healthcare – Baker, Clay, Nassau, & Duval, and two PSNs, First Coast Advantage – Duval and the South Florida Community Care Network – Broward. Overall, PSN plans had fewer changes to service limits across the 2007, 2011, 2012, and 2013 period. Both PSNs had changes to limits for vision services and pharmacy services in all years for both the SSI and TANF eligibility groups. In 2011 and 2012, one PSN, the SFCCN, changed plan limits for dental services but only for the SSI eligibility group. One PSN, First Coast Advantage – Duval had changes to limits for home health services in all three years for the TANF eligibility group only. Both PSNs had changes to plan limits for primary care services in 2013; one changed limits for both eligibility groups, and one changed limits only for the SSI group. One of the two HMOs changed limits for dental services, pharmacy, and vision services in 2011, 2012, and 2013, for both eligibility groups with the exception of one plan in 2012, which changed vision limits only for the SSI group that year. Moreover, at least one of the two HMOs changed limits for chiropractor, durable medical equipment, hearing, home health, and podiatrist services for both the SSI and TANF groups in 2011, 2012, and 2013. None of the HMOs made changes to plan limits for primary care services during the time period. For outpatient hospital services and outpatient therapy services, none of the HMOs changed plan limits for any group in 2011 and only one HMO changed plan limits for both eligibility groups in 2012 and 2013. Table 39: Number of Plans that Changed Limits for Specific Services by Plan Type for Aged and/or Disabled (SSI) and Children and Families (TANF) Eligibility Groups

HMOs (n=2 ) PSNs (n=2*)

Service 2011 2012 2013 2011 2012 2013 Chiropractor 1 1 1 0 0 0

Dental Services 2 2 2 1a 1

a 0

Durable Medical Equipment 1 1 1 0 0 0

Hearing 1 1 1 0 0 0

Home Health 1 1 1 1b 1

b 1

b

Outpatient Hospital Services (non-emergency) 0 1 1 0 0 0

Outpatient Therapy (physical/respiratory) 0 1 1 0 0 0

Pharmacy 2 2 2 2 2 2

Podiatrist 1 1 1 0 0 0

Primary Care 0 0 0 0 0 2c

Vision 2 2c 2 2 2 2

* Children’s Medical Services did not have any changes to the limits for services during the time periods. aSSI only,

bTANF only,

c1 plan both SSI & TANF, 1 plan SSI only

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Expanded Benefits All plan comparison 2007, 2011, 2012, and 2013

Table 40 shows the number of plans with and without expanded benefits for the years 2007, 2011, 2012, and 2013. Children’s Medical Services was excluded from the totals in the table below since the plan is limited to a very specific population and did not provide expanded benefits to enrollees during any time period. The total number of plans with expanded benefits increased from 12 in 2007 to 13 in 2013. The number of plans without expanded benefits decreased from three in 2007 to one in 2013. The most common expanded benefit offered amongst the plans was “over-the-counter (OTC) pharmacy,” followed by “adult dental” (See Appendix D: Expanded Benefits Detailed Chart). Table 40: Total Number of Plans With and Without Expanded Benefits, All Plan Comparison, 2007 vs. 2011, 2012, and 2013

2007 2011 2012 2013

With Expanded Benefits

Without Expanded Benefits

With Expanded Benefits

Without Expanded Benefits

With Expanded Benefits

Without Expanded Benefits

With Expanded Benefits

Without Expanded Benefits

12 3 12 1 12 1 13 1

Note. Adapted from health plan comparison charts from October 2007 and September 2013.

Plan Comparison for Plans That Were in Operation 2007 to 2013

During the 2007, 2011, 2012, and 2013 period examined, changes were made for seven of the 14 expanded benefits. In 2013, one HMO plan, Humana Family, and one PSN plan, South Florida Community Care Network, offered expanded benefits for respite care/home health services. In prior years examined, no plans offered expanded benefits for respite care/home health services. Children’s Medical Services did not provide expanded benefits to their enrollees, so there was no change from 2007 to 2013. Among the HMOs, the number of plans that offered expanded benefits for OTC-pharmacy, circumcision, and adult dental services decreased from 2007 to 2013. After 2007, none of the HMO plans offered expanded benefits for adult dental services. Similarly, among the PSNs, after 2007, none of the plans offered expanded benefits for adult hospital inpatient or adult outpatient services (Table 41). Table 41: Number of Plans that Changed Certain Expanded Benefits Operational in 2007 and 2013

HMOs (n=2) PSNs (n=2) Expanded Benefits 2007 2011 2012 2013 2007 2011 2012 2013 Over the Counter Pharmacy 2

a 1

a 1

a 1

a 0 0 0 0

Circumcision 2a 1

c 1

c 1

c 1

a 1

a 1

a 1

a

Adult Dental Services 1a 0 0 0 1

a 1

a 1

a 1

a

Respite Care/Home Health Services 0 0 0 1a 0 0 0 1

a

Maternity 0 0 0 0 1a 1

c 1

c 1

a

Adult Hospital Inpatient 0 0 0 0 1b 0 0 0

Adult Outpatient 0 0 0 0 1b 0 0 0

aBoth SSI and TANF bSSI only cTANF only

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Table 42 provides a summary of plans that provided OTC pharmacy expanded and adult dental expanded benefits in both 2007 and 2013. South Florida Community Care Network was the only plan in existence in both years that offered adult dental expanded benefits in 2007 and 2013. United Healthcare – Baker, Clay, Nassau, & Duval was the only plan in existence in both years that offered over-the-counter pharmacy benefits in both 2007 and 2013.

Table 42: Plans Providing Over-the-Counter (OTC) Pharmacy Expanded and Adult Dental Expanded Benefits in 2007 and 2013

Plan Name Reform Plan OTC Dental

2007 2011 2012 2013 2007 2011 2012 2013 2007 2011 2012 2013

Access Health Solutions X

Amerigroup Florida, Inc. X 1 1

Better Health X X X 1 1 1 1 1 1

CareFlorida X X X 1 1 1 1 1 1

CMS – Broward X X X X 1

CMS – Duval X X X

Clear Health Alliance X 1 1

First Coast Advantage – Duval X X X X

First Coast Advantage – Baker/Clay/Nassau

X X X

Florida NetPASS X

Freedom Health Plan X X X 1 1 1 1 1 1

HealthEase X 1 (Br/D)

1 (Br/D,

SSI)

Humana X X X X 1 (Br)

Magellan Complete Care X

Medica Health Plans of Florida X X X 1 1 1

Molina Healthcare X X X 1 1 1 1 1 1

Pediatric Associates X

Positive Health Care X X X 1 1 1 1 1 1

Preferred Medical Plan, Inc. X 1 1

South Florida Community Care Network

X X X X 1 1 1 1

Staywell X 1 (Br/D)

1 (Br/D)

Staywell Health Plan of Florida X 1 1

Sunshine State Health Plan – Broward

X X X 1 1 1 1 1

Sunshine State Health Plan – Clay/Duval

X X X

Total Health Choice, Inc. X 1 1

United Healthcare – Baker/Clay/Nassau/Duval

X X X X 1 1 1 1 1

United Healthcare – Broward X 1 1

Universal Health Care – Broward/Duval

X X X 1 1 1

Vista Health Plan of S. Florida, Inc.

X 1 1 (SSI)

Vista Health Plan: Buena Vista X 1 1 (SSI)

Notes. Br/D = Plan benefit available in Broward and Duval counties only. SSI – Plan benefit provided for the Aged and/or Disabled population group only. Adapted from health plan comparison charts from October 2007, April 2011, April 2012, and September 2013.

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Summary: Domains ii.a and ii.b Throughout the duration of the demonstration, plans have exited and entered the market with some frequency. Of the fifteen plans participating in 2013, four were PSNs and the remaining eleven were HMOs. Positive Health Care is an HMO that became operational in Broward County in 2010, and serves only individuals with HIV or AIDS and their household members. Children’s Medical Services is a PSN that provides specialized pediatric-only plans (POPs) for children with special needs. Overall, plan limits did not vary greatly in the 2007, 2011, 2012, and 2013 period, nor did they vary greatly between plans. Many standard services were covered completely. The number of plans requiring copays decreased from nine in 2007 to three in 2013. The number of plans that were operational throughout the duration of the entire demonstration was relatively small. There were five plans that were operational in the 2007, 2011, 2012, and 2013 period. Two were HMOs, United Healthcare and Humana Family, and three were PSNs, Children’s Medical Services, SFCCN, and First Coast Advantage. The plans were primarily located in Broward County, which had three plans available in both years: one HMO, Humana Family, and two PSNs, Children’s Medical Services and SFCCN; Duval County, which had two plans, First Coast Advantage and United Healthcare; and Baker, Clay, and Nassau Counties which had one plan, United Healthcare HMO. Generally speaking, average market share for the HMOs declined in the 2007, 2011, 2012, and 2013 period, while average market share for the PSNs increased during the time period. Of the five plans operational throughout the 2007, 2011, 2012, and 2013 period, three required some form of copay from their enrollees. Two of the PSNs, Children’s Medical Services and First Coast Advantage – Duval did not require any copays for any standard service during the time period. For both eligibility groups, the PSN plan, SFCCN, required a copay for clinic services, primary care physician/ARNP/PA services, and specialty physician services across all years but the HMO plans did not. The only service in which an HMO plan required a copay but the PSN did not was for non-emergency transportation. Four of the five plans operational in the 2007, 2011, 2012, and 2013 period experienced changes in limits, with the majority of plans changing limits related to pharmacy and vision services. At least one of the four plans made changes to the plan limits for approximately eleven of the twenty-five standard services. Children’s Medical Services did not have any changes to the limits for services during the time period. Most plans slightly decreased the number of expanded benefits available to beneficiaries between the 2007, 2011, 2012, and 2013 period. South Florida Community Care Network was the only plan in existence in the period of analyses that offered adult dental expanded benefits throughout. United Healthcare – Baker, Clay, Nassau, & Duval was the only plan in existence in the 2007, 2011, 2012, and 2013 period that offered OTC pharmacy expanded benefits throughout.

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Domain ii.c: Are there differences in enrollees’ satisfaction with and experiences with care between plans with different benefit packages ? Between plans that offer additional benefits vs. those that do not?

To address this domain, comparisons in the CAHPS reports and ratings of care in DY6 through DY8 were conducted within the following groups: enrollees in plans with OTC expanded benefits and enrollees in plans without OTC expanded benefits (Tables 43 and 44); and enrollees in plans that provided adult dental benefits, and enrollees in plans that did not provide adult dental benefits (Tables 45 and 46). Two types of tests of statistical significance are presented. First, Tables 43 and 45 indicate trends for OTC expanded benefits/adult dental benefits between DY6 and DY8 and no OTC expanded benefits/no adult dental benefits between DY6 and DY8, respectively. Second, Tables 44 and 46 indicate trends in differences between OTC expanded benefits/adult dental benefits across all three years and no OTC expanded benefits/no dental adult benefits across all three years, respectively. The values in the table represent percentages of enrollees who rated their care at the highest levels (9 and 10, or Level 3, on a scale from 0 to 10). Table 43 compares satisfaction and experience with care for enrollees in plans with OTC expanded benefits and enrollees in plans without OTC expanded benefits in DY6 through DY8. The tests of statistical significance refer to trends for OTC expanded benefits between DY6 and DY8 and no OTC expanded benefits between DY6 and DY8. There was a statistically significant (p<.05) increase over time in the percentage of enrollees who would recommend their health plan to family and friends (“Definitely Yes”), both within the plans that offered OTC expanded benefits and within the plans that did not. Within the plans that offered OTC expanded benefits, the percentage of enrollees who gave their care the highest ratings (Level 3) increased from DY7 to DY8 across all measures but one. However, only one increase was statistically significant. In contrast, within the plans that did not offer OTC expanded benefits, the percentage of enrollees who gave their care the highest ratings (Level 3) increased from DY7 to DY8 on three measures and decreased on two measures. As in the preceding comparison, only one trend was statistically significant (Table 43).

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Table 43: Domain ii.c, Over-the-Counter (OTC) Pharmacy Expanded Benefits vs. No OTC Pharmacy Expanded Benefits, DY6 – DY8 Domain ii.c: Are there differences in enrollees’ satisfaction with and experiences with care between plans with

different benefit packages? Between plans that offer additional benefits vs. those that do not?

Questions DY6 DY7 DY8 Coefficient Trend

Using any number from 0 – 10, where 0 is the worst and 10 is the best health care possible, what number would you use to rate your (your child’s) health care in the last 6 months? [Level 3 (9–10)]

OTC Expanded Benefits

59.8% 60.6% 66.3% 0.0773 ↔a

No OTC Expanded Benefits

65.7% 63.2% 64.2% -0.0729 ↔a

Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would you use to rate your (your child’s) health plan? [Level 3 (9–10)]

OTC Expanded Benefits

56.3% 59.9% 59.3% 0.108 ↔a

No OTC Expanded Benefits

60.2% 61.2% 59.0% 0.0079 ↔a

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor? [Level 3 (9–10)]

OTC Expanded Benefits

71.1% 72.4% 78.6% 0.1207 ↔a

No OTC Expanded Benefits

75.4% 73.4% 70.1% -0.1201 ↔a

Would you recommend your health plan to your family or friends? (Adults only) [Definitely Yes]

OTC Expanded Benefits*

37.9% 54.7% 55.6% 0.6208 ↑*

No OTC Expanded Benefits*

48.2% 53.0% 54.1% 0.2279 ↑*

Using any number from 0 – 10, where 0 is the worst and 10 is the best specialist possible, what number would you use to rate your (your child’s) specialist? [Level 3 (9–10)]

OTC Expanded Benefits

62.5% 64.3% 61.5% 0.0355 ↔a

No OTC Expanded Benefits

68.8% 62.5% 65.8% -0.184 ↔a

Note. Statistical tests are based on weighted data. *Trend was statistically significant at < 0.05. aThe average annual change

was not statistically significant, so there was no change across the years.

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Table 44 compares enrollees’ satisfaction with and experiences with care in plans with OTC expanded benefits and plans without OTC expanded benefits in DY6, DY7, and DY8. The values in the table represent percentages of enrollees who rated their care at the highest levels (9 and 10, or Level 3, on a scale from 0 to 10). In addition, the trend in the percentage difference from year to year is indicated, as well as its statistical significance. Enrollees in plans with OTC expanded benefits were more satisfied than enrollees in plans without OTC expanded benefits in several Domain ii.c areas, and less satisfied in other areas. In general, enrollees in plans with OTC expanded benefits appeared less satisfied in DY6 and DY7 and more satisfied in DY8. However, across the three years displayed, only one increasing trend was statistically significant (p<.05). More enrollees in plans without OTC expanded benefits rated their health care at the highest level 3 (9-10), compared to enrollees in plans with OTC expanded benefits (Table 44). Table 44: Domain ii.c, Over-the-Counter (OTC) Pharmacy Expanded Benefits vs. No OTC Pharmacy Expanded Benefits, DY6 – DY8 (II) Domain ii.c: Are there differences in enrollees’ satisfaction with and experiences with care between plans with

different benefit packages? Between plans that offer additional benefits vs. those that do not?

Questions

OTC Expanded Benefits

No OTC Expanded Benefits

Coefficient Trend

Using any number from 0 – 10, where 0 is the worst and 10 is the best health care possible, what number would you use to rate your (your child’s) health care in the last 6 months? [Level 3 (9–10)]

DY6 59.8% 65.7% 4.45

↑* DY7 60.6% 63.2%

DY8 66.3% 64.2%

Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would you use to rate your (your child’s) health plan? [Level 3 (9–10)]

DY6 56.3% 60.2% 2.1

↔a DY7 59.9% 61.2%

DY8 59.3% 59.0%

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor? [Level 3 (9–10)]

DY6 71.1% 75.4% 6.4

↔a DY7 72.4% 73.4%

DY8* 78.6% 70.1%

Would you recommend your health plan to your family or friends? (Adults only) [Definitely Yes]

DY6* 37.9% 48.2% 5.9

↔a DY7 54.7% 53.0%

DY8 55.6% 54.1%

Using any number from 0 – 10, where 0 is the worst and 10 is the best specialist possible, what number would you use to rate your (your child’s) specialist? [Level 3 (9–10)]

DY6 62.5% 68.8% 1.0

↔a DY7 64.3% 62.5%

DY8 61.5% 65.8%

Note. Statistical tests are based on weighted data. *Trend was statistically significant at < 0.05. aThe average annual change

was not statistically significant, so there was no change across the years.

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Table 45 compares satisfaction and experience with care for enrollees in plans with adult dental benefits and for enrollees in plans without adult dental benefits for DY6 through DY8. The tests of statistical significance refer to trends for adult dental benefits between DY6 and DY8 and no adult dental benefits between DY6 and DY8. The values in the table represent percentages of enrollees who rated their care at the highest levels (9 and 10, or Level 3, on a scale from 0 to 10). There was a statistically significant increase (p<.05) over time in the percentage of enrollees who would recommend their health plan to family or friends (“Definitely Yes”), both within plans that offered adult dental benefits and plans that did not. There was also a statistically significant increase (p<.05) over time in the percentage of enrollees in plans with adult dental benefits who gave the highest rating (Level 3) to their health plan and personal doctor, coupled with a significant decrease (p<.05) in the rating of personal doctors by enrollees in plans without adult dental benefits (Table 45). Table 45: Domain ii.c, Adult Dental Benefits vs. No Adult Dental Benefits, DY6 – DY8 Domain ii.c: Are there differences in enrollees’ satisfaction with and experiences with care between plans with

different benefit packages? Between plans that offer additional benefits vs. those that do not?

Questions DY6 DY7 DY8 Coefficient Trend Using any number from 0 – 10, where 0 is the worst and 10 is the best health care possible, what number would you use to rate your (your child’s) health care in the last 6 months? [Level 3 (9–10)]

Adult Dental Benefits 62.2% 64.0% 66.7% 0.0856 ↔a

No Adult Dental Benefits

64.0% 60.4% 63.3% -0.0899 ↔a

Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would you use to rate your (your child’s) health plan? [Level 3 (9–10)]

Adult Dental Benefits* 57.0% 62.3% 59.9% 0.1443 ↑*

No Adult Dental Benefits

59.8% 59.1% 58.3% -0.0308 ↔a

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor? [Level 3 (9–10)]

Adult Dental Benefits* 72.5% 75.8% 78.6% 0.17 ↑*

No Adult Dental Benefits*

74.5% 70.3% 68.5% -0.1778 ↓*

Would you recommend your health plan to your family or friends? (Adults only) [Definitely Yes]

Adult Dental Benefits* 38.6% 55.8% 56.1% 0.6022 ↑*

No Adult Dental Benefits*

47.2% 51.9% 53.3% 0.2331 ↑*

Using any number from 0 – 10, where 0 is the worst and 10 is the best specialist possible, what number would you use to rate your (your child’s) specialist? [Level 3 (9–10)]

Adult Dental Benefits 63.6% 63.8% 63.4% 0.0048 ↔a

No Adult Dental Benefits

68.3% 62.7% 64.8% -0.1739 ↔a

Note. Statistical tests are based on weighted data. *Trend was statistically significant at < 0.05. aThe average annual change

was not statistically significant, so there was no change across the years.

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Table 46 compares enrollees’ satisfaction with and experiences with care in plans with adult dental benefits and plans without adult dental benefits in DY6, DY7, and DY8. The values in the table represent percentages of enrollees who rated their care at the highest levels (9 and 10, or Level 3, on a scale from 0 to 10). In addition, the trend in the percentage difference from year to year is indicated, as well as its statistical significance. Enrollees in plans with adult dental benefits were more satisfied than enrollees in plans without adult dental benefits in several Domain ii.c areas, and less satisfied in other areas. In general, enrollees in plans with adult dental benefits appeared less satisfied in DY6 and more satisfied in DY7 and DY8. However, across the three years displayed, none of the trends (whether increasing or decreasing) was statistically significant (Table 46).

Table 46: Domain ii.c, Adult Dental Benefits vs. No Adult Dental Benefits, DY6 – DY8 (II) Domain ii.c: Are there differences in enrollees’ satisfaction with and experiences with care between plans with

different benefit packages? Between plans that offer additional benefits vs. those that do not?

Questions Adult Dental Benefits

No Adult Dental Benefits

Coefficient Trend

Using any number from 0 – 10, where 0 is the worst and 10 is the best health care possible, what number would you use to rate your (your child’s) health care in the last 6 months? [Level 3 (9–10)]

DY6 62.2% 64.0% 2.6 ↔a DY7 64.0% 60.4%

DY8 66.7% 63.3%

Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would you use to rate your (your child’s) health plan? [Level 3 (9–10)]

DY6 57.0% 59.8% 2.2 ↔a DY7 62.3% 59.1%

DY8 59.9% 58.3%

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor? [Level 3 (9–10)]

DY6 72.5% 74.5% 3.75 ↔a DY7 75.8% 70.3%

DY8* 78.6% 68.5%

Would you recommend your health plan to your family or friends? (Adults only) [Definitely Yes]

DY6 38.6% 47.2% 5.7 ↔a DY7 55.8% 51.9%

DY8* 56.1% 53.3%

Using any number from 0 – 10, where 0 is the worst and 10 is the best specialist possible, what number would you use to rate your (your child’s) specialist? [Level 3 (9–10)]

DY6 63.6% 68.3% 1.65 ↔a DY7 63.8% 62.7%

DY8 63.4% 64.8%

Note. Statistical tests are based on weighted data. *Trend was statistically significant at < 0.05. aThe average annual change

was not statistically significant, so there was no change across the years.

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Summary: Domain ii.c Trend analysis of satisfaction and experience with care from DY6 to DY8 within plans that offered OTC expanded benefits and adult dental benefits generated mixed results. The percentage of enrollees who gave the highest ratings to their health care, health plan, personal doctor, and specialist increased on several measures and decreased on others. However, most of the changes were not statistically significant. In general, enrollees in plans with OTC expanded benefits appeared less satisfied than enrollees in plans without OTC expanded benefits in DY6 and DY7 and more satisfied in DY8. In general, enrollees in plans with adult dental benefits appeared less satisfied than enrollees in plans without adult dental benefits in DY6 and more satisfied in DY7 and DY8. Over time, significantly more enrollees would recommend their health plan to family or friends, whether or not they received OTC pharmacy expanded benefits or adult dental benefits.

Domain ii.d: Does access to and quality of care vary between plans with different benefit packages? Between plans that offer additional benefits vs . those that do not?

To address this domain, comparisons in the CAHPS reports and ratings of care in DY6 through DY8 were conducted within the following groups: enrollees in plans with OTC expanded benefits and enrollees in plans without OTC expanded benefits (Tables 47 and 48); and enrollees in plans that provided adult dental benefits, and enrollees in plans that did not provide adult dental benefits (Tables 49 and 50). Two types of tests of statistical significance are presented. First, Tables 47 and 49 indicate trends for OTC expanded benefits/adult dental benefits between DY6 and DY8 and no OTC expanded benefits/no adult dental benefits between DY6 and DY8, respectively. Second, Tables 48 and 50 indicate trends in differences between OTC expanded benefits/adult dental benefits across all three years and no OTC expanded benefits/no dental adult benefits across all three years, respectively. The values in the tables represent percentages of enrollees who rated their access to and quality of care at the highest level (Always/Yes). Over time, there was a statistically significant increase (p<.05) in the percentage of enrollees in plans that provided OTC expanded benefits who indicated “Always” to the ease of getting care, tests and treatment through their health plan. There was also a significant increase in the frequency of getting timely urgent and non-urgent care (“Always”) within plans that did not offer OTC expanded benefits (Table 47). Among enrollees whose plans had OTC expanded benefits, there was an increase from DY7 to DY8 across all measures in the percentage of enrollees who reported “Always” to the ease of getting care and who reported “Yes” to having a personal doctor. However, only one trend was statistically significant. Enrollees whose plans did not offer OTC expanded benefits also reported increased access and quality of care from DY7 to DY8 across most measures. Two trends, related to getting urgent and non-urgent care, were statistically significant (Table 47).

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Table 47: Domain ii.d, Over the Counter (OTC) Expanded Benefits vs. No OTC Expanded Benefits, DY6 – DY8

Domain ii.d: Does access to and quality of care vary between plans with different benefit packages? Between plans that offer additional benefits vs. those that do not?

Questions DY6 DY7 DY8 Coefficient Trend In the last 6 months how often was it easy to get appointments for you (your child) with specialists? [Always]

OTC Expanded Benefits 52.4% 51.5% 52.6% -0.0166 ↔b

No OTC Expanded Benefits

44.6% 45.5% 58.6% 0.1533 ↔b

In the last 6 months, how often was it easy to get the care, tests, or treatment you thought you (your child) needed through your health plan? [Always]

OTC Expanded Benefits*

53.3% 60.8% 68.2% 0.3107 ↑*

No OTC Expanded Benefits

58.6% 58.8% 67.8% 0.1318 ↔b

A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt. Do you (your child) have a personal doctor? [Yes]

OTC Expanded Benefits 89.9% 91.0% 91.0% 0.0956 ↔b

No OTC Expanded Benefits

90.7% 92.0% 91.7% 0.1189 ↔b

In the last 6 months, when you needed care right away, how often did you get care as soon as you needed? [Always]

a

OTC Expanded Benefits 73.8% 75.0% 79.6% 0.1071 ↔b

No OTC Expanded Benefits*

73.2% 80.9% 82.3% 0.3631 ↑*

In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor's office or clinic as soon as you needed?[Always]

a

OTC Expanded Benefits 66.5% 68.3% 68.3% 0.063 ↔b

No OTC Expanded Benefits*

64.8% 71.8% 70.4% 0.2312 ↑*

Note. Statistical tests are based on weighted data. *Trend was statistically significant at <0.05. aWording of question is different than in previous years. bThe average annual change was not statistically significant, so there was no change across the years.

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Enrollees in plans with OTC expanded benefits were more satisfied than enrollees in plans without OTC expanded benefits in several Domain ii.d areas, and less satisfied in other areas. There was no consistent tendency in any of the domain areas. However, across the three years displayed, none of the trends (whether increasing or decreasing) was statistically significant (Table 48).

Table 48: Domain ii.d, Over the Counter (OTC) Expanded Benefits vs. No OTC Expanded Benefits, DY6 – DY8 (II)

Domain ii.d: Does access to and quality of care vary between plans with different benefit packages? Between plans that offer additional benefits vs. those that do not?

Questions OTC Expanded Benefits

No OTC Expanded Benefits

Coefficient Trend

In the last 6 months how often was it easy to get appointments for you (your child) with specialists? [Always]

DY6 52.4% 44.6% -6.9 ↔a

DY7 51.5% 45.5%

DY8 52.6% 58.6%

In the last 6 months, how often was it easy to get the care, tests, or treatment you thought you (your child) needed through your health plan? [Always]

DY6 53.3% 58.6% 2.85 ↔a DY7 60.8% 58.8%

DY8 68.2% 67.8%

A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt. Do you (your child) have a personal doctor? [Yes]

DY6 89.9% 90.7% 0.05 ↔a DY7 91.0% 92.0%

DY8 91.0% 91.7%

In the last 6 months, when you needed care right away, how often did you get care as soon as you needed? [Always]

a

DY6 73.8% 73.2% -1.65 ↔a DY7 75.0% 80.9%

DY8 79.6% 82.3%

In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor's office or clinic as soon as you needed? [Always]

a

DY6 66.5% 64.8% -1.9 ↔a DY7 68.3% 71.8%

DY8* 68.3% 70.4%

Note. Statistical tests are based on weighted data. *Trend was statistically significant at <0.05. aWording of question is different than in previous years. bThe average annual change was not statistically significant, so there was no change across the years.

Table 49 compares access to quality of care for enrollees in plans with adult dental benefits and for enrollees in plans without adult dental benefits in DY6 through DY8. The tests of statistical significance refer to trends for adult dental benefits between DY6 and DY8 and no adult dental benefits between DY6 and DY8. The values in the table represent percentages of enrollees who rated their access to and quality of care at the highest level (Always/Yes). Over time, there was a statistically significant increase (p<.05) in the percentage of adults who reported that it was always easy to get needed care, tests, and treatment through their health plan within those plans that offered adult dental benefits. There was also a statistically significant increase (p<.05) in the percentage of adults who reported that it was always easy to get non-urgent care as soon as needed, within the plans that did not offer adult dental benefits. Among enrollees whose plans had adult dental benefits, there was an increase from DY7 to DY8 in the percentage who reported “Always” to the ease of getting appointments with specialists, getting care through the health plan, and getting urgent care as soon as they wanted. However, only one of these increases was statistically significant. Enrollees whose plans did not offer adult dental benefits also reported increased access and quality of care from DY7 to DY8 across all measures, except for one. One comparison, with regard to the ease of getting timely non-urgent care, was statistically significant (Table 49).

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Table 49: Domain ii.d, Adult Dental Benefits vs. No Adult Dental Benefits, DY6 – DY8 Domain ii.d: Does access to and quality of care vary between plans with different benefit packages? Between

plans that offer additional benefits vs. those that do not?

Questions DY6 DY7 DY8 Coefficient Trend In the last 6 months how often was it easy to get appointments for you (your child) with specialists? [Always]

Adult Dental Benefits

50.5% 50.9% 53.9% 0.0398 ↔b

No Adult Dental Benefits

45.6% 45.4% 58.5% 0.1113 ↔b

In the last 6 months, how often was it easy to get the care, tests, or treatment you thought you (your child) needed through your health plan? [Always]

Adult Dental Benefits*

54.5% 59.4% 67.3% 0.2472 ↑*

No Adult Dental Benefits

58.1% 59.9% 68.5% 0.168 ↔b

A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt. Do you (your child) have a personal doctor? [Yes]

Adult Dental Benefits

91.0% 91.8% 90.5% 0.0414 ↔b

No Adult Dental Benefits

89.8% 91.3% 92.3% 0.1675 ↔b

In the last 6 months, when you needed care right away, how often did you get care as soon as you needed? [Always]

a

Adult Dental Benefits

72.9% 77.7% 80.1% 0.2331 ↔b

No Adult Dental Benefits

73.8% 79.1% 82.3% 0.2754 ↔b

In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor's office or clinic as soon as you needed? [Always]

a

Adult Dental Benefits

67.8% 68.5% 67.3% 0.0108 ↔b

No Adult Dental Benefits*

63.6% 71.9% 71.9% 0.3013 ↑*

Note. Statistical tests are based on weighted data. *Trend was statistically significant at <0.05. aWording of question is different than in previous year. bThe average annual change was not statistically significant, so there was no change across the years.

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Enrollees in plans with adult dental benefits were more satisfied than enrollees in plans without adult dental benefits in several Domain ii.d areas, and less satisfied in other areas. There was no consistent tendency in any of the domain areas. However, across the three years displayed, none of the trends (whether increasing or decreasing) was statistically significant (Table 50).

Table 50: Domain ii.d, Adult Dental Benefits vs. No Adult Dental Benefits, DY6 – DY8 (II) Domain ii.d: Does access to and quality of care vary between plans with different benefit packages? Between

plans that offer additional benefits vs. those that do not?

Questions Adult Dental Benefits

No Adult Dental Benefits

Coefficient Trend

In the last 6 months how often was it easy to get appointments for you (your child) with specialists? [Always]

DY6 50.5% 45.6% -4.75 ↔a

DY7 50.9% 45.4%

DY8 53.9% 58.5%

In the last 6 months, how often was it easy to get the care, tests, or treatment you thought you (your child) needed through your health plan? [Always]

DY6 54.5% 58.1% 1.2 ↔a DY7 59.4% 59.9%

DY8 67.3% 68.5%

A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt. Do you (your child) have a personal doctor? [Yes]

DY6 91.0% 89.8% -1.5 ↔a DY7 91.8% 91.3%

DY8 90.5% 92.3%

In the last 6 months, when you needed care right away, how often did you get care as soon as you needed? [Always]

a

DY6 72.9% 73.8% -0.65 ↔a DY7 77.7% 79.1%

DY8 80.1% 82.3%

In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor's office or clinic as soon as you needed? [Always]

a

DY6 67.8% 63.6% -4.4 ↔a DY7 68.5% 71.9%

DY8* 67.3% 71.9%

Note. Statistical tests are based on weighted data. *Trend was statistically significant at <0.05. aWording of question is different than in previous years. bThe average annual change was not statistically significant, so there was no change across the years.

Summary: Domain ii.d Overall, there was an increase in access and quality of care from DY7 to DY8 within each of the four enrollee comparison groups (OTC expanded benefits, no OTC expanded benefits, adult dental benefits, and no adult dental benefits). Over time, however, most of the increases were not statistically significant. Similarly, the difference in enrollee access and quality of care between plans with additional benefits and plans without additional benefits was neither systematic, nor statistically significant except for one survey item (getting timely appointments in DY8).

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General Findings: Domain iii

The basic analytic strategy for Domain iii was a comparison over time between two urban Medicaid Reform counties (Broward and Duval) and two urban Non-Reform counties (Orange and Hillsborough) representing the Non-Reform Medicaid population in Florida. For this sub-study, the data came from five primary sources: (1) the Enhanced Benefits Information System (EBIS); (2) Medicaid claims and eligibility data; (3) hospital discharge data; (4) encounter data, and (5) AHCA quarterly and annual reports.

Domain iii.a: To what extent do enrollees earn Enhanced Benefits? To what extent do they spend their rewards?

Table 51 below presents the distribution of EBA program enrollees by reward earning levels across six years of the demonstration. Approximately 55 – 60% of enrollees earned no EBA credits in each demonstration year. Between 22% and 30% of enrollees earned at the lowest level and between 1% and 4% earned the maximum number of credits. There are some changes in the distribution of earning levels across the six years. In DY2, 3, and 4, approximately 60% of the enrollees had no credits, while in years 5, 6, and 7, 56% did not earn credits. In DY2, 4% of enrollees earned the maximum number of credits. This declined to less than 1% in DY3 and 4 and climbed to about 2.5% in DY6 and 7 (Table 51). Table 51: Domain iii.a: Distribution of EBA Program Enrollees by Levels of Earnings, DY2 – DY7

Demonstration Year (DY)

None ($0) N (%)

Low ($1–50) N (%)

Medium ($51–124) N (%)

High ($125) N (%)

Total N (%)

2 254,810 (60.2) 93,400 (22.1) 57,390 (13.6) 17,510 (4.1) 423,110 (100.0)

3 265,279 (60.4) 128,534 (29.3) 41,937 (9.6) 3,349 (0.8) 439,099 (100.0)

4 288,237 (59.2) 144,856 (29.8) 49,650 (10.2) 4,195 (0.9) 486,938 (100.0)

5 290,346 (55.5) 154,962 (29.6) 71,248 (13.6) 6,789 (1.3) 523,345 (100.0)

6 307,538 (55.3) 154,580 (27.8) 79,846 (14.4) 14,309 (2.6) 556,273 (100.0)

7 324,178 (55.7) 157,106 (27.0) 86,241 (14.8) 14,274 (2.5) 581,799 (100.0)

During the six years of the EBA program reported herein (DY2 through DY7), Medicaid enrollees earned approximately $64 million in EBA credits through eligible behaviors and spent slightly over $38 million (60.0%) of their earnings on eligible purchases (Table 52). The percentage of credits spent relative to the amount of credits earned peaked in DY3 (88.9%), then declined over the next three years, and rose again in DY7. Table 52: Domain iii.a: EBA Program Earnings and Purchases, DY2 – DY7 Demonstration Year (DY) Total EBA Credit Earnings ($) Total Purchases using EBA Credits ($) % Credits Used

2 10,725,480 2,432,729 22.7

3 7,181,145 6,386,161 88.9

4 8,317,698 6,132,458 73.7

5 11,060,385 6,527,726 59.0

6 12,944,773 7,537,453 58.2

7 13,590,615 9,273,520 68.2

Total 63,820,095 38,290,046 60.0

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The most credits were earned by engaging in preventive care for children and adults, which accounted for almost half (49.4%) of the total credits (Table 53 and Figure 3). Office visits by children and adults accounted for approximately 20% of the total credits earned. Cancer screenings, including Pap smear, mammogram, colorectal screening, and Prostate Specific Antigen (PSA), contributed to a combined 4.3% of total credits. All disease management and diabetes management programs accounted for 1.2% of all credits earnings. Participation in and completion (6 months) of alcohol and substance abuse programs, smoking cessation, BMI assessment and weight management, and exercise programs were reported 2,814 times in six years. It should be noted that DY6 was the first year when adult BMI assessment became eligible for EBA rewards and the counts of occurrences increased from DY6 (1,962) to DY7 (5,964)

Table 53: Domain iii.a: EBA Credit-Earning Behaviors, DY2 – DY7 Behavior EBA Earnings ($) Counts of

Occurrence Counts with Annual Cap

% of Total EBA Earnings

Preventive Care (Adult/Child)a 31,547,288 1,361,013 1,297,115 49.4

Office Visits (Adult/Child) 12,598,753 4,310,598 1,211,678 19.7

Dental Preventive Services (Adult/Child) 8,362,630 573,251 336,648 13.1

Vision Exams (Adult/Child) 3,526,403 288,011 141,759 5.5

Compliance with Prescribed Maintenance Drugs

3,135,430 2,749,730 421,771 4.9

Pap Smear 2,148,420 119,756 86,171 3.4

Diabetes Management 656,520 85,840 43,897 1.0

Mammography 345,238 34,487 13,990 0.5

Prostate-Specific Antigen 152,385 14,296 10,197 0.2

Colorectal Screening 128,878 8,743 5,207 0.2

Disease Management 96,643 11,235 3,968 0.2

Healthy Start Screening 79,623 5,926 5,309 0.1

Adult BMI Assessmentb 69,225 5,964 2,783 0.1

Exercise 310 14 14 0.0

Flu Shots 300 15 12 0.0

Addictions 180 10 8 0.0

Weight Management 175 9 9 0.0

Other 971,698 803,911 128,981 1.5 aDescriptions of preventive services are provided in Appendix I.

bAdult BMI Assessment was approved as an EBA credit-eligible behavior in 2011.

Almost 60% of EBA credits accumulated during the first year and a half of the Medicaid Reform period were earned by accessing primary care appointments (office visits) for adults and children. In March 2008, the state changed the policy to award credits only for office visits in the first 60 days of enrollment and the amount of credits for adult office visits was reduced by half to $7.50 per visit (Florida Agency for Health Care Administration, 2008, p. 63). The change was implemented at the beginning of DY3, the third year of the EBA program. As a result, the EBA earnings obtained through accessing office visits dropped in DY3 but have risen in subsequent years of the program (Table 54). Other top EBA credit-earning behaviors such as preventive services used by children and adults and dental services have been increasing consistently over the years, both in number of occurrences and value of credits earned. Diabetes management has been an EBA credit-eligible category since DY4 and its growth has been consistent.

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Table 54: Domain iii.a: Select EBA Credit-Earning Behaviors by Demonstration Year, DY2 – DY7 Preventive Services

(Adult/Child) Office Visits Preventive Dental Services Diabetes Management

DY EBA Credits

($)

Counts of

Behavior

Counts with

Annual Cap

EBA Credits

($)

Counts of

Behavior

Counts with

Annual Cap

EBA Credits ($)

Counts of Behavior

Counts with

Annual Cap

EBA Credits

($)

Counts of Behavior

Counts with

Annual Cap

2 3,826,958 168,780 156,538 5,264,143 532,073 233,707 390,128 27,617 15,678 0a 19 0

3 3,954,975 168,397 162,336 1,130,325 548,460 150,712 730,625 48,600 29,395 0a 130 0

4 4,446,903 187,518 182,622 1,318,058 663,120 175,747 963,845 65,789 38,673 61,103 7,776 4,095

5 5,418,355 230,465 222,663 1,544,250 834,506 205,904 1,917,270 133,016 77,124 128,835 15,008 8,623

6 6,940,085 304,486 285,943 1,642,668 869,781 219,027 2,016,665 142,878 81,276 208,768 27,875 13,954

7 6,960,013 301,367 287,013 1,699,310 862,658 226,581 2,344,098 155,351 94,502 257,815 35,032 17,225

Total 31,547,288 1,361,013 1,297,115 12,598,753 4,310,598 1,211,678 8,362,630 573,251 336,648 656,520 85,840 43,897 aDiabetes management behaviors did not become eligible for EBA credit earning until DY4.

Figures 3 and 4 depict the overall EBA credit-earning patterns in DY2 through DY7 in two different ways. Figure 3 indicates the use of different categories of EBA-eligible behaviors by EBA earners. Figure 4 indicates the categories of behaviors that earned the greatest amount of rewards. Approximately 32.7% (1,211,678) of EBA-eligible behaviors by enrollees were adult and child office visits, although only 19.7% of EBA credits ($12,598,753) were earned that way. Similarly, 11.4% (421,771) of behaviors were adherence with prescription medication, but those behaviors earned 4.9% ($3,135,430) of the total credits. In contrast, 35% (1,297,115) of behaviors were adult and child preventive services, which earned 49.4% ($31,547,288) of all EBA credits for the study period. Figure 3: Domain iii.a: EBA-Eligible Behavior Occurrences, DY2 – DY7

Preventive Services, 35.0%

Office Visits, 32.7%

Dental, 9.1%

Pap Smear, 2.3%

Maintenance Drugs, 11.4%

Vision Exams, 3.8%

Other, 5.7%

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Figure 4: Domain iii.a: EBA-Eligible Behavior Earnings, DY2 – DY7

Summary: Domain iii.a During DY2 – DY7 of the EBA program, a total of $63,820,095 in EBA credits were earned by enrollees and 60.0% of the earned credits were spent on eligible purchases. Approximately 55 – 60% of eligible enrollees each year did not earn credits. Overall, preventive care for children and adults accounted for 49.4% of the total credits earned. With the policy change at the end of DY2 regarding earning credits by accessing primary care services (office visits), in DY3 the EBA credit earnings associated with office visits dropped to almost one fifth of the previous year’s earnings in the same category. Credit earnings from office visits remained low in the subsequent years, which suggest that the adjustment has reached its expected goal. While the policy change resulted in reduced credit earnings for office visits, the counts of office visits actually increased. Cancer screening behaviors accounted for 4.4% of credit earning, while participation in disease management and diabetes management programs accounted for only 1% of all credit earnings; other healthy behaviors such as smoking cessation, alcohol and substance abuse programs, and fitness programs were reported 5,352 times over six years.

Preventive Services, 49.4%

Office Visits, 19.7%

Dental, 13.1%

Pap Smear, 3.4%

Maintenance Drugs, 4.9%

Vision Exams, 5.5% Other, 4.0%

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Domain iii.b: Is the Enhanced Benefits program associated with increased use of preventive services by enrollees?

Domain iii.c: Is there a difference in services used by enrollees participating in the Enhanced Benefits Account (EBA) program vs. enrollees who do not in Reform and Non -Reform counties?

Domains iii.b and iii.c are addressed in the following section. As illustrated above, preventive services accounted for a great share of the health behaviors for which credits were earned. It is not clear, however, that such behaviors increased in frequency during the time period of Medicaid Reform. In fact, the policy change to reduce the amount and frequency of rewards for office visits reflected a concern that the program was rewarding behaviors that would have occurred even in the absence of the EBA program. Furthermore, even if increased frequency could be clearly documented, it is yet another step to assess whether or not and how such an increase might be associated with the EBA program itself. These questions were explored by measuring differences in service use by level of EBA earnings (none, high, medium, low). Comparisons were also made between utilization rates of Reform enrollees and Non-Reform Florida Medicaid enrollees using similar services. Two types of preventive service claims were compared: those resulting from the use of medical facilities and those from outpatient settings. Additionally, utilization rates were calculated using encounter data, comparing Reform enrollees and Non-Reform Medicaid enrollees, as well as Reform enrollees by EBA earning levels. Results are available for DY5, DY6, and DY7, given the absence of comprehensive encounter data for the earlier years of the Medicaid Reform program. It should be noted that statistical analyses based on claims data may not necessarily lead to similar results as analyses based on encounter data. The Medicaid claims data covers mostly PSN enrollees and represent the actual services billed for reimbursement. On the other hand, the Medicaid encounter data cover mostly HMO enrollees and may include services that are not reimbursable.

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Enrollees in urban Medicaid Reform counties on average had more preventive claims than Medicaid enrollees in urban Non-Reform counties (Table 55). The difference was largest in DY5, when Reform enrollees had 1.16 medical preventive claims on average, compared to Non-Reform enrollees at 0.68. The average number of outpatient preventive claims per enrollee is small with only very small differences between the urban Reform and urban Non-Reform counties. Table 55: Domains iii.b and iii.c: Claims Data—Use of Preventive Services in Urban Reform vs. Urban Non-Reform Counties (Per Member Per Year)

Demonstration Year (DY)

Medical Preventive Claims Outpatient Preventive Claims

Reform (Mean)

Non-Reform (Mean)

Reform

(Mean) Non-Reform

(Mean)

2 0.90 0.85 0.03 0.02

3 1.00 0.77 0.03 0.02

4 1.14 0.71 0.04 0.01

5 1.16 0.68 0.05 0.01

6 1.11 0.65 0.06 0.02

7 0.87 0.47 0.04 0.01

Table 56 shows the differences in preventive service use between Reform and Non-Reform urban counties using Medicaid encounter data. In DY5, the mean number of medical preventive encounters was higher in Medicaid Reform compared to the Non-Reform counties. By DY6, the difference had narrowed and, in DY7, Reform enrollees had a smaller mean number of medical preventive encounters than Non-Reform enrollees. The mean numbers of outpatient preventive encounters in all years were very small in both Medicaid Reform and Non-Reform counties. Table 56: Domains iii.b and iii.c: Encounter Data—Use of Preventive Services in Urban Medicaid Reform vs. Urban Non-Reform Counties (Per Member Per Year)

Demonstration Year (DY)

Medical Preventive Encounters Outpatient Preventive Encounters

Reform

(Mean) Non-Reform

(Mean) Reform

(Mean) Non-Reform

(Mean)

5 4.01 2.86 0.02 0.01

6 2.60 2.43 0.01 0.01

7 1.04 1.40 0.00 0.01

Note. Encounter data are only available starting in DY5.

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Within Medicaid Reform counties, enrollees who earned rewards through EBA-eligible behaviors had more medical preventive claims per member per year than non-earners (Table 57). Qualified enrollees who did not earn any rewards had on average between 0.40 and 0.71 medical preventive claims in each of the Medicaid Reform years, whereas enrollees in the “high” earning group ($125 per year) had on average between 2.39 and 5.88 medical preventive claims. With regard to outpatient preventive claims, the mean number of claims per member per year changed little by earning level with the exception of high earners who had an average of 0.14 outpatient preventive claims in DY4. Table 57: Domains iii.b and iii.c: Claims Data—Use of Preventive Services by EBA Earning Level in Urban Reform Counties (Per Member Per Year) Demonstration

Year (DY)

Medical Preventive Claims

Outpatient Preventive Claims

None (Mean)

Low (Mean)

Medium (Mean)

High (Mean)

None (Mean)

Low (Mean)

Medium (Mean)

High (Mean)

2 0.65 0.86 1.30 2.56 0.03 0.02 0.04 0.06

3 0.67 1.05 2.20 2.39 0.02 0.03 0.04 0.07

4 0.71 1.07 2.91 3.67 0.03 0.04 0.08 0.14

5 0.59 1.16 2.61 3.63 0.04 0.05 0.07 0.09

6 0.55 0.91 2.21 5.88 0.05 0.06 0.06 0.08

7 0.40 0.81 1.80 3.67 0.05 0.03 0.04 0.05

In DY6, there was a substantial decrease in the mean number of medical preventive encounters per member per year in the low, medium, and high credit-earning groups, compared to the previous year, followed by a further decrease in DY7. Among those who did not earn any EBA credits, the mean number of medical preventive encounters was approximately the same in DY6 and DY7. The mean number of outpatient preventive encounters by earning category was very close to 0 in each of the three years of available data (Table 58). Table 58: Domains iii.b and iii.c: Encounter Data—Use of Preventive Services by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year) Demonstration

Year (DY)

Medical Preventive Encounters

Outpatient Preventive Encounters

None (Mean)

Low (Mean)

Medium (Mean)

High (Mean)

None (Mean)

Low (Mean)

Medium (Mean)

High (Mean)

5 0.27 6.10 10.43 17.56 0.00 0.04 0.04 0.03

6 0.16 4.11 6.41 7.18 0.00 0.02 0.03 0.03

7 0.13 1.39 2.54 4.60 0.00 0.01 0.01 0.01

Note. Encounter data are only available starting in DY5.

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The mean number of medical claims was higher in Medicaid Reform counties compared to Non-Reform counties for the demonstration period. Within Medicaid Reform counties, the average number of claims increased between DY2 and DY5 (Table 59). There was a slight decline in the mean number of claims from DY5 to DY6 and a further decline of four claims per member per year from DY6 to DY7. In the Non-Reform counties, the average number of claims slightly declined from DY2 to DY3 but then remained fairly constant through DY5. There was a slight decline from DY5 to DY6 and a further decrease of approximately four claims per member per year from DY6 to DY7. While the average number of medical claims per enrollee was similar between the two groups in DY2 (17.70 versus 17.46), it grew to a difference of more than three claims per enrollee in DY6 and DY7. Table 59: Domains iii.b and iii.c: Claims Data—Use of Medical Services in Urban Medicaid Reform vs. Urban Non-Reform Counties (Per Member Per Year)

Demonstration Year (DY) Total Medical Claims Reform

(Mean) Non-Reform

(Mean)

2 17.70 17.46

3 17.75 16.47

4 19.69 16.93

5 20.22 16.72

6 19.83 15.69

7 15.56 11.84

The average number of medical encounters steadily declined in both Medicaid Reform and Non-Reform counties between DY5 and DY7. Table 60 shows that there was an average difference of about nine medical encounters between Reform and Non-Reform enrollees during DY5. This declined to a difference of 4.32 encounters in DY6. From DY6 to DY7 there was a large decline in the average number of medical encounters in urban Medicaid Reform counties of approximately 12 medical encounters per member per year. There was also a decline in the Non-Reform counties of approximately four medical encounters per member per year from DY6 to DY7, resulting in an average difference of 3.71 medical encounters between Medicaid Reform and Non-Reform enrollees in DY7. It is important to note that differences in claims and encounter data in Medicaid Reform and Non-Reform counties likely cannot be solely attributed to the EBA program. Table 60: Domains iii.b and iii.c: Encounter Data—Use of Medical Services in Urban Medicaid Reform vs. Urban Non-Reform Counties (Per Member Per Year)

Demonstration Year (DY) Total Medical Encounters Reform

(Mean) Non-Reform

(Mean)

5 25.85 16.90

6 20.06 15.75

7 7.70 11.41

Note. Encounter data are only available starting in DY5.

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Table 61 illustrates the number of medical service claims by earning category in urban Medicaid Reform Counties across the demonstration years. Enrollees who did not earn any EBA rewards on average had a greater number of medical claims than earners in the “low” category in each of the Medicaid Reform years. However, enrollees in the “high” earner category had on average more claims than those in the “low,” “medium,” and non-earner categories across all demonstration years. High earners had a peak average per member per year of 42.59 claims in DY5. All earner categories saw a decline in the average number of claims in DY7 with the greatest decline among the high earners. Table 61: Domains iii.b and iii.c: Claims Data—Use of Medical Services by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year)

Demonstration Year (DY) Total Medical Claims None

(Mean) Low

(Mean) Medium (Mean)

High (Mean)

2 20.34 9.86 14.48 22.87

3 19.20 11.67 18.17 40.02

4 21.13 12.49 22.66 42.33

5 21.50 13.21 21.36 42.59

6 20.72 12.86 20.21 37.56

7 15.17 11.26 17.44 27.46

Non-earners had fewer medical encounters compared to EBA earners in DY5, DY6, and DY7. Similarly, high EBA earners registered the highest mean number of encounters DY5 to DY7. Among high earners, the average difference between DY5 and DY6 was about 45 encounters per member per year and the average difference between DY6 and DY7 was approximately 14.65 encounters per member per year (Table 62). Table 62: Domains iii.b and iii.c: Encounter Data—Use of Medical Services by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year)

Demonstration Year (DY) Total Medical Encounters

None (Mean)

Low (Mean)

Medium (Mean)

High (Mean)

5 3.11 45.14 51.00 85.05

6 2.82 35.40 38.46 39.79

7 1.98 11.20 14.92 25.20

Note. Encounter data are only available starting in DY5.

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Tables 63, 64, 65, and 66 below compare the average number of outpatient and inpatient claims and encounters, first between urban Reform and urban Non-Reform Medicaid enrollees, then by EBA earning categories within urban Medicaid Reform counties. Overall, enrollees in urban Medicaid Reform counties had more outpatient claims per person than enrollees in urban Non-Reform counties. The difference was greatest in DY7 with an average of two more outpatient claims per person in Medicaid Reform counties. Per person inpatient claims were also higher in Medicaid Reform counties compared to Non-Reform counties. However, the differences were relatively small (Table 63). Table 63: Domains iii.b and iii.c: Claims Data—Use of Outpatient and Inpatient Services in Urban Medicaid Reform vs. Urban Non-Reform Counties (Per Member Per Year) Demonstration Year

(DY) Outpatient Claims Inpatient Claims

Reform

(Mean) Non-Reform

(Mean) Reform

(Mean) Non-Reform

(Mean)

2 4.29 3.55 3.09 2.71

3 4.83 3.46 2.33 2.04

4 5.75 3.89 2.53 2.25

5 5.89 4.03 3.36 3.04

6 5.92 4.06 3.43 2.88

7 4.58 2.44 2.03 1.68

Outpatient encounters were higher in Medicaid Reform counties compared to Non-Reform counties in DY5 and DY6. However, in DY7 this changed and there were on average two more outpatient encounters per member per year in Non-Reform counties. There was a similar change among inpatient encounters with more inpatient encounters per member per year in Non-Reform counties in DY7 than Medicaid Reform counties (Table 64). Table 64: Domains iii.b and iii.c: Encounter Data—Use of Outpatient and Inpatient Services in Urban Medicaid Reform vs. Urban Non-Reform Counties (Per Member Per Year)

Demonstration Year (DY)

Outpatient Encounters Inpatient Encounters

Reform (Mean)

Non-Reform (Mean)

Reform (Mean)

Non-Reform (Mean)

5 7.60 5.01 0.52 0.41

6 5.78 5.69 0.50 0.41

7 1.90 3.97 0.47 0.85

Note. Encounter data are only available starting in DY5.

By EBA earning category within urban Medicaid Reform counties, “high” earners and non-earners in general had more outpatient and inpatient claims per person than enrollees in the “low” and “medium” earning categories, in all years (Table 65).

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Table 65: Domains iii.b and iii.c: Claims Data—Use of Outpatient and Inpatient Services by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year) Demonstration Year

(DY) Outpatient Claims Inpatient Claims

None (Mean)

Low (Mean)

Medium (Mean)

High (Mean)

None (Mean)

Low (Mean)

Medium (Mean)

High (Mean)

2 5.35 2.23 2.69 3.22 4.25 1.23 1.13 1.47

3 5.74 2.87 3.23 7.30 3.02 1.04 1.23 3.20

4 6.84 3.29 4.37 9.30 3.43 1.00 1.14 3.14

5 7.34 3.38 3.73 7.76 4.91 1.23 1.12 3.37

6 7.25 3.48 3.89 6.46 4.96 1.33 1.18 2.53

7 5.26 3.08 3.40 4.84 2.70 1.01 0.97 1.72

For encounter data, as shown in Table 66, the mean number of outpatient encounters declined across all EBA earning levels between DY5 and DY6, and DY6 and DY7. Interestingly, “low” and “medium” EBA earners had more outpatient encounters in DY6 compared to high earners. The average number of inpatient encounters was small across all earning levels for all years. Table 66: Domains iii.b and iii.c: Encounter Data—Use of Outpatient and Inpatient Services by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year) Demonstration

Year (DY) Outpatient Encounters Inpatient Encounters

None (Mean)

Low (Mean)

Medium (Mean)

High (Mean)

None (Mean)

Low (Mean)

Medium (Mean)

High (Mean)

5 1.87 13.77 11.31 14.57 0.14 0.95 0.66 1.32

6 1.44 10.79 8.43 7.23 0.15 0.92 0.63 0.83

7 0.65 2.97 3.05 4.31 0.17 0.74 0.68 1.52

Note. Encounter data are only available starting in DY5.

Similar to the results presented above, enrollees in urban Medicaid Reform counties on average had a greater number of maintenance drug claims per member per year than Medicaid enrollees in Non-Reform counties. Moreover, enrollees in Medicaid Reform counties had one pharmacy claim more than Non-Reform enrollees in both DY3 and DY7 (Table 67). Table 67: Domains iii.b and iii.c: Claims Data—Use of Pharmacy Services in Urban Medicaid Reform vs. Urban Non-Reform Counties (Per Member Per Year)

Demonstration Year (DY) Total Pharmacy Claims

Reform

(Mean) Non-Reform

(Mean)

2 4.36 4.00

3 7.80 6.71

4 7.29 6.91

5 7.84 7.08

6 7.78 6.91

7 7.67 6.26

As shown in Table 68, enrollees in urban Medicaid Reform counties on average had a greater number of maintenance drug encounters per member per year than Medicaid enrollees in Non-Reform counties in DY5 and DY6. However, in DY7, enrollees in Non-Reform counties had on average 2.10 more maintenance drug encounters per member per year than enrollees in urban Medicaid Reform counties.

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Table 68: Domains iii.b and iii.c: Encounter Data—Use of Pharmacy Services in Urban Medicaid Reform vs. Urban Non-Reform Counties (Per Member Per Year)

Demonstration Year (DY) Total Pharmacy Encounters

Reform (Mean)

Non-Reform (Mean)

5 12.68 9.52

6 9.92 8.93

7 2.63 4.73

Note. Encounter data are only available starting in DY5.

As shown in Table 69, “high” EBA earners had the highest average number of pharmacy claims per person in each of the demonstration years compared to “none,” “low,” and “medium” level earners. The highest number of average claims within each group peaked in DY3. After DY3, the number of pharmacy claims per person declined until DY7, when there was a very slight increase from the previous year for “medium” and “high” level earners. Table 69: Domains iii.b and iii.c: Claims Data—Use of Pharmacy Services by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year)

Demonstration Year (DY) Total Pharmacy Claims None

(Mean) Low

(Mean) Medium (Mean)

High (Mean)

2 2.99 3.80 7.27 13.99

3 3.45 10.11 17.72 60.89

4 3.29 9.31 15.21 53.17

5 3.16 9.48 14.65 49.30

6 2.89 9.68 14.42 28.98

7 2.60 9.12 15.84 29.87

Table 70 shows that the average number of pharmacy encounters for individuals with no EBA credits ranged between 1.46 in DY5 and 0.36 in DY7. Additionally, “low” EBA earners averaged between 20.88 and 3.81 pharmacy encounters while “medium” EBA earners averaged between 24.63 and 5.61 pharmacy encounters for the same time period. During DY5, high EBA earners averaged approximately 80 pharmacy encounters per person. However, this declined dramatically to 25.42 encounters per EBA high earner in DY6 and even further to 12.45 encounters per member per year in DY7. Table 70: Domains iii.b and iii.c: Encounter Data—Use of Pharmacy Services by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year)

Demonstration Year (DY) Total Pharmacy Encounters None

(Mean) Low

(Mean) Medium (Mean)

High (Mean)

5 1.46 20.88 24.63 79.71

6 1.13 17.85 18.26 25.42

7 0.36 3.81 5.61 12.45

Note. Encounter data are only available starting in DY5.

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Summary: Domains iii.b and iii.c Domains iii.b and iii.c compared the use of healthcare services as measured by claims and encounters in urban Medicaid Reform and urban Non-Reform counties, as well as the use of services by EBA earning categories within urban Medicaid Reform counties. During the study years, enrollees in Medicaid Reform counties had a greater number of medical preventive claims and outpatient preventive claims than Medicaid enrollees in Non-Reform counties. Within Medicaid Reform counties, enrollees who earned rewards through EBA-eligible behaviors had more medical preventive claims per member per year than non-earners. The number of outpatient preventive claims was very small and varied only slightly by earning level. Within Medicaid Reform counties, enrollees in the “high” earner category had on average more medical claims than those in the “low,” “medium,” and non-earner categories across all demonstration years. Similarly, high EBA earners registered the highest mean number of medical encounters DY5 to DY7. For outpatient and inpatient services, within urban Medicaid Reform counties, “high” earners and non-earners in general had more outpatient and inpatient claims per person than enrollees in the “low” and “medium” earning categories, in all years. The mean number of outpatient encounters declined across all EBA earning levels DY5 to DY7. The average number of inpatient encounters was small across all earning levels DY5 to DY7. “High” EBA earners had the highest average number of pharmacy claims and encounters per person in each of the demonstration years compared to “none,” “low,” and “medium” level earners. Results for encounter data are available only for DY5, DY6, and DY7, given the absence of comprehensive encounter data for the earlier years of the Medicaid Reform program. In DY5 and DY6, enrollees in Medicaid Reform counties had a greater number of medical preventive encounters than Medicaid enrollees in Non-Reform counties, while in DY7 enrollees in Medicaid Reform counties had a slightly smaller number of medical preventive encounters than Medicaid enrollees in Non-Reform counties. The average number of outpatient preventive encounters was about the same for enrollees in both Reform and Non-Reform counties. In DY5 and DY6, the average number of medical service encounters, outpatient, inpatient, and maintenance drug encounters per member per year was higher for enrollees in Medicaid Reform counties than enrollees in Non-Reform counties. However, in DY7, enrollees in Non-Reform counties had on average a greater number of medical, outpatient, inpatient, and maintenance drug encounters than enrollees in Medicaid Reform counties.

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Domain iii.d: Is there variation in the li kelihood of participation in certain health care behaviors between enrollees in Reform and Non-Reform counties?

The following section describes differences in the frequency of participation in cancer screenings and dental screenings. Medicaid enrollees in urban Medicaid Reform counties are compared to Medicaid enrollees in urban Non-Reform counties for cancer screenings only because health plans in the urban Non-Reform counties were not required to provide dental services to their recipients; thus, there is no dental encounter data for these counties. However, within Medicaid Reform counties, comparisons are made among Enhanced Benefits Account (EBA) earning levels. The number of cancer screening claims and encounters per person was similar and remained fairly constant in both the Medicaid Reform and Non-Reform counties throughout the study period, although a slight decrease in utilization was noted in DY7 compared to DY6 (Table 71). In DY2, 3, and 4, enrollees in urban Medicaid Reform counties had a slightly lower average number of claims than enrollees in Non-Reform counties. In DY5, the number of cancer screening claims was similar in Reform and Non-Reform counties. Table 71: Domain iii.d: Claims Data—Use of Cancer Screening Services in Urban Medicaid Reform vs. Urban Non-Reform Counties (Per Member Per Year)

Demonstration Year (DY) Medical Cancer Screening Claims

Reform (Mean)

Non-Reform (Mean)

2 0.10 0.13

3 0.09 0.13

4 0.10 0.12

5 0.11 0.11

6 0.10 0.09

7 0.08 0.08

As shown in Table 72, in DY5 and DY6, there were more cancer screening encounters in urban Medicaid Reform counties compared to Non-Reform counties. Since DY5, the use of cancer screening services has decreased within the Medicaid Reform counties, with a 50% decline in the average number of cancer screening encounters in DY7 compared to DY6. Among the Non-Reform counties, there was a slight decrease in the use of cancer screening services from DY5 to DY6 but no change in use from DY6 to DY7. In DY7, the average number of cancer screening encounters was higher in Non-Reform counties. Table 72: Domain iii.d: Encounter Data—Use of Cancer Screening Services in Urban Medicaid Reform vs. Urban Non-Reform Counties (Per Member Per Year)

Demonstration Year (DY) Medical Cancer Screening Encounters

Reform (Mean)

Non-Reform (Mean)

5 0.19 0.12

6 0.13 0.10

7 0.06 0.10

Note. Encounter data are only available starting in DY5.

Within urban Medicaid Reform counties, “high” EBA earners had more claims for cancer screening services than non-earners and earners in the “low” and “medium” groups, particularly in DY3, DY4, and DY5 (Table 73). Similarly, “high” EBA earners had more encounters for cancer screening services

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compared to “medium,” “low,” and “non-earners” except in DY6, when “medium” earners had more encounters for cancer screening services than the other earner and non-earner levels (Table 74). Table 73: Domain iii.d: Claims Data—Use of Cancer Screening Services by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year)

Demonstration Year (DY) Medical Cancer Screening Claims None

(Mean) Low

(Mean) Medium (Mean)

High (Mean)

2 0.12 0.04 0.07 0.08

3 0.10 0.06 0.08 0.29

4 0.11 0.07 0.10 0.32

5 0.12 0.07 0.09 0.30

6 0.11 0.07 0.10 0.14

7 0.08 0.06 0.09 0.11

Table 74: Domain iii.d: Encounter Data—Use of Cancer Screening Services by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year)

Demonstration Year (DY) Medical Cancer Screening Encounters None

(Mean) Low

(Mean) Medium (Mean)

High (Mean)

5 0.01 0.34 0.41 0.74

6 0.01 0.22 0.31 0.27

7 0.00 0.09 0.16 0.19

Note. Encounter data are only available starting in DY5.

Although dental encounter data was analyzed for this report, differences in the utilization of dental screening services using dental encounter data between urban Medicaid Reform counties and Non-Reform counties could not be assessed because health plans in the Non-Reform counties are not required to provide dental services to their recipients; thus, dental encounter data was not available for the Non-Reform counties.

For the urban Medicaid Reform counties, enrollees who did not earn EBA credits had the fewest number of dental encounters in all years. Moreover, as shown in Table 75, among the earners, DY2 “high” earners had the greatest average number of encounters in DY2 but in DY3 through DY7 the “medium” earners had the greatest number of dental screening encounters. Table 75: Domain iii.d: Claims Data—Use of Dental Screening Services by EBA Earning Level in Urban Reform Counties (Per Member Per Year)

Demonstration Year (DY) Medical Dental Screening Claims

None (Mean)

Low (Mean)

Medium

(Mean) High

(Mean) 2 0.06 0.10 0.24 0.46

3 0.07 0.16 0.39 0.19

4 0.06 0.12 0.74 0.33

5 0.06 0.12 0.91 0.63

6 0.07 0.13 0.79 0.70

7 0.05 0.12 0.71 0.62

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Summary: Domain iii.d Domain iii.d compared the use of cancer screenings in urban Reform and urban Non-Reform counties, as well as the use of cancer screenings and dental screening services by EBA earning categories within urban Medicaid Reform counties. During the study period, enrollees in Medicaid Reform and Non-Reform counties had a similar number of cancer screening claims and cancer screening encounters per member per year. Within urban Medicaid Reform counties, “high” EBA earners had more claims and encounters for cancer screening services than non-earners and earners in the “low” and “medium” groups. For dental screening services, enrollees in the urban Medicaid Reform counties that were “medium” earners had more dental screening encounters per member per year than “low,” “high,” and non-earners.

Domain iii.e: To what extent does participation in the Enhanced Benefits Account (EBA) program vary by characteristics of enrollees (e.g., race/ethnicity, chronic il lness, and plan type)?

Table 76 presents the demographic characteristics of the Medicaid enrollee population in urban Medicaid Reform counties by EBA earning category, summarized between DY2 and DY7. The unit of analysis is a person year, such that one enrollee could be counted as many as six times if they were enrolled all six years. During the period, about 58% of the person years are for Medicaid enrollees in Medicaid Reform counties who were females, 46% who were black, 24% who were white, and 18% who were Hispanic. About 62% percent of the person years are for individuals who resided in Broward County and 38% who resided in Duval County. About 20% of the person years are for individuals who were age 3 or younger and 45% of the person years were for individuals who were over the age of 18. Age distributions varied across EBA earning category. For example, 59% of high earner person years were for beneficiaries 3 years and younger compared to 26% of low EBA person years who were 3 years or younger. Further, 23% of high earner person years were over 18 years of age, while 17% of medium earner person years were for enrollees over 18 years. The majority (86% of the person years) were eligible for Medicaid through the TANF program, while 14% were eligible for Medicaid through the SSI program. There was some variation in eligibility category by EBA earning level. Among person years for high EBA earners, 74% were TANF recipients. However, among low EBA earner years, 87% were for TANF beneficiaries.

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Table 76: Domain iii.e: Characteristics of the Enrollee Population by Person Year and by EBA Earning Level (DY2 – DY7)

Variable None ($0) N (%)

Low ($1 to $50) N (%)

Medium ($51 to $124)

N (%)

High ($125) N (%)

Total N (%)

Gender

Female 1,037,502 (60.0) 459,760 (55.1) 211,108 (54.7) 32,670 (54.1) 1,741,040 (57.8)

Male 692,886 (40.0) 373,678 (44.8) 175,204 (45.4) 27,756 (45.9) 1,269,524 (42.2)

Race/Ethnicity

White 477,628 (27.6) 172,502 (20.7) 73,430 (19.0) 11,607 (19.2) 735,167 (24.4)

Black 769,669 (44.5) 407,867 (48.9) 175,062 (45.3) 26,240 (43.4) 1,378,838 (45.8)

Hispanic 295,745 (17.1) 144,413 (17.3) 79,430 (20.6) 12,286 (20.3) 531,874 (17.7)

Other 187,346 (10.8) 108,656 (13.0) 58,390 (15.1) 10,293 (17.0) 364,685 (12.1)

Age

0 to 3 175,135 (10.1) 214,960 (25.8) 164,381 (42.6) 35,519 (58.8) 589,995 (19.6)

Over 3 to 10 238,874 (13.8) 236,415 (28.4) 100,041 (25.9) 6,851 (11.3) 582,181 (19.3)

Over 10 to 18 267,683 (15.5) 163,469 (19.6) 57,245 (14.8) 4,307 (7.1) 492,704 (16.4)

Over 18 1,048,696 (60.6) 218,594 (26.2) 64,645 (16.7) 13,749 (22.8) 1,345,684 (44.7)

County

Broward 1,079,521 (62.4) 489,429 (58.7) 248,663 (64.4) 43,623 (72.2) 1,861,236 (61.8)

Duval 650,867 (37.6) 344,009 (41.3) 137,649 (35.6) 16,803 (27.8) 1,149,328 (38.2)

Eligibility

TANF 663,705 (86.0) 553,050 (86.7) 248,236 (85.9) 33,560 (74.0) 1,498,551 (85.9)

SSI 107,960 (14.0) 84,762 (13.3) 40,613 (14.1) 11,801 (26.0) 245,136 (14.1)

A logistic regression model was used to examine the association between EBA credit earning and Medicaid Reform beneficiaries’ characteristics. EBA program participation level was determined based on beneficiaries’ total EBA earnings from DY2 to DY7. The following characteristics were considered in the model: gender (female as reference category), race (white as reference category), age (0 to 3 as reference category), Reform county (Broward as reference category), and eligibility program (SSI as reference category). Due to policy changes in the EBA program that occurred during the study years (see Appendix H), it was necessary to take the difference between years into consideration when we attempted to study the determinants of EBA program participation. Thus, the logistic regression model included a Reform year variable (with DY2 as the reference category) to examine the impact of each year on enrollees’ EBA program participation (Table 77). Results suggest that DY3 and DY4 are associated with significantly lower odds of earning EBA credits than DY2, while DY5, DY6, and DY7 are associated with significantly greater odds of earning EBA credits compared to DY2. Furthermore, DY7 is associated with nearly three times greater odds of earning EBA credits compared to DY2. This effect could be potentially explained by the policy change at the end of DY2 that reduced the credits associated with accessing primary care appointments. After adjusting for year effects, the eligibility variable (whether TANF or SSI) was a significant predictor in the regression model. TANF-eligible enrollees had 2.391 greater odds of earning EBA rewards than SSI-eligible enrollees. This may be because childhood preventive care comprised a high percentage of total EBA earnings and children have Medicaid eligibility through TANF. Blacks, Hispanics and individuals who were grouped in another race category were significantly more likely than whites to earn credits. Finally, compared to children 3 years and younger, older beneficiaries were significantly less likely to earn credits.

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Table 77: Domain iii.e: Association between Likelihood of EBA Earnings and Enrollees’ Characteristics Effect Odds Ratio 95% Confidence Limits

Demonstration Year (DY2)

DY3 0.888*** 0.877 0.900

DY4 0.980*** 0.967 0.993

DY5 1.270*** 1.253 1.288

DY6 1.310*** 1.292 1.329

DY7 2.915*** 2.859 2.972

Gender (Female)

Male 1.077 0.941 1.233

Race/Ethnicity (White)

Black 1.070** 1.007 1.137

Hispanic 1.098*** 1.029 1.172

Other 1.553*** 1.477 1.632

Age Group (0–3)

Over 3 to 10 0.725*** 0.711 0.739

Over 10 to 18 0.812*** 0.786 0.838

Over 18 0.358*** 0.341 0.375

County (Broward)

Duval 1.021 0.907 1.148

Eligibility (SSI)

TANF 2.391*** 2.354 2.429

**Predictor statistically significant at 0.05 level. ***Predictor statistically significant at 0.01 level.

Summary: Domain iii.e Between DY2 and DY7, about 58% of EBA enrollees were females, 46% were black, and about 62% lived in Broward County. The majority of EBA earners had Medicaid eligibility through TANF. There were statistical differences in enrollees’ tendency to earn EBA credits by Reform year. In DY3 and DY4 they had significantly lower odds of earning rewards than in DY2, while in DY5, DY6, and DY7 they had significantly greater odds of earning rewards than in DY2. TANF-eligible individuals had significantly greater odds of earning rewards than SSI-eligible individuals.

Domain iii.f: Is there a difference in rates of avoidable hospitalizations and emergency department use among Enhanced Benefits Account (EBA) users (high, medium, low) and non-users?

Prevention Quality Indicators (PQIs) developed by the Agency for Health Care Research and Quality are a set of measures that are used to identify quality of care for avoidable hospitalizations. ICD-9 codes were used to identify these PQI conditions within the study data set to analyze the difference in rates of

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avoidable hospitalizations. 4 Emergency department visits were identified from outpatient data. The differences in utilization rates of avoidable hospitalizations and emergency department visits were compared across groups of beneficiaries with different levels of EBA earnings (high, medium, low, and non-earner). Since PQIs indicate episodes of medical care that could have been prevented, lower rates are more desirable. Overall, enrollees had on average less than one episode of avoidable hospitalizations per member per year in each of the study years, irrespective of their EBA earning level (Table 78). However, enrollees in the low and medium EBA earning categories had the lowest number of avoidable hospitalizations (0.01 to 0.04). Overall, individuals in the non-earner group and the high earner group had the highest mean number of avoidable hospitalizations. Table 78: Domain iii.f: Rates of Avoidable Hospitalizations by EBA Earning Level in Urban Reform Counties (Per Member Per Year)

Demonstration Year (DY) Avoidable Hospitalizations None

(Mean) Low

(Mean) Medium (Mean)

High (Mean)

2 0.16 0.03 0.03 0.05

3 0.22 0.03 0.04 0.20

4 0.21 0.03 0.03 0.21

5 0.22 0.03 0.02 0.17

6 0.20 0.03 0.02 0.07

7 0.06 0.01 0.01 0.03

4 The analyses of avoidable hospitalizations are based on the Prevention Quality Overall Composite that includes

admissions for one of the following conditions: diabetes with short-term complications, diabetes with long-term complications, uncontrolled diabetes without complications, diabetes with lower-extremity amputation, chronic obstructive pulmonary disease, asthma, hypertension, heart failure, angina without a cardiac procedure, dehydration, bacterial pneumonia, or urinary tract infection (Agency for Healthcare Research and Quality, 2013).

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On average, EBA enrollees in urban Medicaid Reform counties had less than one emergency department (ED) visit per person per year in each of the study years, irrespective of their EBA earning level (Table 79). However, enrollees in the low and medium EBA earning categories had the lowest number of ED visits (0.03 to 0.08), while the non-earners and high earners had the highest average number of ED visits. Among these two groups, there was a substantial decrease in the mean number of visits from DY6 to DY7. Table 79: Domain iii.f: Rates of Emergency Department (ED) Visits by EBA Earning Level in Urban Reform Counties (Per Member Per Year)

Demonstration Year (DY) ED Visits

None

(Mean) Low

(Mean) Medium (Mean)

High (Mean)

2 0.26 0.07 0.08 0.10

3 0.35 0.08 0.08 0.35

4 0.36 0.08 0.07 0.39

5 0.37 0.08 0.06 0.32

6 0.36 0.08 0.06 0.14

7 0.12 0.04 0.03 0.07

Summary: Domain iii.f This domain analyzed the rates of hospitalizations that could have been avoided with better preventive care and the rates of ED visits among the EBA enrollees by credit-earning level. Overall, enrollees had less than one episode of avoidable hospitalization and ED visit per person per year, respectively. Enrollees in the low and medium EBA earning levels had the lowest rates in each of the six years.

Domain iii.g: What is the effect of Enhanced Benefits participation on total expenditures ?

Enrollees’ total expenditures in urban Medicaid Reform counties were calculated using Medicaid claims data; the figures in Table 80 represent averages per person per year by EBA earning category. This assesses the trend in enrollees’ total Medicaid expenditures following the implementation of the EBA program. Overall, the total health expenditures increased over the study years. The greatest change was noted among “high” earners, whose expenditures more than doubled from DY2 to DY3, then decreased only slightly in the next four years. “High” earners had the greatest expenditures of all four earning categories, followed by “non-earners,” “medium” earners, and “low” earners. Table 80: Domain iii.g: Total Health Expenditures by EBA Earning Level in Urban Medicaid Reform Counties (Per Member Per Year)

Demonstration Year (DY)

Total Health Expenditures ($)

None

(Mean) Low

(Mean) Medium (Mean)

High (Mean)

2 5,545 1,755 2,219 3,509

3 5,512 1,868 2,738 7,087

4 5,593 1,857 2,756 6,153

5 5,907 2,013 2,516 6,205

6 5,417 2,102 2,627 5,466

7 2,644 1,748 2,217 4,068

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Summary: Domain iii.g Enrollee health expenditures were highest for the “high” EBA earner level and non-earners. Expenditures for non-earners were fairly steady in DY2 through DY6 and decreased by approximately half from DY6 to DY7. Expenditures for enrollees in the “high” earner category doubled from DY2 to DY3 but steadily declined in DY3 through DY7. Health expenditures for “low” and “medium” earners were fairly constant at approximately $2,000 and $2,500, respectively, throughout the demonstration.

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General Findings: Domain iv

Florida International University (FIU) conducted the analyses associated with Domain iv. The Domain iv analyses measure the effectiveness of Reform in serving as a deterrent against fraud and abuse and in maintaining oversight of the managed care plan policies and procedures that deter fraud and abuse. The evaluation utilized a mixed-methods approach (Driscoll, Appiah-Yeboah, Salib and Rupert, 2007; Lieber, 2009), that is, the design and analyses include both qualitative and quantitative methods. The qualitative study included: 1) content analysis of the fraud and abuse plans submitted to the Bureau of Medicare Program Integrity (MPI) by managed care plans and 2) analyses and integration of supplemental interview data. The quantitative analyses utilized secondary data obtained through the 1) Annual Anti-Fraud and Abuse Activity Report (AFAAR) and 2) the Quarterly Anti-Fraud and Abuse Activity Report (QFAAR) that are required from each managed care plan by the Bureau of MPI. The general findings for Domain iv are presented by the type of analyses utilized, including the qualitative and quantitative analyses. The complete report as submitted to AHCA is included in Appendix J.

Integrated Qualitative Analyses

This section presents the integration of the content analysis of the fraud and abuse plans with the more detailed information obtained through the semi-structured interviews. The narrative below follows five major themes or categories identified through the iterative coding of excerpts from the fraud and abuse plans using Dedoose content analysis tool:

1. Detection and Prevention Tools 2. Education and Training 3. Internal and External Investigations 4. Internal and External Reporting 5. Corrective Actions

The first four categories align with the Agency’s contractual requirements in F.S. 409.91212. The fifth category, Corrective Actions, captures information consistent with Agency and managed care plan policies and procedures that follow the identification of suspected and/or confirmed overpayments, fraud and abuse. The level of detail regarding the specific policies and procedures within each of the categories differed across the individual anti-fraud plans; therefore, examples provided below generally derive from plans providing more specific information. Examples should be considered illustrative but not exhaustive, as the descriptions provided do not signify exclusive policies. As such, an attempt was made to pull the descriptive examples from each of the anti-fraud plans. The examples demonstrate differences in the conveyance of the written policy but not necessarily in application or practice. In addition, the categories themselves are not exclusive as tools may be utilized for multiple purposes.

Detection and Prevention Tools Under the theme of Detection and Prevention Tools, the coded excerpts from each anti-fraud plan describe the policies and procedures designed to prevent and detect waste, abuse, and fraud among their providers, members, and employees as well as vendors and subcontractors. Generally defined, prevention refers to “processes to avoid fraud and/or abuse prior to payments” while detection refers

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to “processes to monitor, detect, or investigate fraud after an instance of fraud and/or abuse has occurred” (Strike Force, 2013, pp. 45-46). Detection can result from computerized detection tools, leads from incoming complaints and referrals, information from other regulatory agencies, newspaper articles and advertisements, and Explanation of Medical Benefits forms, as well as from data mining and audit reviews (AHCA and Attorney General of the State of Florida, 2013). The distinction between prevention and detection is not absolute. In the semi-structured interviews as well as in the content analysis, some plans noted the overlap of the two. As stated by one Compliance Officer, “detection leads to prevention.” While some plans discuss specific tools that are clearly aimed at either` prevention or detection, the “commingling” of detection and prevention was evident. For example, retrospective analytics and data mining often identify outliers and practices that point to a need for preventive intervention and/or educational initiatives. Similarly, retrospective approaches can also identify predictive modeling to prevent future fraudulent activities. All plans, as required, include an anonymous compliance hotline (toll-free phone number and/or online forms) as a means to obtain tips. In addition to their own hotlines, plans also refer to AHCA’s Consumer Complaint Hotline as well as to the Florida Attorney General’s Hotline. The hotlines serve as a referral tool for both internal (i.e., employees) and external (i.e., members, providers, vendors/suppliers, contractors, other third-persons) individuals. At least one plan, Plan C, goes a step further to encourage member involvement in detection by offering a reward for reporting suspected fraud and abuse. Beyond hotlines, a few of the anti-fraud plans mention notifications and mailings as detection tools focused on their members. For instance, mailing an Explanation of Medical Benefits (EOMB) provides a means of members “being informed of their recent utilized services through the plan’s explanation of benefits, allowing them an opportunity to question unauthorized services,” for Plan A. Plan B states, “Use of this card by any person other than the member is fraud” on member ID cards as a means of fraud prevention. Other primarily preventive strategies include monthly checks of providers, subcontractors, vendors and employees against federal and state excluded parties systems. Many plans reference (re)credentialing procedures with particular attention to identifying entities excluded from participation in federal healthcare programs. Plan H, for example, states it “actively monitors various government reports besides federal and state reporting systems for listings of suspended or terminated entities.” Plans include pre-authorization or prospective utilization management by either manual or electronic review as a means to control potential fraud, waste, or abuse. Plan I mentions pre-payment verification systems that check the accuracy of benefits and eligibility before a payment is approved. The managed care plans also utilize provider profiling based on past patterns of falsified or overstated claims including up-coded levels of service, misrepresented medical information, and charging for services not rendered. While some anti-fraud plans provide extensive descriptions of their fraud and abuse analytical detection strategies and tools, other plans are not as detailed. The majority of the information regarding these detection tools focuses on medical utilization review, auditing, monitoring, and other types of data analysis and data mining. Some simply mention that the reviews of payments for non-covered or non-rendered services, reviews of falsified encounters, and reviews of overpayments/upcoding are conducted by a Registered Nurse (RN). Others detail more specific strategies targeting pre-payment and post-payment utilization reports, provider billing pattern reviews, reviews of gender- and age-appropriate billing, reviews of multiple-item billing and bundled/unbundled billing, “blitz” audits, and pharmacy reports.

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Following the feedback from the pilot interviews, the second round of semi-structured interviews specifically asked for information regarding the use of organizationally developed and/or commercially purchased analytical software in fraud detection efforts. Information obtained from interviews with the Compliance Officers as well as data from Quarterly Fraud and Abuse Activity Reports (QFAARs) and Annual Fraud and Abuse Activity Reports (AFAARs) demonstrated that the use of data analysis methods, including commercially available anti-fraud software as a tool for prevention and detection is relatively more common among large and some medium managed care plans. In alignment with the CMS and Agency area of specific interest, the Compliance Officers were asked to provide their perception about the most effective policies and procedures intended to deter and identify fraud and abuse. Respondents from all medium and large plans either had a system in place, or were in the process of implementing one, to measure the effectiveness of their efforts to control program integrity. A variety of techniques and methods were identified by the respondents, including tracking soft dollar savings, periodic reviews and updates to their policies in response to the previous year’s activities, monitoring volume of cases, time constraints on cases, amounts recovered from providers, internal audit outcomes, and monthly audits of their investigators. Only one small plan stated that they do not formally measure the effectiveness of their policies and procedures related to fraud detection efforts. While the methods for measuring the effectiveness of policies and procedures varied among plans, there was general agreement that data analysis and the use of pre-payment and post-payment fraud detection tools were the most effective methods for both prevention and detection.

Education and Training Under the theme Education and Training, the content analysis concentrated on the anti-fraud plan policies, procedures, and activities that seek to educate employees, providers, members, and others. All managed care plans have handbooks, manuals, and guidelines that contain information regarding fraud and abuse detection and prevention policies and procedures. These materials are made available to employees, members, and providers electronically, physically, or both. Internal education of employees must contractually occur within 30 days after hiring, and is followed by periodic training (at least annually). The majority of anti-fraud plans, however, disclosed that initial education occurred in conjunction with new employee orientation. Further commitment to the educational process is evident through references to on-going training, formal tracking of education, and communicating timely updates on anti-fraud policy changes to relevant parties, including employees, members, providers, and vendors, through newsletters. At least one managed care plan (Plan H) not only tracks training but goes further by stating that they require their employees to pass a test with a perfect score. Managed care plans utilize a variety of educational tools and methods including lectures, videos, self-study, conferences/seminars, and interactive computer-based training. Additionally, specific educational materials are developed and available for differing groups, i.e., members, employees, providers, and vendors. For example, Plan B features member and provider newsletters as a means to provide educational updates, and Plan C mentions specific training for employees within specialized departments such as Compliance and Finance. Similarly, Plan D mentions, “specialized sessions specific to the individual risk area.” Additionally, some plans mention the inclusion of disciplinary guidelines and whistleblower protection as part of their educational program.

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Provider education receives particular attention. Examples include sending provider education letters, holding post-training Q & A sessions, and conducting group as well as one-on-one training sessions. The importance of provider education is highlighted by mention that non-compliant providers (30 days past anniversary date of becoming active and who have not received training) receive one-on-one fraud and abuse training (Plan C). The importance of provider education was also emphasized by some Compliance Officers during the interviews. Healthcare fraud and abuse training is often supplemented with training in business ethics and/or specific training based on specific needs, for example, differentiated education tailored for specific departments (e.g., claims, credentialing, member services) or Plan D’s “specialized sessions specific to the individual risk area.” In addition to internal educational programs, some plans also mention that they encourage individuals to seek more education through professional associations or industry training. Responsibility for the fraud and abuse education of vendors and suppliers is assumed internally by some fraud and abuse plans while not by others. One anti-fraud plan (Plan D) states, “delegated vendors conduct their own training programs related to fraud and abuse.” While all managed care plans utilize education and training as a method of prevention and deterrence of waste, fraud, and abuse, most also mention education incorporated within corrective actions. The utilization of education in corrective actions is addressed below. Semi-structured interviews addressed the frequency and methods of interactions with providers. Responses focused mostly on communications that target education and updating of policies and procedures. To do so, the communication between both parties occurs by mailed correspondence such as newsletters, by phone or email, or during an audit process. In addition to planned communications and education sessions, “as needed” communication of a more personal nature occurs in response to detection of aberrant activity. As one Compliance Officer explained (Plan E), “I may be called in to provide guidance and meet directly with a provider when requested by the SIU [Special Investigative Unit]…, or when necessary to resolve a compliance matter.”

Internal and External Investigations The theme Internal and External Investigations encompasses content analysis excerpts that identified reference to internal investigations, which refer to those that occur within the managed care plan and external investigations, which involve an outside entity conducting the entire or part of an investigation. The anti-fraud plans provided information regarding the internal organizational structure, the composition of their investigative team, and a description of their investigative operations of each of the sample of managed care plans to varying degrees of specificity. All managed care plans must have a Special Investigative Unit (SIU) and/or a Compliance Officer that is ultimately responsible for conducting the investigation and overseeing suspected misconduct in a timely, confidential, and reasonable manner. Some managed care plans employ their own investigators within a SIU, and others mention using subcontractors to conduct the investigations (e.g., Plan G) or to aid with the plan’s internal SIU’s investigative work (e.g., Plan H). If a plan chooses to subcontract investigations, filing of information on the subcontracting party must be provided to the Bureau of MPI at least 60 days before contract execution (2012 – 2015 Core Contract).

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The steps involved in an internal investigation, as outlined in some anti-fraud plans, include detection and referral, initial assessment to determine the merit of an allegation, development of investigative strategy, information gathering, evaluation of evidence, determination of corrective action and recommendations, and civil/criminal proceedings. Procedures for tracking and documenting of investigations vary across managed care plans. While some use manual systems, others utilize computer software programs. As mentioned under the theme detection and prevention tools, the use of data analysis is an increasingly important tool in detection and deterrence of fraud and abuse. Analytical software is also becoming an important tool in the investigation process, most commonly among large and medium managed care plans, as its use requires ample resources. Software tools can both assist in the investigative process, and serve as tracking mechanisms for ongoing investigations. One of the larger managed care plans (Plan I) noted success in utilizing software to differentiate between billing errors and suspected fraud and abuse. Another plan (Plan H) prepares detailed internal reports that show all of SIU’s activity as part of their tracking and documenting efforts to prevent fraud and abuse. Other medium managed care plans mention using case management systems to track and document investigative cases. Smaller managed care plans, on the other hand, tend to maintain manual logs and securely store all physical documentation of fraud and abuse. All plans highlight collaborative efforts across various departments internally during investigations and emphasize cooperation with state and federal regulatory agencies as well as law enforcement agencies in the case of external investigations. External investigations may result from internally reported incidents to the appropriate agencies or may be initiated externally.

Internal and External Reporting Under the theme Internal and External Reporting, information was compiled regarding the internal and external policies for the reporting of suspected fraud, waste, or abuse. In accordance with the Agency’s requirements, all nine managed care plans in the sample require their members, providers, and employees to report any suspected fraud and abuse. Internal reporting policies focus on reporting within the managed care plan to senior management or the governing body, whereas external reporting policies focus on post-investigative reporting to government (e.g., Bureau of MPI, CMS) or law enforcement agencies. For external reporting, each anti-fraud plan outlines the required procedures for reporting suspected or confirmed fraud and abuse to the appropriate regulatory or law enforcement agencies established through contractual and/or regulatory guidelines. Most managed care plans specify the contractual time requirement (within 15 calendar days of detection). Some of the managed care plans mention required periodic internal reports that correspond to the required time for submission of their external reports, e.g., the AFAAR and QFAAR, to appropriate oversight agencies. While all plans are required to submit the annual and quarterly reports to the Bureau of MPI, Plan E indicates external reporting of suspected fraudulent activities to the state’s insurance department as well. Information on the organization as well as specific policies for internal reporting vary widely across plans as the internal policies on reporting are not subject to regulatory guidelines. Some plans describe internal compliance committees to handle reports of fraud and abuse—notwithstanding that the Compliance Officer can always go directly to higher administration. These internal committees often receive and compile any required internal reports that may be submitted to the managed care plan’s

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executive team and/or governing body. Internal reports are also, generally, the basis for the submission of external reports to appropriate oversight agencies. As discussed above under Detection and Prevention Tools and Internal and External Investigations, there appears to be a movement towards online reporting both internally and externally. Indeed, the 2012 – 2015 Health Plan Model Contract states that “unless otherwise specified, all reports are to be submitted electronically.”

Corrective Actions The final theme, Corrective Actions, includes the actions taken upon confirmation of any misconduct in terms of fraud and abuse. Corrective actions can take the form of disciplinary steps or termination in the case of employee misconduct. In the case of providers, vendors/suppliers, and contractors, corrective actions may first include education and training to address the underlying problem and prevent future non-compliance as long as it is only a billing error. If subsequent monitoring reveals that non-compliance still continues, then more formal corrective actions follow such as possible suspension of payments, recovery of losses through repayments, and eventually termination or amendment of the contract. Depending on the severity of the misconduct, civil litigation and additional reporting to appropriate authorities and law enforcement can occur (as discussed under the Internal and External Reporting theme above). Some anti-fraud and abuse plans describe the time frames for specific achievements in corrective action plans and some provide further specifics in terms of types of corrective actions and how they are enforced. For example, Plan C states that “Monitoring and evaluation of a corrective action(s) is usually done within six (6) months following the implementation of a corrective action(s)”. Plan I also states that indication of fraudulent or abusive practices can trigger an “in-depth retrospective review” of providers. These providers are also automatically placed on pre-payment reviews in order to minimize further potential damages. Another plan (Plan F) lists additional corrective actions such as referral of non-compliant providers to the medical board or to network management for disciplinary action while another plan (Plan G) allows for voluntary withdrawal of providers to avoid formal sanctions. Corrective actions for members do not receive significant attention in the majority of the anti-fraud plans analyzed. However, all plans state that confirmed member misconduct can result in claim suspension or denial. Plan I also mentions using enrollee education as an initial corrective action.

Summary of Qualitative Findings As the above discussion indicates, common themes consistent with the statutory requirements emerged in the content analysis of the anti-fraud plans from the sample of nine Medicaid managed care plans. Semi-structured interviews added depth and specificity to the themes in areas of interest identified for inclusion in the evaluation by CMS and the Agency, for example, types and frequency of interactions with providers, and tracking and the effectiveness of fraud and abuse policies and procedures. Across the anti-fraud and abuse plans analyzed, the amount of detail provided varied greatly; however, all met the statutory requirements of the State of Florida and demonstrated a strong commitment to extensive efforts to deter and detect fraud, waste and abuse by providers and members.

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The purpose of the qualitative analysis was two-fold. First, to assist in evaluating the Agency’s oversight of managed care plans, including adapting efforts to the surveillance of fraud and abuse within the managed care system. Included in this purpose is the identification of key policies and procedures utilized in detecting and deterring fraud and abuse by providers and members. The second purpose was to determine if managed care plans displayed any key differences that might identify differential impact on measures of fraud and abuse. Two other potential streams of inquiry could not be pursued. Due to lack of historical data from managed care plans, changes in policies and procedures over time could not be investigated. Nor was data available to assess differences in policies and procedures for detecting fraud in fee-for-service or other commercial or Medicare managed care plans. While the semi-structured interviews sought to obtain this information, the responses indicated that the organizations either offered only Medicaid managed care plans or essentially used the same policies and procedures for all plans administered by their organizations. The State of Florida, through the Bureau of MPI and consistent with the Medicaid Health Plan Standard Contract (AHCA, n.d., a), maintains strict regulatory control oversight of managed care plans and the anti-fraud policies and procedures utilized by these plans in the deterrence, detection, and surveillance of fraud and abuse within the managed care system. The content analysis of anti-fraud plans that each Medicaid managed care plan must submit annually to the Bureau of MPI demonstrates that all plans conform to the standards of the contract and rigorously adhere to the reporting requirements. Due, at least in part, to the managed care plans’ adherence to the strict requirements established in the Standard Contract, no major differences in policies, procedures and/or compliance activities were found across the nine managed care plans included in the sample. Therefore, no attempt to identify differential impact of any distinguishing policy on fraud and abuse measures could be assessed. The only potential difference explored was the utilization of more sophisticated data analytical software packages by larger managed care organizations, that is, those with larger numbers of enrollees. It is important to note that the description of claims surveillance and reviews in anti-fraud plans conveys the complementary nature of prospective and retrospective strategies rather than substituting one for another. Retrospective data mining combined with clinical assessments can help to identify aberrant billing patterns, and these in turn can be used in predictive modeling to develop algorithms to detect and prevent future fraudulent practices and payments. As such, findings of past investigation and recovery efforts feed data analytics, which assist with future auditing, monitoring, and detection capabilities. At the same time, prospective detection and audit efforts can help identify opportunities for retrospective investigation of potential violations.

Quantitative Analyses – Trends from Fiscal Years 2010 – 2013

This section presents the findings from the quantitative analyses of data submitted to the Bureau of MPI in the AFAAR and QFAAR reports from the managed care plans. Based on the findings from the qualitative analyses, the trends were explored to determine if the size of plans, linked to more sophisticated data analytic tools, had an impact on the detection and deterrence of fraud and abuse.

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Trends in AFAAR Data The Agency provided AFAAR data which contained reported overpayments identified and recovered, fraud and abuse identified and recovered, and number of referrals for the nine managed care plans in the sample for FYs 2010 – 2011; 2011 – 2012; and 2012 – 2013. These data were examined to identify trends over time in order to provide a proxy measure of the impact of participation in the demonstration reform program on the deterrence and detection of fraud and abuse. Based on the growing popularity of data mining, business analytics, and analytical fraud detection software packages currently available, and upon the qualitative data finding that larger plans were more likely to use sophisticated software as a detection tool, the AFAAR data was assembled by size of Medicaid managed care enrollment: the small category included enrollment under 10,000 members (Plans A and G); the medium category included enrollment greater than 10,000 but less than 100,000 (Plans C, D, E and H); and the large category included enrollment greater than 100,000 (Plans B, F and I). In addition, the AFAAR data includes a descriptor for contract type, Reform and Non-Reform. Therefore, the data were examined in concert with reported contract type as well as size by enrollment. Table 81 provides a comparison of small, medium and large managed care plans distinguished between Reform (top panel) and Non-Reform (bottom panel) contract types. The direction of changes over time during this period are provided for each of the measures included in AFAAR. The “+” symbol indicates increases and the “-” symbol indicates decreases in proportion of reported measures over time. Table 81: Trends in Overpayments, Fraud, and Abuse Over the Period of FY 2010 – 2011 and FY 2012 – 2013, by Plan Size

Overpayments Identified

(1)

Overpayments Recovered

(2)

Fraud & Abuse

Identified (3)

Fraud & Abuse

Recovered (4)

Total Identified

(1+3)

Total Recovered

(2+4)

Number of

Referrals

Reform

Small plans

G - - -

Medium plans

C - - +

H + + + + + + +

Large plans

B - - - - - - -

F - - - - - - -

Non-Reform

Medium plans

D + + + +

E - - + - - +

H + + + + + + same

Large plans

B - - - - - - same

F + + + - + + +

Notes: Small, medium, and large plans are identified by the number of Medicaid enrollment in Florida state 10,000; 10,000<enrollment<100,000; and enrollment≥100,000, respectively.

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In addition to the measures listed above, a total amount identified for recovery and total amount recovered was created by combining the overpayment with fraud and abuse figures for each managed care plan. The empty cells in Table 81 are due to missing data. While a cursory review of Table 81 suggests that larger plans (those with higher enrollments) provide more consistent and complete reports when compared to smaller health plans, this assumption may not be entirely accurate. The reason for missing data could not be determined and empty cells may result from lack of incidents in the smaller plans or a plan not participating in the demonstration program during earlier years. Based on the total numbers, three general trends can be detected over the time period of 2010 – 2013. First, overpayments as well as fraud and abuse amounts have decreased over time under the Reform contracts with the sole exception of plan H (see the top panel of Table 81). Second, this decrease was particularly notable for larger and more established plans, such as, Plans B and F. Taken together, these two plans account for approximately two-thirds of the total enrollment among the nine plans in the sample, and therefore, are critical in the analysis and interpretation of findings. Third, these first two trends (decreased amounts in overpayments as well as fraud and abuse, and the notably larger decreases in the larger plans) were not observed when examining the data for the Non-Reform contracts. While these overall trends suggest that the managed care plans under the Reform contracts are indeed improving detection and deterrence of fraud, waste, and abuse, there are several limitations that must be taken into account when examining these trend analyses. First, case investigations often extend across fiscal years. Second, whatever the magnitude, increases and/or decreases in overpayments or in fraud and abuse identified can be due to a rise (decline) in fraudulent activities or detection efforts or a combination of the two. The analyses cannot determine causality due to the multiplicity of factors that may impact the activity and the amounts. As such, reductions in these measures over time should not be assessed in isolation but considered as one of several factors that may determine the success of detection, prevention and recovery efforts occurring during the years of the demonstration program.

Trends in QFAAR Data The Agency also provided QFAAR data which offered the opportunity to examine trends over time in terms of the detection tools utilized in identifying fraud and abuse cases. As discussed above, the plans were grouped into small, medium, and large categories. When reviewing the detection tools reported in the QFAAR data, the researchers identified which detection tools would fall under a broad data analysis category, including those that utilized data mining, data analytics, data matching, and pre- and post-payment reviews, sometimes with the use of specialized software. This grouping of analytical tools was then compared over time, i.e., FY 2010 – 2011 and FY 2012 – 2013, to explore for trends in frequency of use for both Reform and Non-Reform contract types. To assure the confidentiality of the managed care plans as well as to provide accurate comparative data, the number of incidents in each have not been reported, but only percentage changes over time. The trend analyses for the utilization of data analysis in detection techniques demonstrate a clear difference between the Reform and Non-Reform contract types. The overall proportion of fraud and abuse cases identified using data analysis tools, decreased from 51% in FY 2010 – 2011 to 30% in FY 2012 – 2013. However, this overall finding conceals the differential between Reform and Non-Reform contract types. In an effort to allow for a meaningful comparison, it is important to evaluate changes over time within each plan and contract type.

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Under the Non-Reform contract type, from FY 2010 – 2011 to FY 2012 – 2013, all plans have decreased the proportion of fraud and abuse cases identified using data analysis tools with the exception of Plan I. This proportion went down from 37% to 15% for plan B, from 63% to 21% for plan D, from 41% to 18% for plan E, from 67% to 41% for plan F, and from 64% to 56% for plan H. Figure 5 displays all these plan-specific proportional changes within the Non-Reform Contracts. Plan I was the only Non-Reform Contract type that increased the proportion of fraud and abuse cases detected using data analysis tools, showing an increase from 48% to 80%. Interestingly, the increase in Plan I for use of data analytical tools parallels the findings in the content analysis of fraud and abuse plans as well as the supplemental interviews that suggests that large plans are more likely to incorporate sophisticated data analysis in their efforts to detect and deter fraud and abuse. Figure 5: Proportion of Fraud and Abuse Cases Identified Using Data Analysis Tools by Contract Type

Examination of the trend of reported utilization of data analysis tools for identification of fraud and abuse cases by plans for the Reform contract type demonstrated increased adoption of these tools. Here, again, however, there was one exception, Plan H. Plan B experienced an increase from 31% to 50%, Plan C experienced an increase from 0% to 8%, and Plan I experienced an increase from 54% to 82% (Figure 5). Plan H, which is categorized as a medium plan based on Medicaid managed care enrollment, reported a decrease from 79% to 56% for the proportion of fraud and abuse cases identified using data analysis tools. Coincidentally, Plan H is also the only plan under the Reform contract type that experienced higher overpayments as well as fraud and abuse amounts during this the same time period (Table 81).

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Summary of Quantitative Findings Trends examined in the amount of reported overpayments and reported fraud and abuse by the Medicaid managed care plans included in the sample indicate overall that these numbers have decreased during the FYs included in the analyses. Accompanying these decreases is a general increase in the proportion of cases identified under the Reform contract types through the utilization of detection tools incorporating data analytical techniques such as data mining, data analytics, data matching, pre- and post-payment reviews, and the utilization of specialized fraud and abuse software. While a causal relationship cannot be established due to the multiplicity of factors that may impact this change, analyses indicate that plans identified as Reform contract types are demonstrating increased use of data analytical tools over time and are increasingly effective in detecting or deferring fraud and abuse within the Medicaid demonstration program.

General Findings: Domain v

Domain v: The effect of LIP funding on the number of uninsured and underinsured, and

rate of uninsurance

This section discusses the effect of LIP funding on access to care and utilization of services for Medicaid, uninsured, and underinsured individuals. The data source used to answer the research questions related to Domain v was the LIP Milestone Statistics and Findings Report for DY7: SFY 2012–2013 (Duncan, Hall, Harman, Bell, & Kinsell, 2014).

Domain v.a: How has LIP funding improved access to care for uninsured/underinsured recipients? That is, how many uninsured and underinsured recipients recei ve services through LIP funding?

LIP Milestone Statistics and Findings Report for DY7: SFY 2012 –2013 Overall, the number of uninsured, underinsured, and Medicaid individuals served and the types and number of outpatient services furnished by non-hospital providers have increased. For hospital providers, the number of individuals with Medicaid served has increased but the number of uninsured and underinsured individuals served has decreased. The types of services provided by reporting hospital providers have not changed. Among reporting hospital providers, between DY1: SFY 2006–2007 and DY7, approximately 2.7 million Medicaid individuals and nearly 2.1 million uninsured and underinsured individuals were treated on an inpatient basis. Moreover, approximately 11.1 million individuals with Medicaid and approximately 12.2 million uninsured and underinsured individuals were served on an outpatient basis. From DY1 to DY7, the number of reporting hospital providers decreased by 52 from 158 in DY1 to 106 in DY7. Over the seven years, the average number of uninsured and underinsured individuals that received inpatient services was approximately 12,000 per reporting hospital. For outpatient services, the average number of uninsured and underinsured individuals served per reporting hospital was approximately 71,000.

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For the same time period, the number of Medicaid individuals served by hospital providers in outpatient settings increased by 116,300. Moreover, approximately 21,000 fewer Medicaid individuals were served by hospital providers in inpatient settings in DY7 compared to DY1. Furthermore, from DY1 to DY7, the number of uninsured and underinsured individuals served on an inpatient basis decreased by approximately 53,500 and the number of uninsured and underinsured individuals served on an outpatient basis by reporting hospital providers decreased by approximately 443,400. For non-hospital providers, there was an increase in the number of reporting providers, from 38 in DY1 to 63 in DY7. The number of Medicaid individuals served increased from approximately 238,250 in DY1 to approximately 522,300 in DY7. Similarly, the number of uninsured and underinsured individuals served increased by approximately 290,000, growing from 438,800 in DY1 to 729,000 in DY7. Between DY1 and DY7, there were 72 reporting non-hospital providers for the time period that furnished outpatient services to approximately 2.7 million Medicaid and 4.1 million uninsured and underinsured individuals, for a total of approximately 6.8 million Medicaid, uninsured, and underinsured patients. For the seven year period, the average total number of uninsured and underinsured individuals served per reporting non-hospital provider was approximately 57,500.

Domain v.b: What types of services are being provided and in what settings?

LIP Milestone Statistics and Findings Report for DY7: SFY 2012 –2013

Information about the type and amount of specific services furnished by providers is important in understanding the link between the provision of health services to Medicaid, uninsured, and underinsured patients and LIP payments. Services provided by hospital providers included discharges, inpatient days, emergency care services, hospital-based outpatient services, prescriptions filled, and affiliated services including primary care or preventive care clinic visits, specialist visits, surgical care furnished in a physician office, home health services, durable medical equipment, prosthetic or orthotic devices (not associated with outpatient therapy visits), and nursing home care.

As illustrated in Table 82, in DY7, hospitals that received LIP funding reported providing approximately 14 million service encounters to Medicaid, uninsured, and underinsured individuals. There were over 790,000 discharges, approximately 3.3 million inpatient days, nearly 3.3 million emergency room service encounters, and approximately 3.0 million outpatient encounters provided. Additionally, over 1.7 million encounters for affiliated services were provided and almost 2.0 million prescriptions filled. Moreover, outpatient care, diagnostic laboratory, and radiology services were provided in 93% of the reporting facilities. Approximately 75% of reporting hospitals indicated providing outpatient surgeries. Similarly, 76% reported providing outpatient facility care. Moreover, 91% of the hospitals reported providing speech, physical, and occupational therapies to patients.

Also in DY7, the Tier-Two Top 15 Hospitals provided inpatient services to approximately 145,000 individuals and outpatient services to approximately 747,800 individuals. In DY7, the average number of uninsured and underinsured individuals served that received inpatient services was approximately 4,600 per reporting hospital. For outpatient services, the average number of uninsured and underinsured individuals served per reporting hospital was approximately 27,500.

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Affiliated services, such as primary care or preventive care clinic visits, were provided by 47 of the 106 reporting hospitals. Thirty-five of the reporting facilities provided specialist visits and 25 facilities indicated providing surgical care services in physician offices. Home health services were provided by 21 hospitals, durable medical equipment was provided by eight hospitals, and prosthetic or orthotic devices (not associated with outpatient therapy visits) were provided by 21 of the reporting hospitals. Finally, ten hospitals provided nursing home care (skilled or intermediate) services.

Measures of services provided for non-hospital providers, as seen in Table 83, include primary care encounters, obstetric and gynecologic (OB/GYN) encounters, disease management encounters, mental health/substance abuse encounters, dental services encounters, number of prescriptions filled, laboratory services encounters, radiology services encounters, specialty encounters, and care coordination encounters.

In DY7, 62 reporting non-hospital providers that received LIP funding provided a total of approximately 6.7 million encounters to Medicaid, uninsured, and underinsured individuals for primary care, OB/GYN, mental health, dental, radiology services, laboratory services, disease management, care coordination, and prescription drug (Table 83).

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Table 83 illustrates that with the exception of mental health and dental health services, the number of services provided to uninsured and underinsured individuals exceeded that provided to Medicaid individuals. As shown in Table 83, in DY7, there were 2.5 million total encounters for Medicaid patients and 4.1 million total encounters for uninsured and underinsured individuals. These results highlight the important role of non-hospital providers in caring for those without insurance.

Table 82: Hospital Services and Encounters, DY7

Types of Hospital Services Number of Encounters

Discharges

Medicaid 476,460

Uninsured and Underinsured 314,570

Total Medicaid, Uninsured, and Underinsured 791,030

Inpatient Days

Medicaid 1,990,927

Uninsured and Underinsured 1,333,373

Total Medicaid, Uninsured, and Underinsured 3,324,300

Emergency Room Encounters

Medicaid 1,849,303

Uninsured and Underinsured 1,425,508

Total Medicaid, Uninsured, and Underinsured 3,274,811

Outpatient Encounters

Medicaid 1,642,891

Uninsured and Underinsured 1,341,334

Total Medicaid, Uninsured, and Underinsured 2,984,225

Affiliated Encounters

Medicaid 1,023,896

Uninsured and Underinsured 690,651

Total Medicaid, Uninsured, and Underinsured 1,714,547

Number of Prescriptions Filled

Medicaid 587,049

Uninsured and Underinsured 1,274,755

Total Medicaid, Uninsured, and Underinsured 1,861,804

Total Encounters for All Services

Medicaid 7,570,526

Uninsured and Underinsured 6,380,191

Total Medicaid, Uninsured, and Underinsured 13,950,717

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Table 83: Non-Hospital Services and Encounters, DY7 Types of Non-Hospital Services Number of Encounters

Primary Care Encounters

Medicaid 896,713

Uninsured and Underinsured 1,176,600

Total Medicaid, Uninsured, and Underinsured 2,073,313

OB/GYN Encounters

Medicaid 258,679

Uninsured and Underinsured 306,004

Total Medicaid, Uninsured, and Underinsured 564,683

Disease Management Encounters

Medicaid 55,950

Uninsured and Underinsured 107,115

Total Medicaid, Uninsured, and Underinsured 163,065

Mental Health Encounters

Medicaid 168,081

Uninsured and Underinsured 126,162

Total Medicaid, Uninsured, and Underinsured 294,243

Dental Services Encounters

Medicaid 418,124

Uninsured and Underinsured 313,800

Total Medicaid, Uninsured, and Underinsured 731,924

Number of Prescriptions Filled

Medicaid 417,562

Uninsured and Underinsured 1,281,492

Total Medicaid, Uninsured, and Underinsured 1,699,054

Lab Services Encounters

Medicaid 234,363

Uninsured and Underinsured 606,477

Total Medicaid, Uninsured, and Underinsured 840,840

Radiology Services Encounters

Medicaid 6,015

Uninsured and Underinsured 31,910

Total Medicaid, Uninsured, and Underinsured 37,925

Specialty Care Encounters

Medicaid 22,116

Uninsured and Underinsured 94,638

Total Medicaid, Uninsured, and Underinsured 116,754

Care Coordination Encounters

Medicaid 68,610

Uninsured and Underinsured 95,718

Total Medicaid, Uninsured, and Underinsured 164,328

Total Encounters

Medicaid 2,546,213

Uninsured and Underinsured 4,139,916

Total Medicaid, Uninsured, and Underinsured 6,686,129

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Summary Domain v Overall, the number of uninsured, underinsured, and Medicaid individuals served and the types and number of outpatient services furnished by non-hospital providers has increased. Among hospital providers, the number of individuals served with Medicaid has increased, the number of uninsured and underinsured individuals served has decreased, and the types of services provided such as discharges, inpatient days, emergency care services, hospital-based outpatient services, affiliated services, and prescriptions filled have not changed.

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General Findings: Domain vi

Domain vi: The effect of LIP funding on disparities in the provision of healthcare services,

both geographically and by population groups

Domain vi research questions generally refer to the provision of healthcare services to uninsured, underinsured, and Medicaid individuals and the effect on disparities. The data sources used to answer the research questions related to Domain vi were the Primary Care and Alternative Delivery Systems Expenditures Report for DY7, the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Approved Initiatives, and the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports. (Florida Agency for Health Care Administration, 2011, 2013a).

Domain vi.a: How does LIP funding impact access to and use of services by different population groups?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Approved Initiatives 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports The purpose of the LIP program is to ensure access to services for the uninsured, underinsured, and Medicaid populations. Therefore, LIP funding increased access to and utilization of services for various population groups including uninsured, underinsured, low income, Medicaid eligible, and homeless adults and children. The number of services provided to uninsured and underinsured individuals exceeded the number provided to Medicaid individuals, except for mental health and dental health services. In DY7, there were 2.5 million total encounters for Medicaid patients and 4.1 million total encounters for uninsured and underinsured individuals. Other population groups for which access to and utilization of services was impacted included pregnant women, diabetics, sickle cell patients, children in foster care, Hispanics, African Americans, and individuals with Congestive Heart Failure (CHF) or respiratory conditions such as chronic obstructive pulmonary disease (COPD), community-acquired pneumonia, and asthma. For instance, 15 of the Tier-One projects increased access to obstetrical (OB) and gynecological (GYN) services and at least two projects increased access to dental services for low income adults and children and children in foster care (see pages 30-31 for descriptions of these projects). Tier-Two initiatives impacted access to and utilization of services for the uninsured and underinsured, and individuals with CHF, COPD, pneumonia, and behavioral health disorders, as well as the homeless, pregnant women, infants, and children. For instance, based on information provided in the Tier-Two quarterly reports, the projects referred to as Specialty Care Coordination Programs (SCCPs) and the Readmission Reduction Programs (RRPs) provided care coordination services including follow up, transportation, home health, patient education, laboratory and/or diagnostic testing, and disease management services to over 125 individuals in South Florida with CHF or previous acute myocardial infarction (AMI) and approximately 90 patients with pulmonary conditions such as COPD, pneumonia, or asthma (see pages 33-34 for descriptions of these projects).

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Lee Memorial enhanced access to and utilization of behavioral health services in their primary care clinics by conducting depression screenings on more than 330 patients. Additionally, Lee Memorial improved access to and utilization of primary care and weight management services for over 100 low-income obese individuals. Memorial Hospital West’s Emergency Department Diversion program improved access to and utilization of services for over 190 patients recently discharged from the ED by increasing the number of appointment slots at partner community health centers (CHCs). Other population groups the Tier-Two initiatives affected were neonates, low income, uninsured and underinsured individuals, individuals with sickle cell disease, as well as others. Broward General Medical Center (BGMC) opened a sickle cell day treatment program to provide an outpatient alternative to care for patients who have sickle cell disease. In addition, BGMC initiated a program providing post-discharge support services to low income and indigent patients with congestive heart failure with the goal of decreasing avoidable readmissions. The program assisted patients with care and services beyond the inpatient setting.

Domain vi.b: Does LIP funding increase access to services in part icular areas?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports LIP-funded initiatives have increased access to and utilization of outpatient primary care services, specialty services, and dental services, in both urban and rural locations. For instance, over three-quarters of the Tier-One initiatives were located in or increased access to services in the urban areas of South Florida, specifically, Miami-Dade, Broward, and Palm Beach counties. Hillsborough, Pinellas, and Brevard counties were also among the urban service areas. Rural service areas covered included the counties of Alachua, Polk, Lake, Franklin, Walton, Calhoun, Liberty, and Leon. For the rural locations, LIP-funded initiatives increased access to and utilization of primary care and dental services. For instance, 3,388 dental services were provided in Franklin County in DY7. In addition, the Calhoun/Liberty County Health Department reported providing over 10,000 child health and OB/GYN services in DY7. For the Tier-Two initiatives, LIP funding increased access to services in more urbanized areas of South Florida and along the coastlines. Nine of the Top 15 Tier-Two hospitals are located in urban Miami-Dade and Broward counties and the other six are located in the more populous coastal areas of the state. Three of the Top 15 hospitals are located on the west coast in Hillsborough, Sarasota, and Lee counties and three are on the east coast in Duval, Volusia, and Indian River counties. Hospital outpatient care, diagnostic laboratory, and radiology services were provided in 14 of the 15 Tier-Two Top 15 Hospitals. Eleven of the 15 indicated providing outpatient surgeries and outpatient facility care. Finally, 14 of the 15 Tier-Two Top 15 Hospitals reported providing speech, physical, and occupational therapies to patients. Among affiliated services, primary care or preventive care clinic visits were provided by 12 of the 15 Tier-Two Top 15 Hospitals. Ten of the Top 15 provided specialist visits and four of the Top 15 Hospitals indicated providing surgical care services in physician offices and home health services. Prosthetic or orthotic devices (not associated with outpatient therapy visits) were provided by six of the Tier-Two Top

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15 Hospitals, and durable medical equipment was provided by two of the Top 15 Hospitals. Finally, three of the Tier-Two Top 15 Hospitals reported providing nursing home care (skilled or intermediate) services. In DY7, four of the Tier-Two Top 15 Hospitals reported providing additional services using LIP funds. The additional services reported by the four Top 15 Hospitals include an ER diversion clinic for Medicaid and uninsured patients, a Medicaid and uninsured readmissions reduction project, discharge clinics for CHF and pulmonary cases, discharge prescriptions for uninsured patients, services in patient-centered medical home primary care centers, community youth services, and Health Interventions in Targeted Services (HITS) community Medicaid enrollment program, among others.

Domain vi.c: How many programs funded by LIP, including Tier -One and Tier-Two initiatives, are focused on reducing disparities in the provision of healthcare services or health outcomes?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports Among the Tier-One initiatives, nearly all of the programs were focused on reducing disparities in the provision of healthcare services or health outcomes. Initiatives aimed at reducing disparities in the provision of healthcare services included those that increased capacity to provide additional or enhanced services, such as constructing new clinics or establishing new locations, programs, or services; hiring additional clinical and support staff; and redesigning processes of care. For example, five initiatives were establishing new primary care clinic locations and at least two initiatives created dental clinics to reduce disparities in the provision of dental services for low-income and uninsured residents of Franklin County and for children in foster care in Palm Beach County. Moreover, fifteen projects focused on reducing disparities in the provision of OB and GYN services for uninsured and low-income pregnant women and women of childbearing age. Nearly three-quarters of the Tier-Two Top 15 initiatives focused on reducing disparities in health outcomes and approximately 30% of the Top 15 initiatives focused on reducing disparities in the provision of healthcare services.

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Domain vi.d: What are the Tier-One and Tier-Two programs doing to reduce disparities and how successful are they?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports Among the Tier-One initiatives, interventions in DY7 focused on reducing disparities in the provision of healthcare services included establishing new clinics or new locations; enhancing or establishing new programs and services to increase access to primary care medical services, dental services, and disease management services; hiring additional clinical and support staff; and redesigning processes of care. In DY7, three of the Tier-One initiatives reported providing approximately 92,350 outpatient visits and three initiatives reported providing approximately 17,600 encounters for primary care medical services and pediatric dental services.

The FoundCare Health Center in Palm Beach County reduced disparities in the provision of health services by expanding hours for walk-in and urgent care services, expanding pediatric services, and establishing a new pediatric dental clinic for children in foster care. By expanding the walk-in and urgent care hours, FoundCare was able to increase the number of patients served. Moreover, adding pediatric support increased the number of pediatric patients who received care. Finally, in the initial year of the project, 175 unduplicated pediatric patients in foster care received dental care with 263 visits. The Care One Clinic at UF Health reduced disparities in health services and health outcomes for high medical needs patients with high ED and hospital utilization rates by providing post-discharge services and timely primary care follow up. Compared to the six months prior to the first Care One Clinic appointment, in the six months after, there was a 33% reduction in hospitalizations, a 15% reduction in length of stay, and a 40% reduction in the number of ED visits by this population. Also in DY7, the Tier-Two Top 15 hospitals provided inpatient services to approximately 145,000 uninsured and underinsured individuals and outpatient services to approximately 747,800 uninsured and underinsured individuals. Moreover, the average number of uninsured and underinsured individuals that received inpatient services was approximately 4,600 per reporting Top 15 hospital. For outpatient services, the average number of uninsured and underinsured individuals served per reporting Top 15 hospital was approximately 27,500. Tier-Two initiatives that focused on reducing disparities in health outcomes included Patient Centered Medical Home (PCMH) implementations and readmission reduction projects for COPD patients as well as others. For instance, Memorial Regional Hospital’s project implementing PCMH models at partner CHCs to improve outcomes among patients with diabetes and hypertension resulted in an 8% and a 28% improvement in the percentage of diabetic patients with hemoglobin A1C levels below eight percent and controlled LDL levels below 100, respectively. Broward Health North improved outcomes among COPD patients by enhancing patient education and ensuring patients received appropriate care and discharge follow up, resulting in a decrease in the COPD readmission rate from 14% to 12%.

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Similarly, Memorial Health System’s (MHS) Readmission Reduction Programs focused on decreasing disparities in health outcomes by providing education, care coordination, and support services to patients previously hospitalized for a cardiovascular or pulmonary condition to reduce avoidable ED visits and inpatient stays. The Specialty Care Coordination Program’s also implemented by MHS focused on reducing disparities in health outcomes by providing care coordination services including follow up, transportation, home health, patient education, laboratory testing, diagnostic testing, and disease management services to over 200 individuals with previous hospitalizations for AMI, CHF, COPD, pneumonia, or asthma.

Summary Domain vi

Overall, the Tier-One and Tier-Two initiatives decreased disparities in the provision of healthcare services in both urban and rural geographic locations. Moreover, both the Tier-One and Tier-Two initiatives have decreased disparities in health outcomes and the provision of healthcare services for multiple demographic, socioeconomic, and disease-specific population groups, particularly individuals with CHF. Other conditions and groups of focus were individuals with pulmonary conditions such as COPD, pneumonia, and asthma; individuals with diabetes; patients with sickle cell disease; pregnant women; the uninsured; and those with low incomes.

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General Findings: Domain vii

Domain vii: The impact of Tier-One and Tier-Two Milestone initiatives on access to care and quality of care (including safety, effectiveness, patient centeredness, timeliness, efficiency, and equity)

Domain vii research questions refer to the Tier-One and Tier-Two initiatives and their effects on access to care and quality of care. The data sources used to answer the research questions related to Domain vii were the Primary Care and Alternative Delivery Systems Expenditures Report for DY7, the 2012–2013 Low Income Pool (LIP) Tier-One Milestone (STC #84) Approved Initiative Proposals , the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Approved Initiatives, and the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports.

Domain vii.a: What are the goals of the Tier -One Milestone programs?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7 As stated in STC #84 and the 2012–2013 LIP Tier-One Milestone Application, Tier-One initiatives must be driven from the broad goals of better care for individuals and better health for populations and should “meaningfully enhance the quality of care and the health of low income populations.” Specific goals of the Tier-One Milestone initiatives included:

expanding primary care infrastructure to increase access to primary care services for the uninsured and underinsured;

improving care coordination and increasing access to disease management services for patients with chronic illnesses;

reducing potentially avoidable hospitalizations, hospital readmissions, and emergency department visits;

improving quality of care and outpatient care management for high-risk patients with target diseases;

producing cost savings related to length of stay (LOS) and readmissions for certain health conditions;

providing dental services to children in foster care and low-income and underinsured populations;

providing a comprehensive medical home for children, adults, and seniors; improving access to obstetric services and immediately establishing pediatric care for all

newborns;

demonstrating the benefits and feasibility of the Patient Centered Medical Home (PCMH) model

for diabetic patients to improve patient outcomes and disease management and improving

patient and provider satisfaction;

expanding access to medical and dental services; and decreasing the number of patients inappropriately using the ED for non-emergency issues.

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Domain vii.b: What interventions/activities are the Tier -One Milestone programs using to enhance quality of care and the health of low income populations ? Are the Tier-One Milestone programs successful?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7 The interventions that Tier-One initiatives used to enhance the quality of care and health of low income populations included implementing PCMH models; expanding mental health, dental, and weight management services to provide patients with comprehensive care; establishing programs such as the Specialty Care Coordination Programs (SCCPs); and establishing post-discharge clinics for targeted health conditions. Other activities Tier-One Milestone initiatives used to enhance quality of care and the health of low-income populations included constructing new primary care facilities, increasing the number of clinical and support staff such as pharmacists, dentists, hygienists, and patient navigators, and expanding hours to include evenings and weekends. Finally, at least two initiatives employed innovative tools such as electronic health information exchanges and clinical decision support modules to enhance the quality of care provided.

The SCCP created specialty clinics within partnering Community Health Centers (CHCs) that provided immediate, post-hospital discharge care coordination services to individuals previously hospitalized for acute myocardiaI infarction (AMI), CHF, COPD, pneumonia, or asthma. The SCCP programs reported providing specialty care coordination and transition services to approximately 400 patients, which helped improve the overall quality of care and quality of life for the patients they served. Reporting Tier-One providers served approximately 31,000 recipients in DY7. The maximum number of low-income recipients served by reporting projects was approximately 6,800 individuals. Furthermore, eleven projects reported that there were approximately 129,680 medical and dental services provided to low-income individuals and three projects reported providing approximately 17,600 encounters for primary, medical, and dental care services.

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Domain vii.c: What are the goals of the Tier-Two Milestone initiatives?

2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports As stated in STC #85 and the “Tier-Two Milestone Report Template,” Tier-Two initiatives must be driven from the broad aims of better care for individuals, better health for populations, and reducing per-capita costs. Moreover, initiatives must incorporate the six priorities of the Three-Part Aim. The detailed list of approved initiatives for each of the Top 15 hospitals can be found in Appendix L at the end of this report. The goals of the Tier-Two Milestone initiatives were to:

increase access to primary care services; reduce avoidable hospital readmissions and emergency department visits; improve access to and quality of care for patients with certain health conditions including CHF,

COPD, and sickle cell disease; improve quality of care and health status for obese low-income populations, pregnant women,

babies in the NICU, and individuals with depression; help recipients access the appropriate level of care and prevent the need for emergency or

inpatient care to lower per capita costs and improve the cost-effectiveness of care; and improve efficiency and reduce errors related to surgical site infections, patient falls, mislabeled

specimens, and turnaround times in the emergency department.

Domain vii.d: How many of the Tier -Two Milestone initiatives are focused on access to care and quality of care?

2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports Of the 44 Tier-Two initiatives, approximately 70% were focused on access to care and quality of care as part of the project. Initiatives that targeted improvements in access to care and quality of care included increasing residency slots, increasing the number of appointment slots at partner CHCs for patients discharged from the ED, adding appointment slots to increase patient access to heart failure clinics, increasing open hours at Family Care Centers, and establishing consolidated clinics incorporating primary and specialty care. Examples of specific initiatives that focused on improvements to quality of care included projects to reduce surgical site infections, decrease patient falls, and reduce the number of mislabeled specimens. Improving access to services and the quality of care provided was also the focus of the following initiatives:

Memorial Health’s enhancement of PCMH models at affiliated community health centers, Broward Health’s post discharge support services for indigent patients with CHF, North Broward’s initiative to decrease Catheter Associated Urinary Tract Infections (CAUTI), and Tampa General’s population focused initiative to reduce central line–associated bloodstream

infections (CLABSIs) in the NICU.

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Domain vii.e: How are the Top 15 hospitals working to meet their goals? Are they successful?

2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports Ways in which the Top 15 hospitals are working to meet their goals include establishing new clinics, implementing new programs, instituting process improvement projects, expanding infrastructure, and offering new or additional services. Halifax Health implemented the Managing Obstetric Risk Efficiently (M.O.R.E.) program to decrease the number of unattended deliveries by 15% and reduce cesarean section rates to 48% or less. At the end of DY7, there was a 71% reduction in the number of unattended deliveries from the baseline. Moreover, the program helped achieve a cesarean section rate of 37%. To meet Tampa General’s goal of increasing access to primary care, the hospital increased the number of aggregate open hours at their family care centers. To improve quality of care and reduce CLABSI infections in the NICU population, Tampa General joined an eight-state collaborative and implemented best practices in the units which resulted in reducing the CLABSI rate from 3.13 infections per 1,000 line days to 2.9 infections per 1,000 line days in DY7. To reduce pneumonia-related readmissions, Broward Health’s Imperial Point Hospital created a Transitional Care Team to implement strategies and actions to improve pneumonia readmission rates. The project resulted in an 8% pneumonia readmission rate in the fourth quarter (Q4) of DY7, which is an 11.5% reduction from the baseline of 19.5% in Q4 of DY6.

Summary Domain vii

Overall, the Tier-One and Tier-Two initiatives focused on improving the health of low-income populations by enhancing access to services and the quality of care provided. Among the Tier-One initiatives, approximately 60% of the projects concentrated on increasing access to care for low income populations. Additionally, approximately 40% of the Tier-One initiatives focused on enhancing the quality of care for those with low incomes. Moreover, 70% or more of the Tier-Two initiatives, impacted quality of care and access to care for uninsured, indigent, and low income populations. Overall, both the Tier-One and Tier-Two initiatives provided better care coordination and increased access to primary care and disease management services.

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General Findings: Domain vii i

Domain viii: The impact of Tier-One and Tier-Two Milestone initiatives on population health

Domain viii research questions refer to the Tier-One and Tier-Two initiatives and their effects on population health. The data sources used to answer the research questions related to Domain viii were the Tier-One Primary Care and Alternative Delivery Systems Expenditures Report for DY7, the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Approved Initiatives, and the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports.

Domain viii.a: How are the Tier-One Milestone initiatives proposing to affect population health?

Domain viii.b: Are the Tier-One Milestone initiatives targeting particular groups of recipients or health conditions? Are they successful in achieving their objectives?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7

Tier-One Milestone initiatives affected population health by increasing access to primary care services, targeting chronic illnesses such as diabetes, hypertension, and cardiovascular disease, and by focusing on groups of individuals at risk for poor physical and mental health. Tier-One initiatives targeted uninsured and underinsured individuals, the homeless, individuals at or below 200% of the federal poverty level (FPL), pregnant women, children, individuals on Medicaid, persons that use emergency departments as their primary source of care, and individuals with diverse ethnic and racial backgrounds including Hispanics, African Americans, non-Hispanic whites, and Hispanic blacks, among others. Health conditions targeted by Tier-One initiatives included obesity, CHF, HIV/AIDS, pneumonia, substance abuse, mental illness, oral health problems, and asthma. Tampa General’s initiative to expand OB/GYN services reported nearly 1,400 visits by low-income and uninsured women in Hillsborough County. FoundCare Health Center’s initiative to increase access to primary and dental care for low-income, minority residents with chronic diseases in Palm Beach County resulted in approximately 8,900 encounters for services for the target recipients. Brevard County Health Department’s (CHD’s) two initiatives targeted residents of Brevard County who were uninsured or had incomes less than 100% of the FPL. The two initiatives resulted in the provision of nearly 2,900 primary care and dental services.

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Domain viii.c: How are the Tier-Two Milestone initiatives proposing to affect population health?

Domain viii.d: Are they targeting particular groups of recipients or health conditions ?

2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports Tier-Two hospitals affected population health by reducing readmissions, preventing avoidable emergency department visits, expanding primary care residency programs, and improving care management and coordination for high-risk individuals with chronic illness. Lee Memorial’s initiatives to improve the health status of low-income patients that are obese, have COPD, or depression impacted population health through reductions in weight, early identification and treatment of mental health disorders, smoking cessation programs, and disease management. Tampa General Hospital’s population-focused initiative to lower the rate of CLABSIs in the NICU impacted population health by reducing mortality and morbidity in the neonate population at Tampa General. Additional health conditions and groups of recipients targeted by the Tier-Two initiatives included patients with CHF, sickle cell disease patients, patients with asthma, and patients with diabetes. Moreover, the Tier-Two hospital initiatives focused on improving the health of low-income populations, pregnant women, infants, and patients with comorbid mental health and medical conditions.

Domain viii.e: What interventions/activities are they engaging in to impact population health? Are they successful?

2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports Tier-Two hospitals used numerous activities to impact population health which included establishing discharge support services, expanding observation services for CHF patients, implementing day treatment programs for sickle cell patients, and increasing primary care capacity by expanding primary care residency programs. Lee Memorial developed a depression screening tool to help PCPs within Lee Memorial Health System identify and track low-income patients with depression. In the quarter ending June 30, 2013, 335 patients had been screened and use of the tool had been expanded to a second location. Tampa General Hospital’s population-focused initiative to decrease the number of CLABSIs in the NICU resulted in a reduction in the CLABSI rate from 3.2 per 1,000 line days in FY2011 to 2.44 per 1,000 line days in FY2012. Finally, Broward General Medical Center’s primary care residency program expansion impacted population health by increasing access to at least 28 primary care physicians in Florida.

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Summary Domain viii

Tier-One and Tier-Two Milestone initiatives affected population health by increasing access to primary care services; improving the management of individuals with chronic illnesses such as diabetes and hypertension; enhancing services for patients with CHF; and focusing on population groups including but not limited to pregnant women, children, and the homeless. Specific activities used to impact population health included the implementation of Specialty Care Coordination Programs for cardiovascular and pulmonary conditions; instituting protocols to reduce infections in neonates; establishing dental clinics to improve the oral health of low income children and adult populations; and creating and encouraging use of a depression screening tool that helped PCPs identify low income patients with depression.

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General Findings: Domain ix

Domain ix: The impact of Tier-One and Tier-Two Milestone initiatives on per-capita costs

(including Medicaid, uninsured, and underinsured populations) and the cost -effectiveness

of care

Domain ix research questions in the following discussion address the effect of the Tier-One and Tier-Two initiatives on per-capita costs and the cost-effectiveness of care. The data sources used to answer the research questions related to Domain ix were the 2012–2013 LIP Tier-One Milestone (STC #84) Approved Applications, the Tier-One Primary Care and Alternative Delivery Systems Expenditures Report for DY7, and the 2012–2013 Tier-Two Milestone (STC #85) Top 15, Quarterly Reports.

Domain ix.a: How do expenditures for services funded through the Tier -One Milestone initiatives differ from other LIP expenditures ?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7 The expenditures for the services funded through the Tier-One Milestone $35 million primary care award differ from other LIP expenditures in that specifically designated funds were allotted based on a competitive solicitation process and were based on a proposed budget that incorporates maximum award levels and project goals stated a priori, or before the distribution of funds.

Domain ix.b: How do the services provided under Tier -One Milestone initiatives differ from those provided under other LIP funding? That is, do Tier-One Milestone expenditures result in more preventive and outpatient care than emergency department and inpatient visits?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7 As outlined in STC #84, services proposed under the Tier-One Milestone initiatives are required to focus on improving access to primary care services in outpatient settings. The four projects referred to as Specialty Care Coordination Programs (SCCPs) provided care coordination services including follow up, transportation, home health, patient education, laboratory and/or diagnostic testing, and disease management services to over 200 individuals previously hospitalized for AMI, CHF, COPD, pneumonia, or asthma. Eleven initiatives funded under the Tier-One Milestones reported providing approximately 129,680 medical and dental services in an outpatient setting to individuals in DY7. Eight of the Tier-One funded initiatives reported providing care coordination, disease management, and transition services to over 400 patients in an outpatient setting with a cardiac or pulmonary condition with the goal of reducing readmissions. Two Tier-One projects provided approximately 17,000 preventive and outpatient encounters for primary, medical, and dental care services.

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Among the Tier-One initiatives, 23 initiatives reported both expenditures and the number of recipients served. The maximum amount of expenditures per recipient was $15,680 and the minimum amount spent per recipient was $29. The average amount spent by reporting projects was $2,223 per recipient. Overall, the proposed Tier-One initiatives were focused on providing comprehensive and coordinated preventive and acute primary care, dental, and behavioral health services in an outpatient setting with the goal of reducing the number of avoidable emergency department visits and inpatient stays to ultimately reduce the costs of care.

Domain ix.c: Do Tier-One Milestone initiatives result in lower expendit ures for recipients who are served by them?

Primary Care and Alternative Delivery Systems Expenditures Report for DY7 It is well documented that barriers to care are linked to poorer health status resulting from delayed or foregone care, particularly among high-risk populations. Thus, for patients presenting in poor health, it is reasonable to expect an increased probability of emergency department use or inpatient admission with a longer length of stay and higher rates of avoidable readmissions, resulting in an increase in expenditures (Billings et al., 1993; Draper, 2011; Harman, Hall, & Zhang, 2007). The central goals of the Tier-One initiatives were to increase access to care for uninsured, underinsured, low-income, indigent, and Medicaid individuals. Several initiatives provided various services to patients based on a sliding scale or at no cost such as Palm Beach CHD’s initiatives and the Brevard CHD’s primary care clinic initiative. In addition to providing services on a sliding scale or at no cost, the Brevard CHD Primary Access to Health (PATH) program assisted clients with enrolling in pharmaceutical assistance programs to receive free medications. The Palm Beach CHD’s maternity clinic at the Jupiter Health Center distributed free prenatal vitamins and iron supplements to maternity patients and provided lab services and contraceptives on site, reducing the recipients’ costs associated with traveling to the main clinic, approximately 20 miles away. The SCCP programs provided eligibility assistance for Medicaid or the MHS Community Health Center program; assistance with transportation services, home health services, pharmacy services, and linkages to community and faith-based agencies for assistance with transportation, child care, afterschool care, utility payments, housing payments, immigration issues, and Earned Income Tax Credits, as needed.

Domain ix.d: Do the Tier-Two Milestone initiatives impact expenditures for care for the uninsured/underinsured? How are expenditures affected?

2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports Memorial Hospital West’s Specialty Care Coordination Program’s (SCCPs) Pulmonary Clinic saw a 35% reduction in the per person 30-day acute episode cost from the baseline during the quarter April 1, 2013 to June 30, 2013. Memorial Regional Hospital’s SCCP’s Heart Failure clinic saw an 8% reduction in the per person 30-day acute episode cost from the baseline during the period July 1, 2012 to June 30, 2013.

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Memorial Hospital Miramar’s ED diversion initiative impacted expenditures by reducing the number of Medicaid and uninsured/underinsured patients utilizing the ED for Level I and Level II services. Between July 1, 2012, and June 30, 2013, 134 individuals had been diverted from the ED to the affiliated CHC. Halifax Health’s MORE OB Tier-Two initiative impacted expenditures for care by reducing the costs of care associated with cesarean rates for first birth women. As of June 30, 2013, the annual rate was 37%, 11% less than the target rate of 48%. Broward General Medical Center created a sickle cell day treatment program to reduce admissions, which would in turn reduce per capita costs and improve the cost-effectiveness of care by providing access to and promoting the use of outpatient services as opposed to inpatient services. The goal was to reduce the number of quarterly admissions for their current adult sickle cell population by 10% from a baseline of 93 admissions per quarter. In the quarter ending June 30, 2013, there were 74 admissions during the quarter, demonstrating the program was meeting the needs of adult patients with sickle cell disease in a setting that allowed acute care hospitalizations to be avoided, resulting in lower expenditures for care. Tampa General Hospital’s population-focused initiative to reduce CLABSI in the NICU impacted expenditures by reducing consumption of NICU resources and decreasing LOS.

Domain ix.e: What initiatives are successful in helping recipients to access the appropriate level of care and prevent the need for emergency or inpatient care?

2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports

Several Tier-Two initiatives helped recipients access the appropriate level of care and prevented the need for emergency and inpatient care. For instance, Halifax Health’s initiative enhancing observational services for CHF patients resulted in a reduction in CHF admissions during the reporting time period. Jackson Memorial also reported that their CHF Readmission Reduction Program (RRP) reduced readmissions among CHF patients by 10%. Indian River Medical Center’s outpatient heart failure clinic increased access to disease management services, thereby reducing readmission rates to less than 10% for the CHF patients utilizing the clinic. Finally, the Post-Discharge Support Services initiative at Broward General Medical Center reduced readmission rates among indigent patients with CHF. The Emergency Department Diversion programs implemented at Memorial Hospital Miramar and Memorial Hospital West helped recipients access the appropriate level of care and prevented the need for emergency and inpatient care by increasing the number of appointment slots available at partnering CHCs for patients discharged from the hospital EDs. The initiatives resulted in a significant reduction in the number of Level I and Level II ED visits at the respective hospitals. Memorial Hospital Pembroke’s RRP provided post-discharge follow-up services, including follow-up coaching over the telephone and post-discharge nutrition and meals to patients previously hospitalized for cardiac or pulmonary conditions, to prevent the need for emergency or inpatient care. Memorial Regional’s initiative transforming four CHCs into PCMHs resulted in patients with chronic diseases such as diabetes, hypertension, and heart failure being able to access more appropriate levels of care such as

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comprehensive disease management services in an outpatient setting, preventing the need for emergency department visits and inpatient care.

Summary Domain ix

Tier-One and Tier-Two initiatives impacted per-capita costs and the cost-effectiveness of care by providing coordinated acute care, disease management services, and preventive primary medical care, dental, and behavioral health services with the goal of reducing the numbers of avoidable emergency department and inpatient visits. Initiatives included implementing Emergency Department Diversion programs, reducing infections in the NICU, developing RRPs, and establishing condition-specific outpatient clinics, among others.

2012–2013 Low Income Pool (LIP) Tier-One Milestone (STC #84) Initiative: $15 Million Quality Add-on Project As previously discussed, Tier-One Milestones as outlined in STC #84 include the development and implementation of a State initiative that requires Florida to allocate $50 million in total LIP funding in DY7 and DY8 to establish new, or enhance existing, innovative programs that meaningfully enhance the quality of care and the health of low-income populations. Initiatives must broadly drive from the three overarching goals of CMS’ Three-Part Aim. The allocation of $50 million includes $15 million in funds (“$15 Million Quality Add-on”) distributed to hospitals in DY7 based on the hospital meeting specific Quality Measures collected by the Agency for Health Care Administration and Core Measures collected by CMS. A detailed description of these measures is provided in Appendix M at the end of this document. Based on the achievement of meeting quality targets in DY6, 62 hospitals were awarded a one-time payment for a total of $15 million in LIP funds for Quality Add-on projects. The minimum amount awarded to a facility was approximately $3,700 and the maximum amount awarded was approximately $2 million.

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Summary and Conclusions

Domains i and ii

The Domains i and ii analyses include a variety of data sources and methodologies to understand the impact of the demonstration on access to care, the cost of care, and the quality of care. Table 84 below summarizes the findings by domain. Detailed CAHPS and HEDIS tables and figures by domain are found in Appendix E. Table 84: Domains i and ii Summary and Conclusions by Domain Questions Table 84: Domains i and ii Summary and Conclusions by Domain Questions (Continued)

Domain Data Source and Approach Answer

Domain i.a

Are services accessible to enrollees? Analysis of CAHPS survey responses over time and from DY6 through DY8 within Reform and Non-Reform counties.

Overall, respondent self-reports indicate that enrollees in the Reform counties perceive services to be accessible. There were increases across several access measures in both Reform and Non-Reform counties between DY6 and DY8. In general changes, whether increases or decreases, were not statistically significant.

Have there been changes in the accessibility of services to enrollees over the course of the demonstration?

Analysis of CAHPS and HEDIS wellness measures over time and between DY6 and DY8 within Reform and Non-Reform counties.

To some extent, there appear to be improvements in respondent self-report of obtaining health services. Over time, there was a significant increase in the percentage of enrollees having a personal doctor in urban reform counties. Moreover, there was a statistically significant increase over time in the percentage of enrollees who saw a doctor for non-urgent care one to three times in the previous six months and a decrease in the percentage of enrollees with four or more non-urgent visits. Enrollees in Reform counties in DY8 reported greater ease of getting specialist appointments as needed (“Always”) than in the previous year, though the change was not statistically significant.

Based on HEDIS measurement trend analysis, Reform plans have improved the appropriate utilization of services in some areas but struggle in others. The main area of

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Table 84: Domains i and ii Summary and Conclusions by Domain Questions (Continued) Domain Data Source and Approach Answer

strength is with the Annual Dentist Visits measure, while they have not done as well with three of the Adults’ Access to Preventive/Ambulatory Health Services measure components and Postpartum Care.

Has the demonstration resulted in more appropriate use of services by enrollees?

Analysis of claims, eligibility, and encounter data for SFY1011 – 1112.

Overall, PMPM utilization of all health service types was greater in the Reform counties compared to the Control counties for both TANF and SSI enrollees for SFY1011 – 1213. Furthermore, the rate of growth over time in the Reform counties is larger compared to controls with the exception of medical services. Therefore Reform counties will achieve lower utilization over time of medical services if the current trend continues, but this will not be the case for inpatient, outpatient, pharmacy and emergency room services.

Domain i.b

Has the quality of care that enrollees receive improved during the demonstration?

Analysis of CAHPS and HEDIS chronic disease measures over time; comparison of results from DY6 to DY8 in Reform and Non-Reform counties.

Out of 12 HEDIS chronic disease measures, 10 have shown improvements throughout the course of the demonstration. The measures that declined had a minimal average annual decrease equal to less than one percentage point.

CAHPS results indicate that in the urban Reform counties there was a significant increase over time in enrollees reporting the highest level rating for their health plans. Increases in other measures were not statistically significant. In rural Reform counties, the proportion of enrollees who reported the highest level rating decreased from DY7 to DY8 on three out of the four measures, but none of the changes over time were statistically significant. In the Reform counties overall, no change (whether increase or decrease) was statistically

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Table 84: Domains i and ii Summary and Conclusions by Domain Questions (Continued) Domain Data Source and Approach Answer

significant over time.

What have managed care plans done to improve the quality of care?

Qualitative analysis of Performance Measure Action Plans (PMAPS) submitted by each plan in the demonstration.

Managed care plans have varied strategies that mostly involve engaging with stakeholders. Based on the comparative PMAP qualitative case study analysis, no major differences could be found among the plans with positive or negative Average Annual Change in measures. It is concluded that the case study of health plan measures with negative and positive Average Annual Changes had more similarities than differences when considering the actions taken to improve performance of these measures by the different health plans.

Domain i.c

How has the demonstration increased timeliness of services?

Analysis of CAHPS surveys over time and between responses in DY6 and DY8 within Reform and Non-Reform counties. Focused on two specific questions: Able to get care needed right away?

How often able to get an appointment when wanted?

There is indication that there might be some improvement in the reports of being able to access care in a timely manner in the urban Reform counties, where there was a statistically significant increase over time in the percentage of enrollees who was “Always” able to get urgent and non-urgent care as soon as they wanted. From DY6 to DY8 in Reform counties, there was a significant increase in the percentage of enrollees who reported “Always” getting urgent care right away and getting an appointment as soon as they needed.

Domain i.d

How has the demonstration affected growth of Medicaid costs?

Analyses of claims and eligibility data.

Overall, it appears that PMPM expenditures were greater in the Reform counties compared to the Control counties for SSI enrollees. However, the rate of growth was lower in the Reform counties relative to the Control counties, suggesting that the Reform counties will achieve savings over time if the current trend continues in the future. However, while PMPM expenditures for TANF enrollees were lower in Reform counties compared to the Control counties, the rate of growth is

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Table 84: Domains i and ii Summary and Conclusions by Domain Questions (Continued) Domain Data Source and Approach Answer

greater in the Reform counties relative to the Control counties. This suggests the Reform will not achieve savings over time for TANF enrollees. Caution should be used in interpreting these results as the TANF trend finding is a reversal of previous results. This may be artifact of the data as well as the new exclusion method used for Reform county observations which is now based on the eligibility file Plan Type field code rather than the Program Code, and excludes MediPass enrollees. This was done at the request of AHCA.

Domain ii.a

To what extent do health plans offer customized benefits?

Analysis of Plan Benefit Charts To a very minimal extent. The scope of expanded benefit packages is small. Expanded benefits could include OTC pharmacy, adult dental, and respite services among others. The number of plans that offered expanded benefits increased slightly from 12 to 13 in the 2007, 2011, 2012, and 2013 period. The number of plans that did not offer expanded benefits decreased slightly from three in 2007 to just one in 2013. There were nine plans that required co-pays at the beginning of the demonstration, in 2007. The number of plans that required co-pays decreased to just three plans in 2013. Plan limits did not vary greatly in the 2007, 2011, 2012, and 2013 period, nor did they vary greatly between plans. Plans that made changes to limits for certain services in the reporting period only made slight changes, with the majority of plans changing limits related to pharmacy and vision services in all years for both the SSI and TANF eligibility groups. Many standard services were completely covered, and a few services varied across plans but the

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Table 84: Domains i and ii Summary and Conclusions by Domain Questions (Continued) Domain Data Source and Approach Answer

differences were not large.

Are there plans whose customized benefits are geared to particular populations?

There are two plans that are geared towards specific population groups. One plan is for children with special health care needs and their siblings. The second plan is for individuals who are HIV positive and their household members.

Domain ii.b

When presented the opportunity, do plans provide additional services not previously covered by Medicaid?

Analysis of Plan Benefit Charts As described above, plans offer expanded benefits only to a minimal extent. Of those operational in the 2007, 2011, 2012, and 2013 period, most decreased the number of expanded benefits available to the beneficiaries.

To what extent do enrollees use these additional services?

Expanded benefits and Reform market share analyses

During the 2007, 2011, 2012, and 2013 period, the number of plans providing additional services or expanded benefits decreased. There are no data that clearly indicate which additional services were used; only whether or not they were offered.

Domain ii.c

Are there differences in enrollees’ satisfaction with and experiences with care between plans with different benefit packages?

Comparison in CAHPS ratings of care from DY6 and DY8 within plans with additional benefit packages and plans without additional benefits

There was a significant increase over time in the percentage of enrollees who would recommend their health plan to family and friends (“Definitely Yes”), within all four groups of plans. There was also a significant increase over time in the percentage of enrollees in plans offering adult dental benefits who gave the highest rating (Level 3) to their health plan and personal doctor. Other trends either increased or decreased over time, though most were not statistically significant. Across DY6 through DY8, the differences in enrollee satisfaction ratings between plans that provided additional benefits and plans that did not (over-the-counter and adult dental) were not systematic, nor were they statistically significant except for one survey item.

Between plans that offer additional benefits vs. those that do not?

Comparison in CAHPS ratings of care between DY6 and DY8 within

Satisfaction ratings increased across most measures from DY6 to DY8

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Table 84: Domains i and ii Summary and Conclusions by Domain Questions (Continued) Domain Data Source and Approach Answer

plans with additional benefit packages and within plans without additional benefits

within plans that offered OTC expanded benefits and, to a lesser extent, within plans that offered adult dental benefits. Across DY6 through DY8, the differences in enrollee satisfaction ratings between plans that provided additional benefits and plans that did not (over-the-counter and adult dental) were not systematic, nor were they statistically significant except for one survey item.

Domain ii.d

Does access to and quality of care vary between plans with different benefit packages?

Comparison in CAHPS reports of care between DY6 and DY8 within plans with additional benefit packages and within plans without additional benefits.

Overall, there was an increase in access and quality of care from DY7 to DY8 within each of the four enrollee comparison groups (OTC expanded benefits, no OTC expanded benefits, adult dental benefits, and no adult dental benefits). Over time, however, most of the increases were not statistically significant. Across DY6 through DY8, the differences in enrollee satisfaction ratings between plans that provided additional benefits and plans that did not (over-the-counter and adult dental) were not systematic, nor were they statistically significant.

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Domain iii

Florida’s Enhanced Benefits Account (EBA) program was designed to incentivize beneficiaries to take an active role in their health and health care through engaging in certain health behaviors. Under the EBA program, Florida Medicaid beneficiaries could earn up to $125 in credits annually for participating in health-related activities. The program is now over seven years old and is an innovative component of the 1115 waiver. Using claims, encounter, hospital discharge, and EBA administrative data, this evaluation sought to describe the extent to which enrollees engaged in the program and whether participation was linked to changes in certain aspects of health care utilization. A summary of the findings are detailed in Table 85 below. Domain Question Table 85: Domain iii Summary and Conclusions by Domain Questions Table 85: Domain iii Summary and Conclusions by Domain Questions (Continued)

Domain Data Source and Approach Answer

Domain iii.a

To what extent do enrollees earn Enhanced Benefits? To what extent do they spend their rewards?

Analysis of counts of services/behaviors for those eligible to participate in the EBA program but who did not participate/earn credit (non-earner), and those who participated (low, medium, or high earners) for each year using EBIS and eligibility data.

During DY2 – DY7 of the Enhanced Benefits Reward$ (EBA) program, a total of $63,820,095 in EBA credits were earned by enrollees and 60% of the earned credits were spent on eligible purchases. Over the 6 year period, between 55% and 60% of eligible enrollees did not earn any EBA credits each year. Overall, preventive care for children and adults accounted for nearly half (49%) of the total credits earned. In DY3, the EBA credit earnings associated with office visits dropped to almost one fifth of the previous year’s earnings in the same category because of a policy change that reduced the dollar credit associated with office visits and capped the number of eligible annual office visits. In subsequent years, both the number of office visits and credit earnings increased but did not return to levels seen in DY2.

Domain iii.b and Domain iii.c

Is the Enhanced Benefits program associated with increased use of preventive services by enrollees?

Is there a difference in services used by enrollees participating in the EBA program vs. enrollees who do not in Reform and Non-Reform counties?

Analysis of differences in preventive services and use of use of facility, medical, and prescription drug services in Medicaid Reform and Non-Reform counties for those eligible to participate in the EBA program but who did not participate/earn credit (non-earner), and those who participated (low, medium, or high earners) for each year using EBIS, eligibility,

During the study years, enrollees in Medicaid Reform counties had a greater number of preventive and other service claims and encounters than Medicaid enrollees in Non-Reform counties. The EBA program is an important difference between Reform and Non-Reform counties. However, the program is likely not the only reason for the difference in

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Table 85: Domain iii Summary and Conclusions by Domain Questions (Continued) Domain Data Source and Approach Answer

claims, and encounter data. utilization. Similarly, Reform enrollees who had earned more EBA credits had a greater number of claims and encounters than Reform enrollees who had earned fewer or no credits.

Domain iii.d

Is there variation in the likelihood of participation in certain health care behaviors between enrollees in Reform and Non-Reform counties?

Analysis of differences in preventive services in Reform and Non-Reform counties for those eligible to participate in the EBA program but who did not participate/earn credit (non-earner), and those who participated (low, medium, or high earners) for each year using EBIS, eligibility, claims, and encounter data.

During the study period, enrollees in Medicaid Reform and Non-Reform counties had a similar number of cancer screening claims and cancer screening encounters per member per year. Within urban Medicaid Reform counties, “high” EBA earners had more claims and encounters for cancer screening services than non-earners and earners in the “low” and “medium” groups. For dental screening services, enrollees in the urban Medicaid Reform counties that were “medium” earners had more dental screening encounters per member per year than “low,” “high,” and non-earners.

Domain iii.e

To what extent does participation in the EBA program vary by characteristics of enrollees (e.g., race/ethnicity, chronic illness, and plan type)?

Descriptive and logistic regression analysis by earning level by gender, race/ethnicity, age group, county, eligibility group (SSI/TANF) using EBIS and eligibility data.

Age distributions varied across EBA earning categories. For example, 59% of high earner person years were for beneficiaries 3 years and younger, compared to 26% of low earner person years who were 3 years or younger. Further, 23% of high earner person years were over 18 years of age, while 17% of medium earner person years were for enrollees over 18 years. Additionally, there was some variation in eligibility category by EBA earning level. Among person years for high EBA earners, 74% were TANF recipients. However, among low EBA earner years, 87% were TANF beneficiaries. A logistic regression model confirmed that TANF enrollees were more likely to earn credits compared to SSI enrollees.

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Table 85: Domain iii Summary and Conclusions by Domain Questions (Continued) Domain Data Source and Approach Answer

Domain iii.f

Is there a difference in rates of avoidable hospitalizations and emergency department use among EBA users (high, medium, low) and non-users?

Comparison of the rates of avoidable hospitalizations and emergency department use for those eligible to participate in the EBA program but who did not participate/earn credit (non-earner), and those who participated (low, medium, high earners) for each year using EBIS, eligibility, inpatient hospital discharge, and emergency department hospital discharge data.

Overall, enrollees had less than one episode of avoidable hospitalization and ED visit per person per year, respectively. Enrollees in the low and medium EBA earning levels had the lowest rates in each of the six years. On average, EBA enrollees in urban Medicaid Reform counties had less than one emergency department (ED) visit per person per year in each of the study years, irrespective of their EBA earning level. However, enrollees in the low and medium EBA earning categories had the lowest number of ED visits (0.03 to 0.08).

Domain iii.g

What is the effect of Enhanced Benefits participation on total expenditures?

Analysis of expenditures for those eligible to participate in the EBA program but who did not participate/earn credit (non-earner), and those who participated (low, medium, or high earners) for each year using EBIS, eligibility, and claims data.

Overall, enrollees’ total health expenditures in urban Medicaid Reform counties increased over the study years. The greatest change was noted among “high” earners, whose expenditures more than doubled from DY2 to DY3, then decreased only slightly in the next four years. “High” earners had the greatest expenditures of all four earning categories, followed by “non-earners,” “medium” earners, and “low” earners.

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Domain iv

The evaluation of the impact of Florida’s Demonstration Waiver program as a deterrent against Medicaid fraud and abuse included both qualitative and quantitative analyses. Based on a sample of nine managed care plans participating in the demonstration program, the evaluation sought to evaluate these two specific objectives of the demonstration program:

To serve as an effective deterrent against fraud and abuse by moving from a fee-for-service to a managed care delivery system; and

To maintain strict oversight of managed care plans including adapting efforts to the surveillance of fraud and abuse within the managed care system.

While the multiplicity of factors impacting the program over the years prevents establishment of direct causality by any specific factor, three general trends emerged from the quantitative analyses using the sample of data provided for evaluation. These three trends provide rudimentary indication that the managed care plans in the sample are increasingly effective in deterring fraud and abuse. First, overpayments as well as fraud and abuse amounts have decreased over time under the Reform contracts with one exception. Second, this decrease was particularly notable for larger and more established plans, e.g., plans with enrollments greater than 100,000. Third, these first two trends (decreased amounts in both overpayments as well as fraud and abuse, and the notably larger decreases for the larger plans) were not observed when examining the data for the Non-Reform contracts. This difference provides preliminary support for the impact of managed care plans operating under the auspices of Reform contracts versus Non-Reform contracts. More conclusive support would require addressing limitations of the current analyses, examples of which are provided below. Several limitations must be taken into account when examining these trend analyses. First, the magnitude of increases and decreases over time cannot be accurately determined due to the fact that case investigations often extend across FYs. Second, whatever the magnitude, increases and/or decreases in overpayments or in fraud and abuse identified can be due to a rise (decline) in fraudulent activities or detection efforts or a combination of the two. As such, reductions in these measures over time should not be assessed in isolation but considered as one of several factors that may determine the success of detection, prevention, and recovery efforts taking place during the years of the demonstration program. These limitations should be taken into account for future research in order to provide more conclusive evidence. The qualitative analyses revealed a complementarity between prospective and retrospective strategies which highlights several important points mentioned in various anti-fraud plans. First, it is important to record continuous data mining queries in electronic libraries in order to be able to effectively exploit them as part of future detection and prevention tools. Second, fighting fraud and abuse requires pooling of data and a coordinated team effort within each plan across various departments and staff members including compliance personnel, investigators, data analysts, and information technology officers. Third, it also requires close collaboration within the industry with other plans and various anti-fraud associations. This collaboration would allow for sharing of best practices, effective detection and prevention tools, as well as trends in fraudulent schemes across the entire healthcare industry. Finally, staying current with the latest research, methodologies, innovative technologies, and updates in state guidelines seems to be crucial to successful integration of proactive and retrospective efforts to

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combat Medicaid fraud, abuse, and waste. Therefore, effective communication and cooperation among the contracted health plans and state agencies plays a key role. Florida’s efforts in establishing such cooperation is evidenced by the Bureau of MPI identifying and developing education programs in conjunction with the Fraud Prevention and Control Unit’s (FPCU’s) identification of issues of concern, including risks of fraud and abuse that may be unique to each geographic area or provider-type specific (AHCA, n.d., a). The Agency maintains regulatory control and oversight of managed care plans and the anti-fraud policies and procedures utilized by these plans in the deterrence, detection and surveillance of fraud and abuse within the managed care system. This control is founded in the details and requirements established by the 2012 – 2015 Health Plan Model Contract that requires the submission of an annual anti-fraud plan as well as quarterly and annual reports on the managed care plan’s efforts to prevent and deter fraud and abuse. The content analysis of anti-fraud plans that each Medicaid managed care plan must submit annually to the Bureau of MPI demonstrates that all plans conform to the standards of the contract and adhere to the reporting requirements. The State of Florida’s efforts to deter and detect fraud and abuse in Medicaid managed care extend beyond regulatory and contractual requirements regarding fraud and abuse policies and procedures for managed care plans in the demonstration program. The State has reviewed anti-fraud systems and resources across state agencies to enhance collaboration and advance the effectiveness of prevention, detection and recoupment (Strike Force, 2013). As a result of the efforts, the Agency is procuring a new case management system that will incorporate advanced detection systems as well as a new Public Benefits Integrity Data Analytics and Information Sharing Initiative that will detect and deter fraud, waste and abuse in Medicaid (Strike Force, 2013). In concert with the increased utilization of data analysis techniques within managed care plans, the State has likewise implemented new technology and software detection tools, specifically predictive modeling software. This new software-based approach enhances the capability to detect fraud in Medicaid claims by identifying data anomalies not found using traditional detection tools. In 2013, the Bureau of MPI was appropriated $3 million to procure a data analytics service to assist in the identification of noncompliant, abusive or possibly fraudulent providers and recipients (Strike Force, 2013, p. 20). Further, the Agency has developed a training program for the Bureau of MPI investigators designed to help them better understand a variety of aspects of managed care in Florida that are integral to success at early detection of fraud (Strike Force, 2013). As Florida moves to statewide managed care, the Bureau of MPI and the MFCU are preparing for the transition. These units will continue to monitor, review, audit and inspect managed care plans to ensure that Florida’s Medicaid enrollees receive appropriate, medically necessary and high quality healthcare (Strike Force, 2013).

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Table 86: Domain iv Summary and Conclusions by Domain Questions Table 86: Domain iv Summary and Conclusions by Domain Questions (Continued)

Domain Data Source and Approach Answer

Domain iv.a

What are the program integrity–related measures employed by the health plans in the Demonstration related to deterring fraud and abuse by network and non-network providers; deterring fraud and abuse by recipients; detecting fraud and abuse by network and non-network providers; and detecting fraud and abuse by recipients?

Qualitative content analysis of anti-fraud plans submitted by managed care organizations to the Bureau of MPI. Semi-structured interviews of Compliance Officers and/or key members of their teams.

The Agency requires managed care plans to adopt an anti-fraud plan addressing the detection and prevention of overpayments, abuse, and fraud, which is submitted for approval by September 1 each year. The Agency requires Quarterly Anti-Fraud and Abuse Reports which include information on the specific detection tools utilized for each suspected fraud and abuse incident. The Agency requires an Annual Fraud and Abuse Report which include investigations of potentially fraudulent or abusive acts during the prior fiscal year. These reports must include, at a minimum: The dollar amount of health plan losses and recoveries attributable to overpayment, abuse and fraud; and the number of health plan referrals to the Bureau of MPI. Within the managed care programs, a variety of internal policies and procedures were identified. Overall, there was general agreement that data analysis and the use of pre-payment and post-payment fraud detection tools were the most effective methods for both prevention and detection. The use of fraud detection software is increasing both within plans and by the Agency itself.

Domain iv.b

How often do health plan compliance officers/teams interact with providers in the health plan networks? What types of contact and interactions do the compliance officers/teams have with providers? How do plans document and track their efforts to deter fraud and abuse?

Semi-structured interviews of Compliance Officers and/or key members of their teams.

The frequency and the nature of interactions between managed care health plan compliance officers/teams and providers focused primarily on targeted educational sessions and communication of updated policies and procedure rather than being restricted to specific scheduled times. The communication frequency also depended on the

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Table 86: Domain iv Summary and Conclusions by Domain Questions (Continued) Domain Data Source and Approach Answer

needs of the providers – compliance officers indicated continuous and immediate availability to providers as needed and/or requested. Various methods of communication between compliance officers and providers were utilized depending on the type and urgency of the issues. Communication between parties occurred by mailed correspondence, such as newsletters, as well as by phone and email. Procedures for tracking and documenting of efforts above and beyond those required by the Agency to deter fraud and abuse vary across managed care plans. While some use manual systems, others utilize computer software programs. The use of data analysis is an increasingly important tool in detection and deterrence of fraud and abuse.

Domain iv.c

How do health plan compliance officers/teams measure the effectiveness of the health plan policies and procedures related to program integrity?

Semi-structured interviews of Compliance Officers and/or key members of their teams.

A variety of techniques and measures were identified by the compliance officers as means to gauge the effectiveness including: tracking soft dollar savings, periodic reviews and updates to their policies in response to the previous year’s activities, monitoring volume of cases, time constraints on cases, amounts recovered from providers, internal audit outcomes, and monthly audits of their investigators. While the techniques for measuring the effectiveness of policies and procedures varied amongst plans, there was general agreement that data analysis and the use of pre-payment and post-payment anti-fraud tools were the most effective methods for both prevention and detection.

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Domains v – ix

The LIP program in Florida was designed to ensure continued government support for the provision of healthcare services to Medicaid, underinsured, and uninsured populations. The program is now over seven years old and is a critical source of funding for providers serving a significant proportion of low-income and indigent populations and an innovative component of the 1115 waiver. Using the LIP Milestone Statistics and Findings Report for DY7: SFY 2012–2013, the Primary Care and Alternative Delivery Systems Expenditures Report for DY7, and the provider submitted proposals and quarterly reports for the Tier-One and Tier-Two initiatives, this evaluation sought to describe the extent to which LIP funding affects the health of low-income populations. A summary of the findings are detailed in Table 87 below. Table 87: Summary and Conclusion by Domain Question

Table 87: Summary and Conclusion by Domain Question (Continued)

Domain Resources Answer Domain v.a and v.b How has LIP funding improved access to care for uninsured/underinsured recipients? That is, how many uninsured and underinsured recipients receive services through LIP funding? What types of services are being provided and in what settings?

The resource used to answer the research questions related to Domain v was the LIP Milestone Statistics and Findings Report for DY7: SFY 2012–2013.

Overall, the number of uninsured, underinsured, and Medicaid individuals served and the types and number of outpatient services such as primary care, OB/GYN, mental health, dental, radiology services, laboratory services, disease management, care coordination, and prescription drugs furnished by non-hospital providers has increased. For hospital providers, the number of individuals served with Medicaid has increased but the number of uninsured and underinsured individuals served has decreased. The types of services provided in the hospital setting included discharges, inpatient days, emergency care services, hospital-based outpatient services, affiliated services, and prescriptions filled.

Domain vi.a, vi.b, vi.c, and vi.d

How does LIP funding impact access to and use of services by different population groups? Does it (LIP funding) increase access to services in particular areas? How many programs funded by LIP, including Tier-One and Tier-Two initiatives, are focused on reducing disparities in the provision of healthcare services or health outcomes?

The resources used to answer the four research questions related to Domain vi were the Primary Care and Alternative Delivery Systems Expenditures Report for DY7, the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Approved Initiatives, and the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports.

Generally speaking, the Tier-One and Tier-Two initiatives decreased disparities in the provision of healthcare services (e.g., primary care services, dental, and disease management) in both urban and rural geographic locations and for multiple demographic, socioeconomic, and disease-specific population groups including individuals with CHF, COPD, diabetics, patients with sickle cell disease, the uninsured, and low income individuals. Almost all of the

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Table 87: Summary and Conclusion by Domain Question (Continued)

Domain Resources Answer What are these programs doing to reduce disparities and how successful are they?

Tier-One programs were focused on reducing disparities in the provision of healthcare services or health outcomes. Nearly three-quarters of the Tier-Two Top 15 initiatives focused on reducing disparities in health outcomes and approximately 30% focused on reducing disparities in the provision of healthcare services. While success varied, in order to reduce disparities the programs established new clinics, implemented new services, redesigned processes, and hired additional personnel among other things.

Domain vii.a, vii.b, vii.c, vii.d, and vii.e

What are the goals of the Tier-One Milestone programs? What interventions/activities are they using to enhance quality of care and the health of low-income populations? Are they successful? What are the goals of the Tier-Two Milestone initiatives? How many of the initiatives are focused on access to care and quality of care? How are the Top 15 hospitals working to meet their goals? Are they successful?

The resources used to answer the research questions related to Domain vii were the Primary Care and Alternative Delivery Systems Expenditures Report for DY7, the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Approved Initiatives, and the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports.

Goals of the Tier-One initiatives included: increasing access to primary care, care coordination, and disease management services; reducing potentially avoidable hospitalizations, hospital readmissions and emergency department visits; providing dental services to children in foster care and low income and underinsured populations; and providing a comprehensive medical home for children, adults, and seniors as well as others. Interventions included establishing new clinics and expanding services. Goals of the Tier-Two initiatives were to reduce avoidable hospital readmissions and emergency department visits; improve access to and quality of care for patients with certain health conditions including CHF, COPD, and sickle cell disease; and improve quality of care and health status for obese low income populations, pregnant women, babies in the NICU, and individuals with depression among others. All of the Tier-One initiatives were focused on access to care or quality of care. 70% or more of the Tier-Two initiatives impacted quality of care and access to care. Overall, both the Tier-One and Tier-Two initiatives provided better care

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Table 87: Summary and Conclusion by Domain Question (Continued)

Domain Resources Answer coordination and increased access to primary care and disease management services.

Domain viii.a, viii.b, viii.c, viii.d, and viii.e

How are the Tier-One Milestone initiatives proposing to affect population health? Are they targeting particular groups of recipients or health conditions? Are they successful in achieving their objectives? How are the Tier-Two Milestone initiatives proposing to affect population health? Are they targeting particular groups of recipients or health conditions? What interventions/activities are they engaging in to impact population health? Are they successful?

The resources used to answer the research questions related to Domain viii were the Primary Care and Alternative Delivery Systems Expenditures Report for DY7, the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Approved Initiatives, and the 2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Quarterly Reports.

Tier-One and Tier-Two Milestone initiatives affected population health by increasing access to primary care services; improving management of chronic illnesses such as diabetes, hypertension, and cardiovascular disease; and focusing on population groups including but not limited to women, children, and the homeless. Specific activities included implementation of Specialty Care Coordination Programs (SCCPs) for cardiovascular and pulmonary conditions; implementation of protocols to reduce infections in neonates, and the development and use of a depression screening tool for PCPs with success varying by initiative.

Domain ix.a, ix.b, ix.c, ix.d, and ix.e

How do expenditures for services funded through the Tier-One Milestone initiatives differ from other LIP expenditures? How do the services provided under Tier-One Milestone initiatives differ from those provided under other LIP funding? That is, do Tier-One Milestone expenditures result in more preventive and outpatient care than emergency department and inpatient visits? Do Tier-One Milestone initiatives result in lower expenditures for recipients who are served by them? Do the Tier-Two Milestone initiatives impact expenditures for care for the uninsured/underinsured? How are expenditures affected? What initiatives are successful in helping recipients to access the

The resources used to answer the research questions related to Domain ix were the 2012–2013 LIP Tier-One Milestone (STC #84) Approved Applications, the Primary Care and Alternative Delivery Systems Expenditures Report for DY7, and the 2012–2013 Tier-Two Milestone (STC #85) Top 15, Quarterly Reports.

Expenditures for services funded through the Tier-One Milestone $35 million primary care award differ from other LIP expenditures in that specifically designated funds were allotted based on a competitive solicitation process and were based on a proposed budget that incorporates maximum award levels and project goals stated a priori. Tier-One and Tier-Two initiatives impacted per-capita costs and the cost-effectiveness of care by providing coordinated acute care, disease management, and primary medical care, and dental services in outpatient settings to reduce the numbers of avoidable emergency department and inpatient visits. Several of the Tier-One initiatives provided various services to patients based on a sliding scale or at no cost. Several Tier-Two initiatives reduced expenditures for care for the uninsured, underinsured, and Medicaid populations.

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Table 87: Summary and Conclusion by Domain Question (Continued)

Domain Resources Answer appropriate level of care and prevent the need for emergency or inpatient care?

Emergency Department Diversion programs, Readmission Reduction Programs (RRPs), and establishing condition-specific outpatient clinics were a few of the initiatives used to improve access to care and prevent the need for emergency or inpatient care.

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Appendices: Domains i and ii

Appendix A: CAHPS Survey Data

Survey and Sampling Methodology The goal of the Medicaid demonstration enrollee satisfaction survey was to first conduct a Benchmark survey that would be followed up annually in Broward and Duval counties. This would be augmented by a similar pattern of Benchmark measures in 2007 followed by annual replications in the tri-county rural area comprised of Baker, Clay, and Nassau counties (Table A1 below). Table A1: Annual Medicaid Enrollee Surveys Fielded

Fieldwork Timeline

Survey Period Instruments Used

Demonstration Urban Counties: Broward and Duval (2006 – 2012)

Fall/Winter 2006 Benchmark Year (pre-demonstration) Survey

Modified CAHPS®health plan survey version 3.0 Adult MediPass Adult non-MediPass Child MediPass Child non-MediPass

Winter 2007/2008

Year 1 Follow-Up Survey Modified CAHPS®health plan survey version 4.0 Adult Medicaid Child Medicaid (including Item Set for Children with Chronic Conditions)

Spring 2009 Year 2 Follow-Up Survey

Spring 2010 Year 3 Follow-Up Survey

Demonstration Rural Counties: Baker, Clay, and Nassau (2007 – 2012)

Winter 2007/2008

Benchmark Year (pre-demonstration) Survey

Modified CAHPS®health plan survey version 4.0 Adult Medicaid Child Medicaid (including Item Set for Children with Chronic Conditions)

Spring 2009 Year 1 Follow-Up Survey

Spring 2010 Year 2 Follow-Up Survey

Demonstration Plans and Non-Demonstration Plans

Fall 2011 – Spring 2012

DY 6 Modified CAHPS® Health Plan Survey version 4.0 Adult Medicaid Child Medicaid

Demonstration Plans and Non-Demonstration Plans

7/1/2012 – 6/30/2013

DY 7 Modified CAHPS® Health Plan Survey version 4.0 Adult Medicaid Child Medicaid

Demonstration Plans and Non-Demonstration Plans

7/1/2013 – 6/30/2014

DY 8 ENGLISH version: Modified CAHPS® Health Plan Survey version 5.0 Adult Medicaid Child Medicaid SPANISH version: Modified CAHPS® Health Plan Survey version 4.0 Adult Medicaid Child Medicaid

Note. The survey fieldwork methodology for DY6 was generally consistent with previous years. However, in consultation with AHCA and to meet multiple survey objectives, the sample design was modified to account for fieldwork in all Florida counties including the demonstration and non-demonstration counties.

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Table A2 below provides details of the Survey Follow-Up Year and the corresponding Demonstration Year. Seven rounds of the survey (DY1 [Benchmark Year], DY2, DY3, DY4, DY6, DY7, and DY8) were conducted in Broward and Duval (urban) counties. Six rounds (DY2, DY3, DY4, DY6, DY7, and DY8) were conducted in Baker, Clay, and Nassau (rural) counties. Survey fieldwork was not conducted in DY5. Starting in DY6, fielding of the CAHPS survey in the demonstration counties was combined with fielding of the survey in the non-demonstration counties; hence this is the third year that comparisons between demonstration and non-demonstration counties can be made. Table A2: CAHPS Field Dates for Demonstration Years

CAHPS Data Year (Current Label)

CAHPS Field Dates

Survey Follow-Up

Year

Month and Year Survey Fielded

6 months prior

Demonstration Year (DY)

Benchmark Year (Fall 06)

7/27/06 – 11/5/06 Benchmark July – November 2006 DY1 (B)

Year 1 (Winter 07/08)

1/10/08 – 3/28/08 1 January – March 2008 Jul 07 DY2

Year 2 (Spring 09) 3/13/09 – 6/28/09 2 March – June 2009 Sep 08 DY3

Year 3 (Spring 10) 5/12/10 – 7/22/10 3 May – July 2010 Nov 09 DY4

Year 4 (10/1/11 –6/30/12)

10/1/11 – 6/30/12 4 October 2011 – June 2012 Apr 11 DY6

Year 5 (7/1/12 –6/30/12)

7/1/12 – 6/30/13 5 July 2012 – June 2013 Jan 12 DY7

Year 6 (7/1/13 –6/30/14)

7/1/13 – 6/30/14 6 July 2013 – June 2014 Jan 13 DY8

Table A3 shows the final unweighted sample sizes (total number of completed interviews) for each year. Findings from this survey are intended to provide information about the study population, which consists of Medicaid enrollees deemed eligible for this study. Only a sample of these enrollees was contacted. In order to calculate estimates that refer to all eligible enrollees, sampled enrollees were weighted by two factors that reflected (1) their probability of being called and (2) their estimated probability of responding given they were called. Together, these two weighting factors allowed the completed interviews to reflect the sample (i.e., those who were called), and the sample to reflect the population of enrollees from which the sample was originally selected. The unweighted sample size and the weighted percentages are presented in each table. Note that the percentages in the table columns may not add up to 100 in some instances. Further information on the survey instrument, statistical weighting, and disposition reports are available upon request. Demographic and health status characteristics for Broward and Duval counties combined, the rural counties (Baker, Clay, and Nassau), and the non-demonstration counties (Alachua, Bay, Bradford, Brevard, Calhoun, Charlotte, Citrus, Collier, Columbia, Dade, De Soto, Dixie, Escambia, Flagler, Franklin, Gadsden, Gilchrist, Glades, Gulf, Hamilton, Hardee, Hendry, Hernando, Highlands, Hillsborough, Holmes, Indian River, Jackson, Jefferson, Lafayette, Lake, Lee, Leon, Levy, Liberty, Madison, Manatee, Marion, Martin, Monroe, Okaloosa, Okeechobee, Orange, Osceola, Palm Beach, Pasco, Pinellas, Polk, Putnam, St. Johns, St. Lucie, Santa Rosa, Sarasota, Seminole, Sumter, Suwannee, Taylor, Union, Volusia, Wakulla, Walton, and Washington) combined are provided in the Demographics section in this Appendix (Appendix A).

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Table A3: Sample Sizes for the Benchmark DY1 (B), DY2, DY3, DY4, DY6, DY7, and DY8 Surveys (Unweighted)

Broward Duval Rural Total

DY1 (Benchmark) 4,197 1,570 – 5,767

DY2 4,345 1,864 997 7,206

DY3 3,917 2,235 752 6,904

DY4 3,515 2,400 1,099 7,014

DY6 Total Reform 5,581

DY6 Total Non-Reform 7,515

DY7 Total Reform 3,260

DY7 Total Non-Reform 8,197

DY8 Total Reform 3,701

DY8 Total Non-Reform 6,165 Note. Seven rounds of the survey (DY1 [Benchmark Year], DY2, DY3, DY4, DY6, DY7, and DY8) were conducted in Broward and Duval (urban) counties. Six rounds (DY2, DY3, DY4, DY6, DY7, and DY8) were conducted in Baker, Clay, and Nassau (rural) counties. Survey fieldwork was not conducted in DY5. Starting in DY6, fielding of the CAHPS survey in the demonstration counties was combined with fielding of the survey in the non-demonstration counties; hence this is the second year that comparisons between demonstration and non-demonstration counties can be made. MediPass is included in non-demonstration plan comparisons. Each strata were categorized by plan in demonstration and non-demonstration counties, not by individual county, so only the total number of completed interviews for the demonstration and non-demonstration plans are provided for DY6 (See Table A5). Differences in the sample sizes across the years are attributed to two factors. First, the number of plans change and the number of enrollees in each plan change in each year. Accordingly, a targeted number of completes that was specific to each year and each plan strata was set. Second, the percentage who responded was slightly different in each year. It is important to note that the intent is not to sample the exact number in each year.

Response Rates The response rate refers to the ratio of people who completed the survey to the number of people in the sample. Three different response rates were calculated, reflecting differences in the denominator based on survey eligibility and whether an individual was actually contacted. All three response rates are presented in Table A4. The Raw Response Rate is the total number of completed interviews divided by the total number of eligible individuals that the survey research center attempted to reach. Raw Response Rates were calculated at 18%, 19%, 21%, 22%, 22%, 22%, 21%, 20%, 16%, and 14% for the DY1 (B), DY2, DY3, DY4, DY6 Reform, DY6 Non-Reform, DY7 Reform, DY7 Non-Reform, DY8 Reform, and DY8 Non-Reform surveys, respectively. The Response Rate Adjusted for Ineligibles excludes those groups that were not eligible for the survey, such as individuals who live in group quarters or telephone numbers determined to be fax/data lines, business, or other institutional telephone numbers. This ineligibility was determined after sampling and was not determined before the survey was launched. Therefore, this rate is reflective of the population under study. Response Rates Adjusted for Ineligibles for the DY1 (B), DY2, DY3, DY4, DY6 Reform, DY6 Non-Reform, DY7 Reform, DY7 Non-Reform, DY8 Reform, and DY8 Non-Reform surveys were 40%, 34%, 40%, 38%, 31%, 31%, 28%, 27%, 25% and 22%, respectively. The Cooperation Rate is a ratio of those who completed the survey to the number of individuals who were contacted and eligible for the study. The Cooperation Rates were 65%, 64%, 76%, 73%, 70%, 69%, 69%, and 66%, 84%, and 53% for the DY1 (B), DY2, DY3, DY4, DY6 Reform, DY6 Non-Reform, DY7 Reform, DY7 Non-Reform , DY8 Reform, and DY8 Non-Reform surveys, respectively.

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Table A4: Response Rates for the DY1 (B), DY2, DY3, DY4, DY6, DY7, and DY8 Surveys

Benchmark

Year DY1 (B)

(pre-

demonstratio

n) Survey for

Demonstratio

n Urban

Counties

(Broward and

Duval)

DY2 Survey

for

Demonstratio

n Urban

Counties

(Broward and

Duval) and

Benchmark

Year DY 2(B)

(pre-

demonstratio

n) Survey for

the Three

Rural

Counties

(Baker, Clay,

and Nassau)

DY3 Survey

for

Demonstrati

on Urban

Counties

(Broward

and Duval)

and for the

Three Rural

Counties

(Baker, Clay,

and Nassau)

DY4 Survey

for

Demonstra

tion Urban

Counties

(Broward

and Duval)

and for the

Three Rural

Counties

(Baker,

Clay, and

Nassau)

DY6

Survey

for

Reform

Plans

DY6

Survey

for

Non-

Reform

Plansc

DY7

Survey

for

Reform

Plans

DY7

Survey

for

Non-

Refor

m

Plans

DY8

Survey

for

Refor

m

Plans

DY8

Survey

for

Non-

Reform

Plans

Raw Response Rate

17.8% 18.6% 20.9% 22.4% 21.9% 21.7% 21.1% 20.0% 15.8% 14.2%

Response Rate Adjusted for “Ineligibles”

a

39.7% 33.8% 39.7% 38.4% 30.5% 30.5% 27.6% 27.0% 25.2% 22.3%

Cooperation Response Rate

b

64.6% 64.1% 76.0% 72.6% 70.4% 69.0% 69.2% 65.9% 83.9% 53.0%

Note. aThe denominator excludes individuals who were deceased, physically/mentally unable, could not answer the questions

due to language barrier, were unable to be contacted at the available telephone number, or who otherwise did not meet the sample eligibility criteria.

bThe denominator includes all telephone contacts (completed interviews, partially completed

interviews, and those who refused to participate in the survey). cNon-demonstration plans were in the following counties:

Alachua, Bay, Bradford, Brevard, Calhoun, Charlotte, Citrus, Collier, Columbia, Dade, De Soto, Dixie, Escambia, Flagler, Franklin, Gadsden, Gilchrist, Glades, Gulf, Hamilton, Hardee, Hendry, Hernando, Highlands, Hillsborough, Holmes, Indian River, Jackson, Jefferson, Lafayette, Lake, Lee, Leon, Levy, Liberty, Madison, Manatee, Marion, Martin, Monroe, Okaloosa, Okeechobee, Orange, Osceola, Palm Beach, Pasco, Pinellas, Polk, Putnam, St. Johns, St. Lucie, Santa Rosa, Sarasota, Seminole, Sumter, Suwannee, Taylor, Union, Volusia, Wakulla, Walton, and Washington.

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Instrumentation DY1: Benchmark Year Survey for Urban Counties (Broward and Duval)

During fall 2006, the University of Florida Survey Research Center in the Bureau of Economic and Business Research (BEBR) administered four versions of the Benchmark Year survey questionnaire in English and Spanish, via telephone, divided among the subgroups shown below:

Adult MediPass

Adult non-MediPass

Child MediPass

Child non-MediPass

The non-MediPass categories include individuals in fee-for-service (FFS), PSN, and HMO plans. The distinction between MediPass and non-MediPass was important to ensure that individuals were asked specifically about their experiences with Medicaid (MediPass) and the various types of health plans (non-MediPass: FFS, PSN, and HMO). In the child survey versions, a proxy (parent, guardian, or other designated party where applicable) responded on behalf of children under age 21. The majority of items used in the Benchmark Year survey were drawn from the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) version 3.0. This survey belongs to a family of standardized survey instruments used widely in the healthcare industry to assess enrollees’ experiences and satisfaction with various aspects of their health care (see https://www.cahps.ahrq.gov). The CAHPS® health plan survey was initially launched in 1997 by a consortium including Harvard University, RTI, and RAND, with funding from an entity now called the Agency for Healthcare Research and Quality (AHRQ). The customized CAHPS-style health plan survey used in the Benchmark Year survey was specifically designed for Medicaid enrollees to report on their

enrollment and coverage;

ability to access care, and assessment of any barriers to care;

relationships with healthcare providers;

global satisfaction and rating of healthcare providers, specialists, health plans, and health care;

health care utilization;

perception of the level of courtesy, respect, and helpfulness of office staff; and

ability to communicate with their providers. Further, respondents self-reported on their (or their child’s) health status and demographic characteristics including age, gender, educational attainment, and race/ethnicity. At the request of AHCA staff, supplemental questions were added to the questionnaires that specifically addressed experiences unique to Florida Medicaid, and especially germane to Broward and Duval counties, the Medicaid demonstration urban counties. Respondents were queried about health plan selection, the enrollment process, and travel time required to see a provider. In the introductory language for each questionnaire, potential respondents were assured that participation in the survey was voluntary and were further assured that their Medicaid benefits would not be affected should they elect not to participate.

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DY2: Year 1 Follow-Up Survey for Urban Counties (Broward and Duval), and Benchmark Year Survey for the Three Rural Counties (Baker, Clay, and Nassau)

The CAHPS® survey instrument was subsequently updated to version 4.0, which deleted and reordered certain core questions. These deletions included key questions and composites that formed the CAHPS® groupings used to compare provider performance. In certain instances, questions were reworded and response categories were accordingly changed. This new version also added a module to identify children with chronic conditions, which allowed direct comparisons of the experiences of children with special healthcare needs with those of similar children in other health plans and/or the general population of children in the same plan. The chronic condition set for children included

a five-item screener classifying children with chronic conditions during the analysis stage, and

a set of supplemental questions identifying access to prescription medicines, access to specialized services, and family-centered care (e.g., having a personal doctor or nurse who knows the child, shared decision-making, getting needed information, and coordination of care and services).

At AHCA’s direction, questions specific to Florida Medicaid and the demonstration were added to the new CAHPS® version 4.0 in order to track availability and use of information, awareness, and use of Choice Counselor services when selecting a health plan, the accrual and use of the Enhanced Benefits Reward$ program credits, and prescription drug availability and utilization. In order to review the experiences in Broward and Duval counties from a temporal perspective, the CAHPS® project team further customized the instrument by re-inserting certain key questions into the 4.0 model. The following two versions of the urban Year 1 Follow-Up survey (along with Benchmark measures for the rural expansion counties) were fielded in English and Spanish, and 7,206 interviews were completed for the five participating counties:

Adult Medicaid, and

Child Medicaid (including Item Set for Children with Chronic Conditions). DY3: Year 2 Follow-Up Survey for Urban Counties (Broward and Duval), and Year 1 Follow -Up Survey for the Three Rural Counties (Baker, Clay, and Nassau)

The Year 2 Follow-Up survey for the urban counties and the Year 1 Follow-Up survey for the three rural counties were fielded from March – June 2009, using the customized CAHPS® version 4.0 adult and child instruments. DY4: Year 3 Follow-Up Survey for Urban Counties (Broward and Duval), and Year 2 Follow -Up Survey for the Three Rural Counties (Baker, Clay, and Nassau)

The Year 3 Follow-Up survey for the urban counties and the Year 2 Follow-Up survey for the three rural counties were fielded from May 12, 2010 – July 9, 2010, using the customized CAHPS® version 4.0 adult and child instruments.

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DY6: Reform Plans and Non-Reform Plans

The DY6 survey fieldwork for all Florida counties including the demonstration and non-demonstration counties was conducted from October 1, 2011 – June 30, 2012, using the customized CAHPS® version 4.0 adult and child instruments. DY7: Reform Plans and Non-Reform Plans

The DY7 survey fieldwork for all Florida counties including the demonstration and non-demonstration counties was conducted from July 1, 2012 – June 30, 2013, using the customized CAHPS® version 4.0 adult and child instruments. DY8: Reform Plans and Non-Reform Plans

The DY8 survey fieldwork for all Florida counties including the demonstration and non-demonstration counties was conducted from July 1, 2013 – June 30, 2014, using the customized CAHPS® version 5.0 adult and child instruments for English-speaking respondents, and CAHPS® version 4.0 adult and child instruments for Spanish speakers.

Survey Sampling

The resulting probability sample had the property that two individuals within the sampling frame for a given county and plan had an equal chance of being called to respond to the survey if they belonged to groups of the same size. Individuals within the same group had the same phone number and the same plan. Two individuals within the same group would have zero probability of both being included in the sample. Assuming no nonresponse, the probability sample approximated a stratified randomized survey sample with individuals in the same strata sharing the same county, plan, and group size. Survey data were weighted to reflect plan share in each county and survey nonresponse, enabling population estimates to be made. DY1: Benchmark Year Survey for Urban Counties (Broward and Duval)

The initial population for the survey was specified as Medicaid enrollees living in the two demonstration counties prior to the implementation. Enrollees had to have at least 6 months of continuous participation in one of the eligible plans or MediPass and be deemed eligible to be enrolled in the demonstration. The demonstration was implemented in the urban counties on July 1, 2006, and the Benchmark Year survey for Broward and Duval counties was fielded in the fall/winter of 2006 (early in the demonstration). So, it is possible that at the time of the survey, enrollees may have been categorized in a different plan type than they were in the previous six months. A total of 5,767 surveys were completed. DY2: Year 1 Follow-Up Survey for Urban Counties (Broward and Duval), and Benchmark Year Survey for the Three Rural Counties (Baker, Clay, and Nassau)

The initial population for the survey was determined in mid-December 2007 and was composed of all Medicaid recipients who, for 6 consecutive months,

resided in Broward, Duval, Baker, Clay, or Nassau counties;

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were participating in one of the 26 recipient assistance categories that qualify for the demonstration; and

participated during the time period of primary focus (June – November 2007). A total of 7,206 interviews were completed for the five participating counties. The demonstration was implemented in the urban counties on July 1, 2006, and implemented in the rural counties one year later. So, the Benchmark Year survey for the rural counties was fielded at the same time as the Year 1 Follow-Up survey for the urban counties. Data from the Benchmark Year survey for the rural counties reflects enrollees’ experiences early in the first year of the demonstration in those counties. DY3: Year 2 Follow-Up Survey for Urban Counties (Broward and Duval), and Year 1 Follow-Up for the Three Rural Counties (Baker, Clay, and Nassau)

The initial population for the survey was determined in January and February 2009 and was composed of all Medicaid recipients who, for 6 consecutive months,

resided in Broward, Duval, Baker, Clay, or Nassau counties;

were participating in one of the 26 recipient assistance categories that qualify for the demonstration; and

participated in the demonstration during the time period of primary focus (June – November 2008).

Consistent with the preceding two years, a goal of 315 completed interviews was set for each participating healthcare plan/stratum, wherever possible. In instances where the provider had a disproportionately larger number of enrollees, targets were adjusted upwards and were correspondingly reduced for significantly smaller plans. A total of 6,904 interviews were completed for the five participating counties. DY4: Year 3 Follow-Up Survey for Urban Counties (Broward and Duval), and Year 2 Follow -Up for the Three Rural Counties (Baker, Clay, and Nassau)

The initial population for the survey was determined early in 2010 and was composed of Medicaid recipients who, for 6 consecutive months,

resided in Broward, Duval, Baker, Clay, or Nassau counties;

were participating in one of the 26 recipient assistance categories that qualify for the demonstration; and

participated in the demonstration during the time period of primary focus (July – December 2009).

Consistent with the preceding three years, a goal of 315 completed interviews was set for each participating healthcare plan/stratum, wherever possible. In instances where the provider had a disproportionately larger number of enrollees, targets were adjusted upwards and were correspondingly reduced for significantly smaller plans. A total of 7,014 telephone interviews were completed for the five participating counties.

170

DY6: Reform Plans and Non-Reform Plans

The survey fieldwork methodology for DY6 was generally consistent with previous years (Duncan, Hall, Brumback, Zhang, Bell, et al., 2011; Duncan, Hall, Brumback, Zhang, Chorba, Bell, et al., 2011; Duncan, Hall, Brumback, Zhang, Chorba, Bilello, et al., 2011). However, in consultation with AHCA and to meet multiple survey objectives, the sample design was modified to account for fieldwork in all Florida counties including the demonstration and non-demonstration counties. Eligibility for all survey participants was determined in 2011 and the eligibility listing was composed of Medicaid recipients who, for 6 consecutive months,

resided in Broward, Duval, Baker, Clay, or Nassau counties;

were participating in one of the 26 recipient assistance categories that qualify for the demonstration; and

participated in the demonstration during the time period of primary focus (April – September 2011).

A goal of 384 completed interviews was set for each participating healthcare plan/stratum, wherever possible. In instances where the provider had a disproportionately larger number of enrollees, targets were adjusted upwards and were correspondingly reduced for significantly smaller plans. A total of 12,787 telephone interviews were completed for all counties participating in Florida Medicaid (both demonstration and non-demonstration).

DY7: Reform Plans and Non-Reform Plans

The survey fieldwork methodology for DY7 was similar to that of DY6 and previous years (Duncan, Hall, Brumback, Zhang, Bell, et al., 2011; Duncan, Hall, Brumback, Zhang, Chorba, Bell, et al., 2011; Duncan, Hall, Brumback, Zhang, Chorba, Bilello, et al., 2011). In consultation with AHCA and to meet multiple survey objectives, the sample design was modified in DY6 to account for fieldwork in all Florida counties including the demonstration and non-demonstration counties. Eligibility for all survey participants was determined in 2012 and the eligibility listing was composed of Medicaid recipients who, for 6 consecutive months,

resided in Broward, Duval, Baker, Clay, or Nassau counties;

were participating in one of the 26 recipient assistance categories that qualify for the demonstration; and

participated in the demonstration during the time period of primary focus (January – June 2012).

A goal of 384 completed interviews was set for each participating healthcare plan/stratum, wherever possible. In instances where the provider had a disproportionately larger number of enrollees, targets were adjusted upwards and were correspondingly reduced for significantly smaller plans. A total of 11,457 telephone interviews were completed for all counties participating in Florida Medicaid (both demonstration and non-demonstration).

DY8: Reform Plans and Non-Reform Plans

171

The survey fieldwork methodology for DY8 was similar to that of DY7 and previous years (Duncan, Hall, Brumback, Zhang, Bell, et al., 2011; Duncan, Hall, Brumback, Zhang, Chorba, Bell, et al., 2011; Duncan, Hall, Brumback, Zhang, Chorba, Bilello, et al., 2011). Eligibility for all survey participants was determined in 2013 and the eligibility listing was composed of Medicaid recipients who, for 6 consecutive months,

resided in Broward, Duval, Baker, Clay, or Nassau counties;

were participating in one of the 26 recipient assistance categories that qualify for the demonstration; and

participated in the demonstration during the time period of primary focus (January – June 2013).

For non-Reform plans, a goal of 323 completed interviews was set for each participating healthcare plan/stratum, wherever possible, while the goal for Reform plans was set for 515 completes. In instances where the provider had a disproportionately larger number of enrollees, targets were adjusted upwards and were correspondingly reduced for significantly smaller plans. A total of 9,695 telephone interviews were completed for all counties participating in Florida Medicaid (both demonstration and non-demonstration).

The defined strata used in DY8 for all Florida counties are summarized in Table A5 on the next page.

172

Table A5: Strata for DY8 Year 4 Follow-Up Survey for Plans in all Florida Counties Stratum

No. Participating Health Plan/Stratum Mail Web Telephone Total

1 MediPass (Non-MPN) 8 1 525 534

2 PSN Better Health, LLC Reform 12 1 527 540

3 PSN CMS Reform 7 1 504 513

4 PSN First Coast Advantage Reform 7 1 509 520

5 PSN Integral Non-Reform 1 0 347 349

6 PSN Prestige Non-Reform 8 1 341 350

7 PSN SFCCN Reform 5 1 503 509

8 PSN SFCCN Non-Reform 5 1 329 335

9 HMO Amerigroup Non-Reform 4 0 324 328

10 HMO Freedom Reform 2 0 91 93

11 HMO Freedom Non-Reform 5 0 309 314

12 HMO Wellcare/ HealthEase Non-Reform 4 0 314 319

13 HMO Healthy Palm Beaches Non-Reform 2 0 320 324

14 HMO Humana Reform 3 1 219 224

15 HMO Humana Non-Reform 4 0 331 337

18 HMO Medica Non-Reform 2 0 134 136

19 HMO Molina Healthcare Reform 9 1 505 515

20 HMO Molina Healthcare Non-Reform 8 0 336 344

22 HMO Preferred D/B/A Care Florida Non-Reform

1 0 117 118

23 HMO Preferred Medical Plan Non-Reform 5 0 348 353

24 HMO Simply Healthcare Plan Non-Reform 4 0 320 324

25 HMO Staywell Non-Reform 8 0 323 333

26 HMO Sunshine Reform 6 1 516 524

27 HMO Sunshine Non-Reform 5 0 369 374

28 HMO United Healthcare Plan Reform 3 0 260 263

29 HMO United Healthcare Plan Non-Reform 6 0 305 312

32 HMO Vista D/B/A Buena Vista Non-Reform

7 1 325 333

33 HMO Vista Health plan of S. Fl. Non-Reform

4 0 344 348

Totals 145 11 9,695 9,866

173

Fieldwork for DY8 was conducted using a three-tiered data collection approach. 1. Adult and child telephone interviews. 2. Letters were sent to qualified enrollees informing them that telephone attempts to reach them

were unsuccessful, and offering them the opportunity to complete the questionnaire in a mail-in, written format.

3. Or, via the internet. In an attempt to reach nonresponders, a follow-up letter was mailed. The telephone approach resulted in 9,695 completed interviews, 145 responses were mailed in to the Survey Research Center at the University of Florida, and there were 11 completes via the internet.

Demographics This section presents a series of tables (Tables A6 – A19) showing the demographic characteristics at the county level for Broward and Duval counties combined and Baker, Clay, and Nassau counties combined (rural counties). Table A6: Overall Health Score, Reform, DY1 – DY8

In general, how would you rate your (your child’s) overall health?

DY1 (B) DY2 DY3 DY4 DY6 DY7 DY8

Broward & Duval Counties (p < .0001)

Sample Size 5,733 6,171 6,118 5,894 4,115 2,941 3,380

Excellent 35.3% 35.3% 35.9% 36.7% 40.0% 44.7% 43.2%

Very good 25.3% 24.5% 28.6% 25.6% 24.6% 25.0% 26.0%

Good 23.5% 24.6% 20.9% 22.8% 21.0% 20.6% 19.7%

Fair 11.1% 11.5% 10.6% 11.3% 11.0% 7.6% 9.0%

Poor 4.9% 4.1% 4.1% 3.6% 3.5% 2.1% 2.1%

Rural Counties (p = 0.08)a

Sample Size 991 747 1,096 372 249 286

Excellent 35.2% 36.2% 36.6% 33.3% 33.3% 38.5%

Very good 29.5% 30.1% 30.2% 31.6% 29.5% 24.4%

Good 20.8% 16.7% 20.6% 24.7% 25.7% 20.8%

Fair 9.7% 11.9% 8.4% 5.2% 11.4% 12.2%

Poor 4.8% 5.1% 4.2% 5.2% 0.1% 4.2%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

174

Table A7: Overall Health Score, Reform vs. Non-Reform, DY6 – DY8 In general, how would you rate your (your child’s) overall health?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform (p = 0.499)

Reform vs. Non-Reform (p = <.0005)

Reform vs. Non-Reform (p =<.0001)

Sample Size 4,503 8,517 3,196 8,002 3,666 6,168

Excellent 39.5% 39.0% 43.9% 40.4% 42.9% 40.2%

Very Good 25.1% 23.9% 25.3% 24.0% 25.9% 23.5%

Good 21.2% 21.2% 21.0% 20.7% 19.7% 19.3%

Fair 10.6% 11.3% 7.9% 10.7% 9.2% 12.9%

Poor 3.6% 4.5% 1.9% 4.1% 2.2% 4.1%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Table A8: Enrollee Race, Reform, DY1 – DY8

What is your (your child’s) race?

DY1 (B) DY2 DY3 DY4 DY6 DY7 DY8

Broward & Duval Counties (p = .0408) Sample Size 5,680 6,108 6,052 5,814 4,038 2,888 3,394

White 31.0% 27.3% 26.4% 28.5% 28.9% 29.0% 27.0%

Black or African American 55.5% 55.6% 56.2% 54.6% 53.5% 54.7% 53.3%

Other 13.5% 17.1% 17.4% 16.9% 17.6% 16.3% 19.8%

Rural Counties (p = .0934)a

Sample Size 983 745 1,085 367 248 286

White 69.6% 66.6% 68.3% 75.1% 81.1% 68.3%

Black or African American 14.9% 20.1% 17.7% 10.9% 9.5% 13.0%

Other 15.5% 13.4% 14.0% 14.0% 9.3% 18.7%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

175

Table A9: Enrollee Race, Reform vs. Non-Reform, DY6 – DY8 What is your (your child’s) race?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p = <.0001) Reform vs. Non-Reform

(p = <.0001) Reform vs. Non-Reform

(p = <.0001)

Sample Size 4,421 8,323 3,142 7,837 3,680 6,186

White 32.7% 50.8% 33.1% 54.8% 29.9% 49.2%

Black or African American 50.1% 29.9% 51.1% 27.9% 50.4% 28.8%

Other 17.2% 19.4% 15.8% 17.2% 19.7% 22.0% Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Table A10: Enrollee Ethnicity, Reform, DY1 – DY8

Are you (is your child) of Hispanic or Latino origin or descent?

DY1 (B) DY2 DY3 DY4 DY6 DY7 DY8

Broward & Duval Counties (p < 0.0001) Sample Size 5,584 6,030 5,944 5,775 3,998 2,878 3,312

Yes, Hispanic 20.3% 20.4% 22.1% 22.5% 24.8% 24.1% 24.3%

No, not Hispanic 79.7% 79.6% 77.9% 77.5% 75.2% 75.9% 75.7%

Rural Counties (p = .505) Sample Size 969 734 1,084 360 247 282

Yes, Hispanic 10.5% 10.0% 11.0% 7.2% 11.1% 9.4%

No, not Hispanic 89.5% 90.0% 89.0% 92.8% 88.9% 90.6%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Table A11: Enrollee Ethnicity, Reform vs. Non-Reform, DY6 – DY8

Are you (is your child) of Hispanic or Latino origin or decent?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p = <.0001) Reform vs. Non-Reform

(p = <.0001) Reform vs. Non-Reform

(p = <.0001)

Sample Size 4,373 8,326 3,131 7,869 3,594 6,070

Yes, Hispanic 23.4% 40.1% 23.1% 41.6% 23.2% 40.4%

No, not Hispanic 76.6% 59.9% 76.9% 58.4% 76.8% 59.6% Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data

176

Table A12: Enrollee Age, Reform, DY1 – DY8 What is your (your child’s) age?

DY1 (B) DY2 DY3 DY4 DY6 DY7 DY8

Broward & Duval Counties (p < .0001) Sample Size 5,750 6,182 6,130 5,905 4,114 2943 3,383

0 – 10 47.6% 53.7% 53.0% 53.3% 56.1% 57.2% 57.5%

11 – 17 30.1% 26.3% 26.6% 26.5% 19.8% 22.6% 26.5%

18 and older 22.3% 20.0% 20.4% 20.2% 24.1% 20.2% 16.0%

Rural Counties (p = 0.6457) Sample Size 996 747 1,095 369 247 285

0 – 10 54.0% 51.3% 51.2% 55.6% 57.1% 47.0%

11 – 17 26.6% 28.7% 27.8% 20.9% 19.8% 32.5%

18 and older 19.4% 20.0% 21.0% 23.5% 23.2% 20.5%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Table A13: Enrollee Age, Reform vs. Non-Reform, DY6 –DY8 What is your (your child’s) age?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p = 0.039) Reform vs. Non-Reform

(p = 0.0003) Reform vs. Non-Reform

(p = <.0001) Sample Size 4,499 8,520 3,196 8,011 3,668 6,156

0 – 10 56.1% 52.3% 57.2% 51.3% 56.8% 50.6%

11 – 17 19.9% 21.1% 22.3% 22.2% 26.9% 22.1%

18 and older 24.1% 26.6% 20.4% 26.5% 16.3% 27.2%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Table A14: Enrollee Gender, Reform, DY1 – DY8

Are you (is your child) male or female?

DY1 (B) DY2 DY3 DY4 DY6 DY7 DY8

Broward & Duval Counties (p < .0001) Sample Size 5,752 6,193 6,137 5,902 4,136 2,949 3,387

Male 46.1% 45.7% 47.3% 47.1% 50.3% 44.9% 46.7%

Female 53.9% 54.3% 52.7% 52.9% 49.7% 55.1% 53.3%

Rural Counties (p = .604) Sample Size 996 751 1,098 371 248 285

Male 50.3% 44.8% 46.3% 54.5% 50.0% 52.4%

Female 49.7% 55.2% 53.7% 45.5% 50.0% 47.6%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

177

Table A15: Enrollee Gender, Reform vs. Non-Reform, DY6 – DY8 Are you (is your child) male or female?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p = .0086) Reform vs. Non-Reform

(p = .4563) Reform vs. Non-Reform

(p = .7116) Sample Size 4,523 8,520 3,203 8,008 3,672 6,149

Male 50.5% 46.4% 45.2% 46.6% 47.1% 46.5%

Female 49.5% 53.6% 54.8% 53.4% 52.9% 53.5%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Table A16: Enrollee Educational Attainment (Adults Only), Reform, DY1 – DY8

What is the highest grade or level of school you have completed?

DY1 (B) DY2 DY3 DY4 DY6 DY7 DY8

Broward & Duval Counties (p = 0.0378) Sample Size 1,328 1,900 1,310 1,075 1,032 536 402

8th

Grade or Less 10.4% 8.3% 9.0% 8.9% 7.5% 8.1% 6.0%

Some High School, but Didn’t Graduate 24.2% 24.5% 21.5% 24.9% 26.7% 25.2% 25.6%

High School Graduate, or GED 38.2% 35.6% 37.3% 37.3% 36.6% 29.3% 40.0%

Some College or more 27.2% 31.6% 32.1% 28.9% 29.2% 37.4% 28.4%

Rural Counties (p = 0.9966) Sample Size 258 167 214 83 47 52

8th

Grade or Less 6.4% 14.9% 11.0% 12.0% 5.1% 2.7%

Some High School, but Didn’t Graduate 27.9% 22.4% 22.8% 22.0% 28.0% 21.3%

High School Graduate, or GED 35.5% 36.2% 37.7% 37.3% 39.7% 46.5%

Some College or more 30.2% 26.5% 28.4% 28.7% 27.2% 29.5%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. Table A17: Enrollee Educational Attainment (Adults Only), Reform vs. Non-Reform, DY6 – DY8

What is your highest grade or level of school that you have completed?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p= 0.4966) Reform vs. Non-Reform

(p= 0.0487) Reform vs. Non-Reform

(p = 0.5582) Sample Size 1,122 2,292 583 1,972 454 1,616

8th grade or less 7.9% 10.4% 7.8% 9.3% 5.7% 8.6%

Some High School, but Didn't Graduate

26.2% 24.8% 25.5% 23.1% 25.2% 24.8%

High School Graduate, or GED 36.6% 37.2% 30.2% 39.2% 40.5% 38.7%

Some College or more 29.3% 27.7% 36.5% 28.5% 28.5% 27.9%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. Table reflects responses from adults only.

178

Table A18: Enrollee Eligibility Category, Reform, DY1 – DY7* Eligibility Category

DY1 (B) DY2 DY3 DY4 DY6 DY7

Broward & Duval Counties (p = <.0001) Sample Size 5,767 6,209 6,152 5,915 4,144 2,954

SSI 19.2% 18.9% 18.5% 18.7% 17.6% 11.7%

Non-SSI 80.8% 81.1% 81.5% 81.3% 82.4% 88.3%

Rural Counties (p = 0.4827) Sample Size 997 752 1,099 372 249

SSI 15.2% 14.2% 13.9% 15.7% 9.1%

Non-SSI 84.8% 85.8% 86.1% 84.3% 90.9%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. SSI enrollees are those who qualify for Medicaid based on whether they receive Supplemental Security Income. Non-SSI enrollees are those who do not qualify for Medicaid based on whether they receive Supplemental Security Income. Specifically, non-SSI enrollees are those who qualify for Medicaid based on being eligible for TANF, being a SOBRA child, or being an unemployed parent. *No eligibility data variable available in the DY8 survey dataset.

Table A19: Enrollee Eligibility Category, Reform vs. Non-Reform, DY6 – DY7*

Eligibility Category

DY6 DY7

Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p = 0.6879) Reform vs. Non-Reform

(p = <.0001) Sample Size 4,532 8,564 3,209 8,042

SSI 17.5% 17.9% 11.4% 16.9%

Non-SSI 82.5% 82.1% 88.6% 83.1%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. SSI enrollees are those who qualify for Medicaid based on whether they receive Supplemental Security Income. Non-SSI enrollees are those who do not qualify for Medicaid based on whether they receive Supplemental Security Income. Specifically, non-SSI enrollees are those who qualify for Medicaid based on being eligible for TANF, being a SOBRA child, or being an unemployed parent. *No eligibility data variable available in the DY8 survey dataset.

179

Appendix B: Description of HEDIS Measures

Table B1: Description of HEDIS Measures (Continued) Measure Description

Adolescent Well Care (AWC) The percentage of enrolled members 12 – 21 years of age who had at least one comprehensive well-care visit with a PCP or OB/GYN practitioner during the measurement year

Adults’ Access to Preventive/Ambulatory Health Services – Ages 20 – 44 (AAP20)

The percentage of members 20 – 44 years of age who had an ambulatory or preventive care visit

Adults’ Access to Preventive/Ambulatory Health Services – Ages 45 – 64 (AAP45)

The percentage of members 45 – 64 years of age who had an ambulatory or preventive care visit

Adults’ Access to Preventive/Ambulatory Health Services – Ages 65+ (AAP65):

The percentage of members 65 years and older who had an ambulatory or preventive care visit

Adults’ Access to Preventive/Ambulatory Health Services – Total (AAP Total)

The percentage of members 20 years and older who had an ambulatory or preventive care visit

Antidepressant Medication Management – Effective Acute Phase Treatment (AMM Acute)

The percentage of members 18 years of age and older who were diagnosed with a new episode of major depression and remained on antidepressant medication for at least 84 days

Antidepressant Medication Management – Effective Continuation Phase Treatment (AMM Continuation)

The percentage of members 18 years of age and older who were diagnosed with a new episode of major depression and remained on antidepressant medication for at least 180 days

Annual Dental Visits – Total (ADV) The percentage of members age 2 – 21 who had at least one dental visit during the measurement year

Cervical Cancer Screening (CCS) One or more Pap tests during the measurement year or the two years prior to them measurement year for women between the ages of 24 and 64 years

Comprehensive Diabetes Testing – HbA1C Testing (CDC HbA1C Testing)

HbA1C test was performed during the measurement year

Comprehensive Diabetes – HbA1C Poor Control (INVERSE) (CDC Poor)

The most recent HbA1C level is >9.0% or is missing or was not done during the measurement year

Comprehensive Diabetes – HbA1C Good Control (CDC Good)

The most recent HbA1C level is <7.0%

Comprehensive Diabetes – Eye Exam (CDC Eye)

A retinal or dilated eye exam by an eye care professional was done during the measurement year or a negative retinal exam was done by an eye professional in the year prior to the measurement year

Comprehensive Diabetes – LDL Screening (CDC LDLS)

An LDL-C test was performed during the measurement year

Comprehensive Diabetes – LDL-C Control (CDC LDLC)

The most recent LCL-C level performed during the measurement year is <100 mL

Comprehensive Diabetes – Nephropathy (CDC Neph)

A urine microalbumin test was done during the measurement year or there is evidence of nephropathy during the measurement year

Controlling Blood Pressure – Total (CBP) The number of members with hypertension whose most recent BP is <140/90

Follow-Up after Hospitalization for Mental Illness – 7 Day (FHM7)

The percentage of discharges for members 6 years and older who were hospitalized for a mental health disorder and who had an outpatient visit, an intensive outpatient encounter, or a partial hospitalization with a mental health practitioner within 7 days

Follow-Up after Hospitalization for Mental Illness – 30 Day (FHM 30)

The percentage of discharges for members 6 years and older who were hospitalized for a mental health disorder and who had an outpatient visit, an intensive outpatient encounter, or a partial hospitalization with a mental health practitioner within 30 days

180

Table B1: Description of HEDIS Measures (Continued) Measure Description

Prenatal Care (PPC-Pre) The percentage of deliveries that received a prenatal care visit as a member of the plan in the first trimester OR within 42 days of enrollment in the plan

Postpartum Care (PPC-Post) The percentage of deliveries that had a postpartum visit on or between 21 and 56 days after delivery

Well-Child 0 Visits (INVERSE) (W15 0) The percentage of children who turned 15 months old during the measurement year who had zero well-child visits with a PCP

Well-Child 6+ Visits (W15 6) The percentage of children who turned 15 months old during the measurement year who had six or more well-child visits with a PCP

Well-Child Visits in the Third, Fourth, Fifth, & Sixth Years of Life (W34)

The percentage of members 3–6 years of age who received one or more well-child visits with a PCP during the measurement year

181

Appendix C: Claims, Eligibility, and Encounter Data: Fiscal Analyses Inclusion and Exclusion Criteria

Eligibility Files Table C1: Eligibility Field: County

County: Only include records where county equals 6 Reform

16 Reform

29 Non-Reform

48 Non-Reform

Table C2: Eligibility Field: PGMCD

PGMCD: Only include recipients that are SSI (MEG#1) or TANF (MEG#2) If PGMCD equals one of these codes then recipient is SSI: MS, MT A, MT C, MT D, MT S, MT W, MX

If PGMCD equals one of these codes then recipient is TANF:

MA I, MA R, MA U, ME C, ME I, ME T, MM C, MM I, MN, MO A, MO D, MO P, MO S, MO T, MO U, MO Y, MP C, MP N, MP U

Table C3: Eligibility Field: Medicare

Medicare: Only include recipients that are not dual eligible

If Medicare = 0 then include. If Medicare does not = 0 then exclude.

Inpatient Fi les Table C4: Inpatient Field: CDE_PROV_TYPE_PRIM

Inpatient: Exclude inpatient SIPP Services If CDE_PROV_TYPE_PRIM = 16 exclude the observation. If CDE_PROV_TYPE_PRIM does not = 16 do not exclude the

observation.

Allpersonmonth Files Table C5: Allpersonmonth Field

Allpersonmonth: Exclude months where recipient was eligible for sipp, cms, aidswaiver, homesafenet, behavioral overlay, or retroactive:

Exclude the observation from calculation of expenditures if:

retro=’Y’, sipp=1, cms=1, aids=1, aids= 2, cwf= 1, bhos=1

Exclude the observation from calculation of expenditures if:

a

PLAN_TYPE = X and (County = 6 or 16)

aThis exclusion criteria results in removal of expenditure observations if the plan type code indicates the enrollee is in MediPass

as well as is in a Reform County. The same exclusion criterion was used for the utilization analysis as well.

182

Appendix D: Expanded Benefits Detailed Chart

Table D1: Expanded Benefits Detailed Chart (Continued)

Expanded Benefits

Pla

n T

ype

Co

un

ty

Cat

ego

ry

Ye

ar

Ove

r th

e C

ou

nte

r

Ph

arm

acy

Cir

cum

cisi

on

Ad

ult

De

nta

l

Ad

ult

Vis

ion

Ad

ult

He

arin

g

Ad

ult

Nu

trit

ion

The

rap

y

Acu

pu

nct

ure

He

alth

&

We

llne

ss B

en

efi

t

Re

spit

e

Car

e/H

om

e

He

alth

Se

rvic

es

Me

als

on

Wh

ee

ls

Frai

l & E

lde

r

Mat

ern

ity

Ad

ult

Ho

spit

al

Inp

atie

nt

Ad

ult

Ou

tpat

ien

t

Access Health Solutions

PSN

Baker/ Clay/ Nassau SSI 2007

TANF

Access Health Solutions

PSN

Broward

SSI 2007

TANF

Access Health Solutions

PSN

Duval SSI 2007

TANF

Amerigro

up

Communi

ty Care

HMO

Broward

SSI a 2007 x x x x x x x

TANF b x x x x

Better

Health

PSN

Broward

SSI c 2011 -2013eee

x x x X e e e x

TANFd x x x X e e e x

Buena

Vista HMO

Broward

SSI e 2007 x x x

TANFf x x

CareFlori

da

HMO

Broward SSIg 2011-2013

x x x

TANFh x x x

Children’s Medical Services

PSN

Broward SSI 2007 This plan is limited to children with serious medical, developmental, behavioral or emotional conditions. Their siblings may also enroll. TANF

Children’s Medical Services

PSN Duval SSI 2011 -2013

This plan is limited to children with serious medical, developmental, behavioral or emotional conditions. Their siblings may also enroll. TANF

Children’s Medical Services

PSN

Broward SSI 2011 -2013

This plan is limited to children with serious medical, developmental, behavioral or emotional conditions. Their siblings may also enroll. TANF

Children’s Medical Services

PSN

Duval SSI 2007 This plan is limited to children with serious medical, developmental, behavioral or emotional conditions. Their siblings may also enroll. TANF

Clear HMO Broward SSI 2013 x x x x x

183

Table D1: Expanded Benefits Detailed Chart (Continued) Expanded Benefits

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Health Alliance

TANF x x x x x

First

Coast

Advantag

e

PSN

Baker/ Clay/ Nassau/ Duval

SSIi 2011 -2013

x

TANFj x

First

Coast

Advantag

e

PSN Duval SSIk 2007 x x x

TANFl x

Florida NetPASS

PSN

Broward SSI 2007

TANF

Freedom

Health

HMO

Broward SSIm 2011 -2013

x x

TANFn x x

HealthEa

se

HMO

Broward SSIo 2007 x x x x x

TANFp x x

HealthEa

se HMO

Duval SSIq 2007 x x x x x

TANFr x

Humana

Family

HMO

Broward SSIs 2007 x

TANFt x

Humana Family

HMO

Broward SSI 2011 - 2013

x *

TANF x *

Magellan Complete Care

HMO Broward SSI 2013

TANF

Medica

Health

Plans of

Florida,

Inc.

HMO

Broward SSIu 2011 - 2013

x

TANFv x

Molina

Healthcar

e of

HMO

Broward SSIw 2011 - 2013

x x x x*

TANFx x x x x*

184

Table D1: Expanded Benefits Detailed Chart (Continued) Expanded Benefits

Pla

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Florida,

Inc.

Pediatric Associates Health Plan

PSN

Broward SSI 2007

TANF

Positive

Health

Care

HMO Broward SSIy 2011 - 2013

x x x x

TANFz x x x x

Preferred

Medical

Plan, Inc.

HMO

Broward SSIaa 2007 x x

TANFbb x x

Simply Healthcare Plans, Inc.

HMO Broward SSIcc 2012

TANFdd

South

Florida

Communi

ty Care

Network

PSN Broward SSIee 2007 x x

TANFff x x

South

Florida

Communi

ty Care

Network

PSN

Broward SSIgg 2011 - 2013

x x* x

TANFhh x x* x

Staywell

HMO

Broward SSIii 2007 x x x x x

TANFjj x x x

Staywell

HMO

Duval SSIkk 2007 x x x x x

TANFll x x x

Staywell Health Plan of Florida

HMO Baker/ Broward/ Clay/ Nassau/ Duval

SSImm 2013 x x

TANFnn x x

Sunshine HMO Broward SSIoo 2011 - x x x

185

Table D1: Expanded Benefits Detailed Chart (Continued) Expanded Benefits

Pla

n T

ype

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un

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State

Health

Plan

TANFpp 2013 x x x

Sunshine State Health Plan

HMO

Clay/ Duval SSI 2011 - 2013

TANF

Total

Health

Choice

HMO

Broward SSIqq 2007 x x x x

TANFrr x x x x

United

Health

Care

HMO

Baker/ Clay/ Nassau SSIss 2007 x x x

TANFtt x x x

United

Health

Care

HMO

Baker/ Clay/ Nassau/ Duval SSIuu 2013 x

TANFvv x x

United

Health

Care

HMO

Broward SSIww 2007 x x x x

TANFxx x x x

United

Health

Care

HMO

Duval SSIyy 2007 x x x

TANFzz x x x

Universal

Health

Care

HMO

Broward SSIaaa 2007 x

TANFbbb x

Universal

Health

Care

HMO

Duval SSIccc 2011 - 2012

x

TANFddd x

Universal

Health

Care

HMO

Broward SSIeee 2011 - 2012

x

TANFfff x

186

Table D1: Expanded Benefits Detailed Chart (Continued) Expanded Benefits

Pla

n T

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un

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Universal Health Care

HMO

Duval SSIggg 2007 x

TANFhhh x

Vista

Healthpla

n of

South

Florida

HMO

Broward SSIiii 2007 x x x

TANFjjj x x

Notes. aAmerigroup Community Care – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $25 per house-hold per month; Circumcision: Newborn; Adult Dental: Exams, x-rays, cleanings, fillings, extractions; Adult Vision: Up to $125 for eyeglass upgrade; Adult Hearing: Up to $500 for hearing aid upgrade; Acupuncture: Up to 12 visits per year; Respite Care: Visits of 16 hours/month & 32 hours/yr limit bAmerigroup Community Care – Children & Families (Broward 2007): OTC Pharmacy: $25 per house-hold per month; Circumcision: Newborn; Adult Dental: Exams, x-rays, cleanings, fillings, extractions; Adult Vision: Up to $125 for eyeglass upgrade cBetter Health – Aged and/or Disabled (Broward 2012): OTC Pharmacy: $25 per house-hold per month; Circumcision: Up to 12 weeks; Adult Dental: 1 cleaning per 6 months; Adult Nutrition Therapy: 12 visits per year d Better Health – Children & Families (Broward 2012): OTC Pharmacy: $25 per household per month; Circumcision: Up to 12 weeks; Adult Dental: 1 cleaning per 6 months; Adult Nutrition Therapy: 12 visits per year e Buena Vista – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Up to 6 weeks; Adult Dental: Cleanings (standard & deep), simple fillings, extractions f Buena Vista – Children & Families (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Up to 6 weeks g CareFlorida – Aged and/or Disabled (Broward 2012): OTC Pharmacy: $25 per household per month, credit by mail; Adult Dental: 1 oral exam & 1 cleaning every 6 months, 1 fluoride treatment per year & 1 dental x-ray per year: Adult Vision: Unlimited eye exams & eyeglasses if medically necessary (eyeglasses provided from preselected frames) h CareFlorida – Children & Families (Broward 2012): OTC Pharmacy: $25 per household per month; Adult Dental: 1 oral exam & 1 cleaning every 6 months, 1 fluoride treatment per year & 1 dental x-ray per year; Adult Vision: Unlimited eye exams & eyeglasses if medically necessary (eyeglasses provided from preselected frames) iFirst Coast Advantage – Aged and/or Disabled (Baker/Clay/Nassau/ Duval 2012): Circumcision: For newborns enrolled during infant’s initial hospitalization at Shands Jacksonville only jFirst Coast Advantage – Children & Families (Baker/Clay/Nassau/ Duval 2012): Circumcision: For newborns enrolled during infant’s initial hospitalization at Shands Jacksonville only kFirst Coast Advantage – Aged and/or Disabled (Duval 2007): Circumcision: For newborns enrolled during infant’s initial hospitalization at Shands Jacksonville only; Adult Hospital Inpatient: Extra 20 inpatient days at Shands Jacksonville only (max 65 days combined); Adult Outpatient: Extra $3,500 per year for outpatient services at Shands Jacksonville only (max $5,000 per year combined) lFirst Coast Advantage – Children & Families (Duval 2007): Circumcision: For newborns enrolled during infant’s initial hospitalization at Shands Jacksonville only mFreedom Health – Aged and/or Disabled (Broward 2012): OTC Pharmacy: $25 per household per month; Adult Dental: Exams, cleanings, simple fillings, x-rays nFreedom Health – Children & Families (Broward 2012): OTC Pharmacy: $25 per household per month; Adult Dental: Exams, cleanings, simple fillings, x-rays oHealthEase – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Boys up to 1 year; Adult Dental: Exams, x-rays, cleanings (standard & deep), fillings, crowns, fluoride; Respite Care: 1 per month; Meals on Wheels: Home delivery of up to 10 meals post discharge pHealthEase – Children & Families (Broward 2007): OTC Pharmacy: $25 per household per month; Adult Dental: Exams, x-rays, 2 cleanings per year qHealthEase – Aged and/or Disabled (Duval 2007): OTC Pharmacy: $25 per household per month; Circumcision: Boys up to 1 year; Adult Dental: Exams, x-rays, cleanings (standard & deep), fillings, crowns, fluoride; Respite Care: 1 per month; Meals on Wheels: Home delivery of up to 10 meals post discharge rHealthEase – Children & Families (Duval 2007): OTC Pharmacy: $25 per household per month sHumana Family – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $20 per household per month tHumana Family – Children & Families (Broward 2007): OTC Pharmacy: $20 per household per month uMedica Health Plans of Florida, Inc. – Aged and/or Disabled (Broward 2012): OTC Pharmacy: $25 per household per month vMedica Health Plans of Florida, Inc. – Children & Families (Broward 2012): OTC Pharmacy: $25 per household per month wMolina Healthcare of Florida, Inc. – Aged and/or Disabled (Broward 2012): OTC Pharmacy: $25 per household per month; Adult Dental: Annual exams, cleanings, x-rays once every 3 months, fluoride treatments every 6 months; Adult Vision: Unlimited exams & eyeglasses, if medically necessary

187

xMolina Healthcare of Florida, Inc. – Children & Families (Broward 2012): OTC Pharmacy: $25 per household per month; Adult Dental: Annual exams, cleanings, x-rays once every 3 months, fluoride treatments every 6 months; Adult Vision: Unlimited exams & eyeglasses, if medically necessary yPositive Healthcare Florida – Aged and/or Disabled (Broward 2012): OTC Pharmacy: $25 per household per month; Adult Dental: $1,000/year towards exams, cleanings, certain fillings, & x-rays; Health & Wellness Benefit: Gym membership or nutritional products at $250/yr (in five $50 increments); Respite Care: Limited to 3 hours/week at a specific location zPositive Healthcare Florida – Children & Families (Broward 2012): OTC Pharmacy: $25 per household per month; Adult Dental: $1,000/year towards exams, cleanings, certain fillings, & x-rays; Health & Wellness Benefit: Gym membership or nutritional products at $250/yr (in five $50 increments); Respite Care: Limited to 3 hours/week at a specific location aaPreferred Medical Plan, Inc. – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $10 per household per month; Adult Dental: Exams, x-rays, fluoride, fillings, plus additional discounts bbPreferred Medical Plan, Inc. – Children & Families (Broward 2007): OTC Pharmacy: $10 per household per month; Adult Dental: Exams, x-rays, fluoride, fillings, plus additional discounts ccSouth Florida Community Care Network – Aged and/or Disabled (Broward 2007): Adult Dental: Cleaning, exams, x-rays; Maternity: Home delivered meals for families of newborns; 2 meals delivered for up to 6 people dd South Florida Community Care Network – Children & Families (Broward 2007): Adult Dental: Cleaning, exams, x-rays; Maternity: Home delivered meals for families of newborns; 2 meals delivered for up to 6 people ee South Florida Community Care Network – Aged and/or Disabled (Broward 2012): Adult Dental: 1 cleaning per year ff South Florida Community Care Network – Children & Families (Broward 2012): Adult Dental: 1 cleaning per year; Maternity: Home delivered meals for families of newborns; 2 meals delivered for up to 4 people gg Staywell – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Boys up to 1 year; Adult Dental: Exams, x-rays, cleanings, (standard & deep), filings, crowns, fluoride; Respite Care: 1 per month; Meals on Wheels: Home delivery of up to 10 meals post discharge hhStaywell – Children & Families (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Boys up to 1 year; Adult Dental: Exams, x-rays, 2 cleanings per year ii Staywell – Aged and/or Disabled (Duval 2007): OTC Pharmacy: $25 per household per month; Circumcision: Boys up to 1 year; Adult Dental: Exams, x-rays, cleanings, (standard & deep), filings, crowns, fluoride; Respite Care: 1 per month; Meals on Wheels: Home delivery of up to 10 meals post discharge jj Staywell – Children & Families (Duval 2007): OTC Pharmacy: $25 per household per month; Circumcision: Boys up to 1 year; Adult Dental: Exams, x-rays, 2 cleanings per year kk Sunshine State Health Plan – Aged and/or Disabled (Broward 2012): OTC Pharmacy: $25 per household per month; Adult Dental: 2 preventatives services (including cleanings & fluoride) & 1 routine x-ray per year; Adult Vision: 1 visit per day & 2 eyeglasses per year ll Sunshine State Health Plan – Children & Families (Broward 2012): OTC Pharmacy: $25 per household per month; Adult Dental: 2 preventatives services (including cleanings & fluoride) & 1 routine x-ray per year; Adult Vision: 1 visit per day & 2 eyeglasses per year kk Total Health Choice – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Up to 1 year; Adult Dental: Cleanings, fillings, extractions; Adult Nutrition Therapy: 15 visits per year ll Total Health Choice – Children & Families (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Up to 1 year; Adult Dental: Cleanings, fillings, extractions; Adult Nutrition Therapy: 15 visits per year mm United Health Care – Aged and/or Disabled (Baker/Clay/ Nassau 2007): OTC Pharmacy: $25 per household per month; Circumcision: Routine for babies up to 12 weeks; Adult Dental: Exams, x-rays, cleanings, fillings, extractions nn United Health Care – Children & Families (Baker/Clay/ Nassau 2007): OTC Pharmacy: $25 per household per month; Circumcision: Routine for babies up to 12 weeks; Adult Dental: Exams, x-rays, cleanings, fillings, extractions oo United Health Care – Aged and/or Disabled (Baker/Clay/ Nassau/ Duval 2012): OTC Pharmacy: $25 per household per month pp United Health Care – Children & Families (Baker/Clay/ Nassau/ Duval 2012): OTC Pharmacy: $25 per household per month; Circumcision: Male children under the age of 6 qq United Health Care – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $10 per household per month; Circumcision: Routine for babies up to 12 weeks; Adult Dental: Exams, cleanings, fillings, extractions, x-rays; Frail & Elder: Evercare at Home program offering additional services to help someone stay at home and avoid going to a nursing home rr United Health Care – Children & Families (Broward 2007): OTC Pharmacy: $10 per household per month; Circumcision: Routine for babies up to 12 weeks; Adult Dental: Exams, cleanings, fillings, extractions, x-rays ss United Health Care – Aged and/or Disabled (Duval 2007): OTC Pharmacy: $25 per household per month; Circumcision: Routine for babies up to 12 weeks; Adult Dental: Exams, cleanings, fillings, extractions, x-rays tt United Health Care – Children & Families (Duval 2007): OTC Pharmacy: $25 per household per month; Circumcision: Routine for babies up to 12 weeks; Adult Dental: Exams, cleanings, fillings, extractions, x-rays uu Universal Health Care – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $25 per household per month vv Universal Health Care – Children & Families (Broward 2007): OTC Pharmacy: $25 per household per month ww Universal Health Care – Aged and/or Disabled (Duval 2012): OTC Pharmacy: $25 per household per month xx Universal Health Care – Children & Families (Duval 2012): OTC Pharmacy: $25 per household per month yy Universal Health Care – Aged and/or Disabled (Broward 2012): OTC Pharmacy: $25 per household per month zz Universal Health Care – Children & Families (Broward 2012): OTC Pharmacy: $25 per household per month aaa Universal Health Care – Aged and/or Disabled (Duval 2007): OTC Pharmacy: $25 per household per month bbb Universal health Care – Children & Families (Duval 2007): OTC Pharmacy: $25 per household per month ccc Vista Healthplan of South Florida – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Up to 6 weeks; Adult Dental: Cleanings (standard & deep), simple fillings, extractions ddd Vista Healthplan of South Florida – Children & Families (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Up to 6 weeks eee Universal Health Care – Aged and/or Disabled (Broward 2012): OTC Pharmacy: $25 per household per month fff Universal Health Care – Children & Families (Broward 2012): OTC Pharmacy: $25 per household per month ggg Universal Health Care – Aged and/or Disabled (Duval 2007): OTC Pharmacy: $25 per household per month hhh Universal health Care – Children & Families (Duval 2007): OTC Pharmacy: $25 per household per month iii Vista Healthplan of South Florida – Aged and/or Disabled (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Up to 6 weeks; Adult Dental: Cleanings (standard & deep), simple fillings, extractions jjj Vista Healthplan of South Florida – Children & Families (Broward 2007): OTC Pharmacy: $25 per household per month; Circumcision: Up to 6 weeks

188

*This benefit was new to 2013 **This benefit was not available in 2011

Adapted from health plan comparison charts from October 2007, April 2011, April 2012, and September 2013.

189

Appendix E: Detailed CAHPS and HEDIS Tables and Figures by Domain

Domain i.a: Are services accessible to enrollees? Have there been changes in the accessibility of services to enrollees over the course of the demonstration ? Has the demonstration resulted in more appropriate use of services by enrollees?

CAHPS Survey Data Counties Over Time

Table E1: How Much of a Problem to See Specialist, Reform, DY1 – DY8

In the last 6 months, how much of a problem, if any/how often was it easy to get appointments for you (your child) with specialists?

How Much of a Problem to See Specialist, Reform

DY1a

Broward and Duval Counties (p = 0.0467)

Sample Size 2,123

A big problem 24.4%

A small problem 20.9%

Not a problem 54.7%

Ease of Getting Specialist Appointments, Reform

DY2b DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p = 0.1017) Sample Size 1,967 1,673 1,524 1,353 899 1,079

Never 14.3% 13.6% 15.9% 10.3% 12.1% 6.6%

Sometimes 21.6% 21.7% 21.2% 22.8% 21.9% 18.5%

Usually 18.3% 15.8% 17.3% 18.5% 16.2% 19.1%

Always 45.9% 48.9% 45.6% 48.4% 49.9% 55.8%

Rural Counties (p = 0.665) Sample Size 252 170 265 106 70 79

Never 12.1% 18.8% 14.7% 20.2% 22.1% 0.3%

Sometimes 19.7% 18.3% 20.2% 10.1% 21.8% 18.8%

Usually 15.9% 17.3% 17.6% 27.4% 25.0% 18.4%

Always 52.3% 45.6% 47.5% 42.2% 31.1% 62.4%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork

started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

190

Notes. These findings were statistically significant across the six demonstration years, DY2 to DY8. CAHPS Survey fieldwork was not conducted for DY5.

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5.

46% 49%

46% 48% 50%

56%

0%

20%

40%

60%

80%

100%

DY2 DY3 DY4 DY6 DY7 DY8

Always

52%

46% 48% 42%

31%

62%

0%

20%

40%

60%

80%

100%

DY2 DY3 DY4 DY6 DY7 DY8

Always

Figure E1: Respondents Who Reported "Always" with Ease of Getting Specialist Appointments, Broward and Duval Counties, Reform, DY1 – DY8

Figure E2: Respondents Who Reported "Always" with Ease of Getting Specialist Appointments, Rural Counties, Reform, DY1 – DY8

191

Table E2: How Much of a Problem if any to get Care, Tests, or Treatment Thought Needed, Reform, DY1 – DY8 In the last 6 months, how much of a problem, if any/how often was it easy to get the care, tests, or treatment you thought you

(your child) needed through your health plan?

How Much of a Problem to get Care, Tests, or Treatment Thought Needed, Reform

DY1a

Broward and Duval Counties (p = 0.0014)

Sample Size 2,328 A big problem 12.1% A small problem 18.2% Not a problem 69.6% Ease of Getting Care, Tests, or Treatment Thought Needed, Reform

DY2b DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p < 0.0001) Sample Size 2,911 2,468 2,056 1,723 1,147 2,407

Never 8.8% 9.6% 10.4% 8.8% 7.2% 2.1%

Sometimes 17.4% 21.0% 20.9% 16.3% 17.8% 14.6%

Usually 19.9% 17.0% 19.7% 17.6% 14.8% 15.6%

Always 54.0% 52.4% 49.0% 57.3% 60.2% 67.7%

Rural Counties (p = 0.7894) Sample Size 453 361 431 148 99 217

Never 6.5% 9.1% 8.5% 10.5% 11.8% 0.5%

Sometimes 17.2% 14.9% 16.5% 15.2% 17.4% 6.4%

Usually 20.1% 21.0% 22.4% 27.3% 17.3% 22.1%

Always 56.2% 55.0% 52.6% 47.0% 53.6% 71.1%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork

started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

192

Notes. These findings were statistically significant across the six demonstration years, DY2 to DY8. CAHPS Survey fieldwork was not conducted for DY5.

Note. CAHPS Survey fieldwork was not conducted for DY5.

54% 52% 49%

57% 60%

68%

0%

20%

40%

60%

80%

100%

DY2 DY3 DY4 DY6 DY7 DY8

Always

56% 55% 53% 47%

54%

71%

0%

20%

40%

60%

80%

100%

DY2 DY3 DY4 DY6 DY7 DY8

Always

Figure E3: Respondents who reported "Always" with Ease of Getting Care, Tests or Treatment Thought Needed, Broward and Duval Counties, Reform, DY1 – DY8

Figure E4: Respondents who reported "Always" with Ease of Getting Care, Tests or Treatment Thought Needed, Rural Counties, Reform, DY1 – DY8

193

Table E3: Enrollees Who Reported Whether or Not They Had a Personal Doctor, Reform, DY1 – DY8 A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt.

Do you (your child) have a personal doctor?

DY1 DY2 DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p < .0001 ) Sample Size 5,699 6,160 6,103 5,878 4,102 2,939 3,375

Yes 79.2% 88.2% 87.4% 87.3% 90.1% 91.6% 91.0%

No 20.8% 11.8% 12.6% 12.7% 9.9% 8.4% 9.0%

Rural Counties (p = 0.4946) Sample Size 989 747 1,094 371 247 284

Yes 89.8% 89.9% 89.0% 93.8% 91.5% 94.8%

No 10.2% 10.1% 11.0% 6.2% 8.5% 5.2%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork

started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

194

Notes. These findings were statistically significant across the seven demonstration years. CAHPS Survey fieldwork was not conducted for DY5.

Note. CAHPS Survey fieldwork was not conducted for DY5.

90% 90% 89% 94% 92%

95%

10% 10% 11% 6% 8%

5%

0%

20%

40%

60%

80%

100%

DY2 DY3 DY4 DY6 DY7 DY8

Yes No

Figure E5: Enrollees Who Reported Whether or Not They Had a Personal Doctor, Broward and Duval Counties, Reform, DY1 – DY8

Figure E6: Enrollees Who Reported Whether or Not They Had a Personal Doctor, Rural Counties, Reform, DY1 – DY8

79%

88% 87% 87% 90% 92% 91%

21%

12% 13% 13% 10% 8% 9%

0%

20%

40%

60%

80%

100%

DY1 DY2 DY3 DY4 DY6 DY7 DY8

Yes No

195

Table E4: Number of Times at Doctor's Office or Clinic for Health Care Last 6 Months, Reform, DY1 – DY8 In the last 6 months, not counting the times you went to an emergency room, how many times did you (your child) go to a

doctor’s office or clinic to get health care for yourself (himself or herself)?

DY1 DY2 DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p < .0001) Sample Size 5,624 5,912 5,838 5,651 3,919 2,829 3,241

0 times 20.5% 19.5% 20.0% 22.3% 18.6% 19.0% 20.9%

1–3 times 60.5% 55.2% 54.1% 53.9% 54.9% 59.4% 60.1%

4 or more times 19.0% 25.3% 26.0% 23.8% 26.5% 21.6% 19.1%

Rural Counties (p = 0.4921) Sample Size 957 716 1,066 357 242 271

0 times 17.2% 20.4% 20.5% 13.3% 12.1% 18.5%

1–3 times 50.3% 49.8% 48.6% 51.6% 59.0% 48.0%

4 or more times 32.5% 29.8% 30.9% 35.0% 28.9% 33.4%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork

started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

196

Notes. These findings were statistically significant across the seven demonstration years. CAHPS Survey fieldwork was not conducted for DY5.

Note. CAHPS Survey fieldwork was not conducted for DY5.

21% 20% 20% 22% 19% 19% 21%

61% 55% 54% 54% 55%

59% 60%

19% 25% 26% 24%

27% 22% 19%

0%

20%

40%

60%

80%

100%

DY1 DY2 DY3 DY4 DY6 DY7 DY8

0 times 1 - 3 times 4 or more times

17% 20% 21%

13% 12% 19%

50% 50% 49% 52%

59%

48%

33% 30% 31%

35% 29%

33%

0%

20%

40%

60%

80%

100%

DY2 DY3 DY4 DY6 DY7 DY8

0 times 1 - 3 times 4 or more times

Figure E7: Number of Times at Doctor's Office or Clinic for Health Care Last 6 Months, Broward and Duval Counties, Reform, DY1 – DY8

Figure E8: Number of Times at Doctor's Office or Clinic for Health Care Last 6 Months, Rural Counties, Reform, DY1 – DY8

197

Reform vs. Non-Reform Counties

Table E5: Ease of Getting Specialist Appointments, Reform vs. Non-Reform, DY6 – DY8

In the last 6 months, how often was it easy to get appointments with specialists?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p = 0.4467) Reform vs. Non-Reform

(p = 0.4041) Reform vs. Non-Reform

(p = 0.4965) Sample Size 1,465 2,397 969 2,255 1,158 1,659

Never 11.1% 10.5% 13.2% 11.8% 6.2% 6.3%

Sometimes 21.7% 23.4% 21.9% 18.0% 18.5% 19.0%

Usually 19.2% 16.3% 17.1% 17.8% 19.0% 15.7%

Always 47.9% 49.8% 47.9% 52.4% 56.3% 59.0%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

48% 48%

56%

50% 52%

59%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

Reform Non-Reform

Figure E9: Ease of Getting Specialist Appointments, Reform vs. Non-Reform, DY6 – DY8

198

Table E6: Ease of Getting Care, Tests, or Treatment Thought Needed, Reform vs. Non-Reform, DY6 – DY8

In the last 6 months, how often was it easy to get the care, tests, or treatment you thought you (your child) needed through your health plan?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p = 0.306) Reform vs. Non-Reform

(p = 0.0607) Reform vs. Non-Reform

(p = 0.5429) Sample Size 1,876 3,273 1,248 3,082 2,624 3,877

Never 8.9% 7.3% 7.6% 8.9% 2.0% 2.9%

Sometimes 16.3% 18.9% 17.7% 16.8% 14.0% 13.9%

Usually 18.4% 18.5% 14.9% 20.4% 16.1% 15.9%

Always 56.4% 55.3% 59.8% 53.9% 68.0% 67.3%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Note.*Trend was statistically significant at <0.05.

56% 60%

68%

55% 54%

67%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

Reform Non-Reform

Figure E10: Respondents who reported "Always" with Ease of Getting Care, Tests, or Treatment Thought Needed, Reform vs. Non-Reform, DY6 – DY8

199

Table E7: Enrollees Who Reported Whether or Not They Had a Personal Doctor, Reform vs. Non-Reform, DY6 – DY8

A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt. Do you (your child) have a personal doctor?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p= 0.0016) Reform vs. Non-Reform

(p = <0.0001) Reform vs. Non-Reform

(p = 0.0007) Sample Size 4,489 8,473 3,192 7,984 3,659 6,143

Yes 90.4% 87.4% 91.5% 87.0% 91.3% 87.9%

No 9.6% 12.6% 8.5% 13.0% 8.7% 12.1%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Note. *Trends were statistically significant at <0.05.

90% 92% 91%

87% 87% 88%

80%

100%

DY6* DY7* DY8*

Reform Non-Reform

Figure E11: Enrollees Who Reported They Had a Personal Doctor, Reform vs. Non-Reform, DY6 – DY8

200

Table E8: Number of Times at Doctor’s Office or Clinic for Health Care Last 6 Months, Reform vs. Non-Reform, DY6 – DY8 In the last 6 months, NOT counting the times you went to an emergency room, how many times did you go to a doctor's office or

clinic to get health care for yourself? DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p=0.1351)

Reform vs. Non-Reform (p=0.1441)

Reform vs. Non-Reform (p = 0.4735)

Sample Size 4,291 8,058 3,077 7,738 3,512 5,892

0 times 18.1% 20.3% 18.6% 20.9% 20.7% 20.1%

1–3 times 54.7% 54.6% 59.3% 55.8% 59.2% 58.2%

4 or more times 27.1% 25.0% 22.1% 23.3% 20.1% 21.7%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

18% 20% 19%

21% 21% 20%

55% 55% 59%

56% 59%

58%

27% 25% 22% 23%

20% 22%

0%

20%

40%

60%

80%

100%

Reform (DY6) Non-Reform(DY6)

Reform (DY7) Non-Reform(DY7)

Reform (DY8) Non-Reform(DY8)

0 times 1-3 times 4 or more times

Figure E12: Number of Times at Doctor's Office or Clinic for Health Care Last 6 Months, Reform vs. Non-Reform, DY6 – DY8

201

HEDIS Data Table E9: Annual Dentist Visits (ADV) – Total, 2008 – 2013

Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%) All Plans 14.7% 27.6% 29.4% 29.7% 29.2% 38.8% 3.6%

Reform Plans 15.2% 28.5% 33.4% 34.0% 35.3% 40.4% 4.2%

Non Reform Plans 11.3% 17.8% 25.5% 16.1% 17.6% 31.6% 2.6%

Difference Average Annual Difference (%) Reform-Non Reform 3.9% 10.7% 7.9% 17.9% 17.7% 8.8% 11.1%

Notes. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E13: Annual Dental Visits (ADV) – Total, 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

202

Table E10: Adults’ Access to Preventive/Ambulatory Health Services – Ages 20 – 44 (AAP20), 2009 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 69.7% 68.2% 68.6% 66.8% 66.8% -0.01%

Reform Plans 71.8% 71.2% 71.2% 69.8% 67.1% 1.1%

Non Reform Plans 69.1% 67.9% 68.1% 66.2% 66.3% 0.7%

Difference Average Annual Difference (%) Reform-Non Reform 2.7% 3.4% 3.1% 3.6% 0.9% 2.7%

Notes. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding. No data are available for 2008 for this HEDIS measure. Figure E14: Adults' Access to Preventive/Ambulatory Health Services – Ages 20 – 44 (AAP20), 2009 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

203

Table E11: Adults’ Access to Preventive/Ambulatory Health Services – Ages 45 – 64 (AAP45), 2009 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 82.8% 81.7% 82.4% 81.5% 82.2% -0.1%

Reform Plans 84.7% 84.9% 85.5% 84.9% 85.0% 0.1%

Non Reform Plans 82.2% 81.2% 81.5% 80.5% 81.5% -0.2%

Difference Average Annual Difference (%)

Reform-Non Reform 2.5% 3.7% 4.0% 4.4% 3.6% 3.6%

Notes. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding. No data are available for 2008 for this HEDIS measure.

Figure E15: Adults’ Access to Preventive/Ambulatory Health Services – Ages 45–64 (AAP45), 2009 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

204

Table E12: Adults’ Access to Preventive/Ambulatory Health Services – Ages 65+ (AAP65), 2009 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 76.1% 68.3% 72.5% 66.0% 71.1% -1.2%

Reform Plans 83.6% 83.7% 84.2% 74.0% 76.2% -2.5%

Non Reform Plans 74.7% 66.9% 69.9% 64.1% 69.8% -1.2%

Difference Average Annual Difference (%)

Reform-Non Reform 8.9% 16.8% 14.3% 9.9% 6.4% 11.3%

Notes. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding. No data are available for 2008 for this HEDIS measure.

Figure E16: Adults’ Access to Preventive/Ambulatory Health Services – Ages 65+ (AAP65), 2009 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

205

Table E13: Adults’ Access to Preventive/Ambulatory Health Services – Total (AAP Total), 2009 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 74.4% 72.1% 72.9% 70.9% 71.7% -0.7%

Reform Plans 77.2% 77.6% 77.1% 75.0% 74.7% -0.8%

Non Reform Plans 73.7% 71.5% 71.9% 70.0% 70.9% -0.7%

Difference Average Annual Difference (%)

Reform-Non Reform 3.6% 6.1% 5.1% 5.1% 3.8% 4.7%

Notes. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding. No data are available for 2008 for this HEDIS measure.

Figure E17: Adults’ Access to Preventive/Ambulatory Health Services – Total (AAP Total), 2009 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

206

Table E14: Adolescent Well Care (AWC), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 42.4% 46.1% 45.8% 48.6% 48.1% 49.8% 1.3%

Reform Plans 44.2% 46.5% 46.3% 46.2% 47.6% 48.5% 0.7%

Non Reform Plans 41.9% 46.0% 45.7% 49.2% 48.2% 50.1% 1.5%

Difference Average Annual Difference (%) Reform-Non Reform 2.3% 0.5% 0.6% -2.9% -0.6% -1.6% -0.3%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E18: Adolescent Well Care (AWC), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

207

Table E15: Cervical Cancer Screening (CCS), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 54.6% 53.5% 54.9% 55.2% 55.4% 56.8% 0.5%

Reform Plans 48.2% 52.2% 50.8% 53.2% 56.8% 58.2% 1.9%

Non Reform Plans 56.6% 53.8% 55.3% 55.6% 55.0% 56.5% 0.1%

Difference Average Annual Difference (%)

Reform-Non Reform -8.4% -1.6% -4.5% -2.5% 1.7% 1.7% -2.3%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E19: Cervical Cancer Screening (CCS), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

208

Table E16: Prenatal Care (PPC-Pre), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 70.7% 68.8% 70.2% 71.1% 72.9% 72.0% 0.6%

Reform Plans 66.6% 67.4% 75.2% 68.4% 72.1% 67.2% 0.3%

Non Reform Plans 71.7% 69.1% 69.6% 71.7% 73.1% 73.3% 0.6%

Difference Average Annual Difference (%)

Reform-Non Reform -5.1% -1.7% 5.6% -3.3% -1.0% -6.0% -1.9%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E20: Prenatal Care (PPC-Pre), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

209

Table E17: Postpartum Care (PPC-Post), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 57.4% 50.4% 52.6% 53.6% 52.0% 52.0% -0.6%

Reform Plans 53.0% 51.5% 52.1% 49.3% 52.9% 51.4% -0.2%

Non Reform Plans 58.5% 50.1% 52.7% 54.6% 51.8% 52.1% -0.7%

Difference Average Annual Difference (%)

Reform-Non Reform -5.5% 1.3% -0.6% -5.3% 1.1% -0.8% -1.6%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E21: Postpartum Care (PPC-Post), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

210

Table E18: Well-Child 0 Visits INVERSE (W15 0), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 3.2% 2.7% 4.2% 3.0% 2.7% 2.4% -0.2%

Reform Plans 4.9% 1.6% 6.0% 3.0% 2.1% 1.6% -0.5%

Non Reform Plans 2.8% 3.0% 4.2% 3.3% 3.2% 2.7% -0.0%

Difference Average Annual Difference (%)

Reform-Non Reform 2.1% -1.4% 1.7% -0.3% -1.1% -1.1% 0.0%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E22: Well-Child 0 Visits INVERSE (W15 0), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

211

Table E19: Well-Child 6+ Visits (W15 6), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 44.1% 50.6% 44.8% 49.2% 55.0% 56.1% 2.2%

Reform Plans 44.4% 49.3% 35.4% 46.5% 58.4% 55.6% 2.7%

Non Reform Plans 44.0% 51.0% 46.1% 51.2% 56.2% 56.3% 2.4%

Difference Average Annual Difference (%)

Reform-Non Reform 0.4% -1.7% -10.6% -4.7% 2.2% -0.7% -2.5%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E23: Well-Child 6+ Visits (W15 6), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

212

Table E20: Well-Child Visits in the Third, Fourth, Fifth, & Sixth Years of Life (W34), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 71.1% 73.2% 74.7% 74.8% 75.6% 73.8% 0.6%

Reform Plans 71.3% 75.7% 72.7% 75.0% 75.6% 75.6% 0.7%

Non Reform Plans 71.1% 72.5% 74.9% 74.8% 75.6% 73.2% 0.6%

Difference Average Annual Difference (%) Reform-Non Reform 0.2% 3.2% -2.2% 0.3% -0.1% 2.4% 0.6%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

Figure E24: Well-Child Visits in the Third, Fourth, Fifth, & Sixth Years of Life (W34), 2008 – 2013

y = 0.0059x + 0.7181

y = 0.0068x + 0.7194

y = 0.0057x + 0.7167

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

Figure X: Well-Child Visits in the Third, Fourth, Fifth, and Sixth Years of Life (W34), 2008-2012

Series1

Reform Plans

Non Reform Plans

Linear (Series1)

Linear (Reform Plans)

Linear (Non Reform Plans)

213

Domain i.b: Has the quality of care that enrollees receive improved during the demonstration? What have managed care plans done to improve quality of care?

CAHPS Survey Data Counties Over Time

Table E21: Health Care Satisfaction Rating (0 – 10), Reform, DY1 – DY8

Using any number from 0 – 10, where 0 is the worst and 10 is the best health care possible, what number would you use to rate all your health care in the last 6 months?

DY1a DY2

b DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p = .065) Sample Size 4,521 5,203 4,952 4,364 3,126 2,245 2,589

Level 1 (0 – 6) 10.7% 12.3% 13.5% 12.6% 11.7% 11.6% 9.0%

Level 2 (7 – 8) 22.8% 28.0% 26.0% 27.3% 25.1% 26.7% 25.4%

Level 3 (9 – 10) 66.5% 59.6% 60.5% 60.1% 63.2% 61.8% 65.6%

Rural Counties (p = .90) Sample Size 845 605 847 306 203 218

Level 1 (0 – 6) 13.9% 12.1% 14.6% 10.6% 9.5% 7.7%

Level 2 (7 – 8) 28.5% 28.7% 29.6% 26.8% 24.9% 34.1%

Level 3 (9 – 10) 57.6% 59.2% 55.8% 62.6% 65.6% 58.2%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

Table E22: Health Plan Satisfaction Rating (0 – 10), Reform, DY1 – DY8

Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would you use to rate your (your child’s) health plan?

DY1a DY2

b DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p < .0001) Sample Size 5,547 5,609 5,903 5,758 4,071 2,918 3,371

Level 1 (0 – 6) 16.3% 17.1% 18.7% 19.5% 13.7% 11.9% 13.3%

Level 2 (7 – 8) 25.6% 25.6% 27.1% 30.3% 27.3% 27.1% 27.1%

Level 3 (9 – 10) 58.1% 57.4% 54.1% 50.2% 59.0% 61.0% 59.7%

Rural Counties (p = .958) Sample Size 896 739 1,078 369 248 284

Level 1 (0 – 6) 18.9% 19.2% 22.1% 19.3% 17.7% 16.3%

Level 2 (7 – 8) 29.8% 28.3% 32.7% 28.0% 26.0% 32.2%

Level 3 (9 – 10) 51.3% 52.5% 45.2% 52.7% 56.3% 51.5%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

214

Table E23: Personal Doctor Satisfaction Rating (0 – 10), Reform, DY1 – DY8

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor?

DY1a DY2

b DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p = .027) Sample Size 4,505 5,339 5,266 5,052 3,615 2,643 3,084

Level 1 (0 – 6) 8.7% 9.0% 8.5% 8.4% 8.7% 6.7% 7.1%

Level 2 (7 – 8) 21.2% 17.6% 18.5% 18.5% 17.7% 20.3% 18.5%

Level 3 (9 – 10) 70.2% 73.4% 73.0% 73.1% 73.7% 73.0% 74.4%

Rural Counties (p = .962) Sample Size 864 649 971 346 228 266

Level 1 (0 – 6) 10.4% 9.7% 10.2% 8.7% 7.8% 14.9%

Level 2 (7 – 8) 21.8% 20.0% 22.9% 19.5% 19.9% 24.0%

Level 3 (9 – 10) 67.9% 70.3% 66.9% 71.9% 72.2% 61.1%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

Table E24: Specialist Satisfaction Rating (0 – 10), Reform, DY1 – DY8 Using any number from 0 – 10, where 0 is the worst and 10 is the best specialist possible, what number would

you use to rate your (your child’s) specialist?

DY1 DY2 DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p =. 426) Sample Size 1,859 2,360 1,928 1,668 1,418 951 1,037

Level 1 (0 – 6) 13.2% 13.7% 14.0% 14.0% 12.0% 12.0% 11.1%

Level 2 (7 – 8) 26.4% 23.0% 22.9% 23.2% 20.9% 24.0% 25.7%

Level 3 (9 – 10) 60.4% 63.3% 63.1% 62.8% 67.1% 64.1% 63.2%

Rural Counties (p = .90) Sample Size 305 176 292 104 68 73

Level 1 (0 – 6) 10.5% 17.4% 9.9% 15.1% 15.4% 11.8%

Level 2 (7 – 8) 21.5% 15.3% 21.6% 31.0% 30.1% 10.5%

Level 3 (9 – 10) 68.0% 67.3% 68.5% 53.8% 54.5% 77.8%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

215

Notes. *These findings were statistically significant across the seven demonstration years. CAHPS Survey fieldwork was not conducted for DY5.

Note. CAHPS Survey fieldwork was not conducted for DY5.

67% 58%

70%

60% 60% 57%

73%

63% 61%

54%

73%

63% 60%

50%

73%

63%

63.2%

59.0%

73.7%

67.1%

61.8% 61.0%

73.0%

64.1% 65.6%

59.7%

74.4%

63.2%

0%

20%

40%

60%

80%

100%

Health Care Satisfaction Health Plan Satisfaction† Personal Doctor Satisfaction†

Specialist Satisfaction

DY1 DY2 DY3 DY4 DY6 DY7 DY8

58%

51%

68% 68%

59%

53%

70% 67%

56%

45%

67% 69%

63%

53%

72%

54%

65.6%

56.3%

72.2%

54.5% 58.2%

51.5%

61.1%

77.8%

0%

20%

40%

60%

80%

100%

Health Care Satisfaction Health Plan Satisfaction Personal Doctor Satisfaction Specialist Satisfaction

DY2 DY3 DY4 DY6 DY7 DY8

Figure E25: Overall Satisfaction of Specialist at the Highest Level (9 – 10), Broward and Duval Counties, Reform, DY1 – DY8

Figure E26: Overall Satisfaction of Specialist at the Highest Level (9 – 10), Rural Counties, Reform, DY1 – DY8

216

Reform vs. Non-Reform Counties

Table E25: Health Care Satisfaction Rating (0 – 10), Reform vs. Non-Reform, DY6 – DY8 Using any number from 0 – 10, where 0 is the worst and 10 is the best health care possible, what number would

you use to rate all your health care in the last 6 months?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform (p = 0.3062)

Reform vs. Non-Reform (p = 0.814)

Reform vs. Non-Reform (p = 0.2456)

Sample Size 3,447 6,041 2,451 5,938 2,807 4,459

Level 1 (0 – 6) 11.6% 12.6% 11.5% 12.0% 8.9% 10.7%

Level 2 (7 – 8) 25.3% 26.9% 26.5% 27.3% 26.1% 24.9%

Level 3 (9 – 10) 63.2% 60.5% 62.0% 60.7% 65.1% 64.3%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Table E26: Health Plan Satisfaction Rating (0 – 10), Reform vs. Non-Reform, DY6 – DY8 Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would

you use to rate your (your child’s) health plan?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform

(p = 0.4533) Reform vs. Non-Reform

(p =0.4028) Reform vs. Non-Reform

(p = 0.207) Sample Size 4,456 8,312 3,172 7,897 3,655 6,071

Level 1 (0 – 6) 14.2% 15.4% 12.3% 14.0% 13.5% 15.3%

Level 2 (7 – 8) 27.3% 26.1% 27.0% 26.5% 27.4% 25.6%

Level 3 (9 – 10) 58.5% 58.4% 60.7% 59.5% 59.1% 59.1%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Table E27: Personal Doctor Satisfaction Rating (0 – 10), Reform vs. Non-Reform, DY6 – DY8

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform (p = 0.6292)

Reform vs. Non-Reform (p = 0.01)

Reform vs. Non-Reform (p = 0.8429)

Sample Size 3,974 6,978 2,872 6,635 3,350 5,152

Level 1 (0 – 6) 8.7% 8.1% 6.8% 10.2% 7.7% 7.8%

Level 2 (7 – 8) 17.8% 18.7% 20.2% 19.5% 18.9% 18.1%

Level 3 (9 – 10) 73.5% 73.2% 73.0% 70.3% 73.4% 74.1%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

217

Table E28: Specialist Satisfaction Rating (0 – 10), Reform vs. Non-Reform, DY6 – DY8 Using any number from 0 – 10, where 0 is the worst and 10 is the best specialist possible, what number would

you use to rate your (your child’s) specialist?

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform (p = 0.5624)

Reform vs. Non-Reform (p = 0.1066)

Reform vs. Non-Reform (p= 0.0018)

Sample Size 1,527 2,463 1,019 2,366 1,110 1,588

Level 1 (0 – 6) 12.2% 10.5% 12.3% 10.7% 11.2% 7.4%

Level 2 (7 – 8) 21.7% 22.9% 24.5% 19.7% 24.7% 18.6%

Level 3 (9 – 10) 66.1% 66.6% 63.2% 69.6% 64.2% 74.0%

Note. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Note. *These findings were statistically significant for Reform DY7 and Non-Reform DY7

63% 59%

74%

66% 61% 58%

73%

67% 62% 61%

73%

63% 61% 60%

70% 70% 65%

59%

73%

64% 64% 59%

74% 74%

0%

20%

40%

60%

80%

100%

Health Care Satisfaction Health Plan Satisfaction Personal DoctorSatisfaction*

Specialist Satisfaction**

Reform (DY6) Non-Reform (DY6) Reform (DY7) Non-Reform (DY7) Reform (DY8) Non-Reform (DY8)

Figure E27: Overall Satisfaction for Specialist at the Highest Level (9–10), Reform and Non-Reform Counties, DY6 – DY8

218

HEDIS Data Chronic Disease Management

Table E29: Antidepressant Medication Management – Effective Acute Phase Treatment (AMM Acute), 2009 – 2013

Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%) All Plans N/A 47.3% 47.3% 48.1% 50.5% 52.4% 1.2%

Reform Plans 52.0% 56.3% 56.4% 57.4% 55.1% 0.7%

Non Reform Plans 45.6% 46.8% 47.0% 50.4% 51.8% 1.6%

Difference Average Annual Difference (%) Reform-Non Reform N/A 6.4% 9.5% 9.4% 7.0% 3.2% 7.1%

Note. No data are available for 2008 for this HEDIS measure. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

Figure E28: Antidepressant Medication Management – Effective Acute Phase Treatment (AMM Acute), 2009 – 2013

219

Table E30: Antidepressant Medication Management – Effective Continuation Phase Treatment (AMM Continuation), 2009 – 2013

Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%) All Plans N/A 30.8% 30.0% 33.0% 34.6% 37.5% 1.8%

Reform Plans 29.8% 43.8% 44.0% 43.1% 41.7% 2.3%

Non Reform Plans 31.2% 29.2% 31.4% 33.6% 36.5% 1.5%

Difference Average Annual Difference (%)

Reform-Non Reform N/A -1.3% 14.5% 12.5% 9.5% 5.2% 8.1%

Notes. No data are available for 2008 for this HEDIS measure. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E29: Antidepressant Medication Management – Effective Continuation Phase Treatment (AMM Continuation), 2009 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

220

Table E31: Follow-Up after Hospitalization for Mental Illness – 7 Day (FMH7), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 27.7% 33.1% 24.4% 27.1% 34.8% 34.0% 1.1%

Reform Plans 20.6% 29.3% 25.4% 23.1% 22.7% 23.5% -0.2%

Non Reform Plans 30.5% 37.0% 24.2% 28.4% 37.5% 36.3% 1.0%

Difference Average Annual Difference (%) Reform-Non Reform -9.9% -7.8% 1.2% -5.3% -14.9% -12.9% -8.2%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E30: Follow-Up after Hospitalization for Mental Illness – 7 Day (FMH7), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

221

Table E32: Follow-Up after Hospitalization for Mental Illness – 30 Day (FMH30), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 31.9% 50.5% 41.4% 47.0% 53.8% 51.2% 3.2%

Reform Plans 35.5% 46.7% 41.3% 44.3% 41.2% 40.8% 0.4%

Non Reform Plans 47.0% 51.9% 41.4% 47.9% 56.5% 53.5% 1.5%

Difference Average Annual Difference (%) Reform-Non Reform -11.4% -5.3% -0.1% -3.6% -15.3% -12.8% -8.1%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E31: Follow-Up after Hospitalization for Mental Illness – 30 Day (FMH 30), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

222

Table E33: Controlling Blood Pressure (CBP), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 51.2% 52.5% 53.1% 52.5% 51.8% 51.1% -0.1%

Reform Plans 46.3% 55.9% 53.4% 46.3% 52.9% 45.4% -0.6%

Non Reform Plans 52.7% 51.6% 53.0% 54.3% 51.5% 52.9% 0.1%

Difference Average Annual Difference (%) Reform-Non Reform -6.4% 4.3% 0.4% -8.1% 1.4% -7.5% -2.6%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E32: Controlling Blood Pressure – Total (CBP), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

223

Table E34: Comprehensive Diabetes Testing – HbA1C Testing (CDC HbA1C Testing), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 75.7% 76.2% 77.2% 80.0% 78.4% 79.5% 0.8%

Reform Plans 78.9% 80.1% 82.8% 81.9% 82.2% 79.5% 0.2%

Non Reform Plans 74.7% 75.1% 76.4% 79.6% 77.3% 79.6% 1.0%

Difference Average Annual Difference (%) Reform-Non Reform 4.2% 5.1% 6.4% 2.3% 5.0% -0.1% 3.8%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E33: Comprehensive Diabetes Testing – HbA1C Testing (CDC HbA1C Testing), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

224

Table E35: Comprehensive Diabetes – HbA1C Poor Control INVERSE (CDC Poor), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 48.4% 50.5% 46.2% 43.4% 45.9% 45.1% -1.0%

Reform Plans 48.4% 46.8% 44.9% 48.6% 43.6% 48.9% -0.1%

Non Reform Plans 48.5% 51.7% 46.4% 42.5% 46.6% 44.0% -1.2%

Difference Average Annual Difference (%) Reform-Non Reform -0.1% -4.9% -1.5% 6.2% -3.0% 4.9% 0.3%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E34: Comprehensive Diabetes – HbA1C Poor Control INVERSE (CDC Poor), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

225

Table E36: Comprehensive Diabetes – Good Control (CDC Good), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 31.8% 42.9% 45.0% 48.7% 46.0% 46.6% 2.5%

Reform Plans 32.2% 48.1% 47.5% 43.7% 47.9% 43.6% 1.5%

Non Reform Plans 31.7% 41.4% 44.6% 49.6% 45.5% 47.5% 2.8%

Difference Average Annual Difference (%) Reform-Non Reform 0.5% 6.7% 2.8% -5.9% 2.5% -3.9% 0.4%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E35: Comprehensive Diabetes – Good Control (CDC Good), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

226

Table E37: Comprehensive Diabetes – Eye Exam (CDC Eye), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 36.2% 42.4% 47.9% 51.7% 46.3% 46.7% 1.9%

Reform Plans 35.7% 44.0% 45.4% 49.3% 50.2% 48.7% 2.5%

Non Reform Plans 36.3% 41.9% 48.3% 52.1% 45.2% 46.1% 1.8%

Difference Average Annual Difference (%) Reform-Non Reform -0.6% 2.0% -2.9% -2.8% 4.9% 2.6% 0.5%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E36: Comprehensive Diabetes – Eye Exam (CDC Eye), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

227

Table E38: Comprehensive Diabetes – LDL Screening (CDC LDLS), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 76.6% 77.2% 78.6% 80.3% 78.4% 79.4% 0.6%

Reform Plans 80.0% 80.2% 83.5% 81.8% 81.9% 80.1% 0.1%

Non Reform Plans 75.6% 76.3% 77.9% 80.0% 77.4% 79.2% 0.7%

Difference Average Annual Difference (%) Reform-Non Reform 4.4% 3.8% 5.6% 1.8% 4.5% 0.8% 3.5%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E37: Comprehensive Diabetes – LDL Screening (CDC LDLS), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

228

Table E39: Comprehensive Diabetes – LDL Control (CDC LDLC), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 29.5% 30.8% 34.1% 33.4% 35.0% 34.4% 1.0%

Reform Plans 29.3% 35.5% 36.1% 36.9% 37.8% 32.1% 0.6%

Non Reform Plans 29.5% 29.4% 33.8% 32.8% 34.2% 35.0% 1.2%

Difference Average Annual Difference (%) Reform-Non Reform -0.2% 6.1% 2.3% 4.1% 3.6% -3.0% 2.2%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E38: Comprehensive Diabetes – LDL Control (CDC LDLC), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

229

Table E40: Comprehensive Diabetes – Nephropathy (CDC Neph), 2008 – 2013 Category 2008 2009 2010 2011 2012 2013 Average Annual Change (%)

All Plans 77.6% 77.1% 77.7% 79.6% 78.7% 79.9% 0.5%

Reform Plans 79.3% 80.3% 81.9% 83.1% 82.3% 80.2% 0.4%

Non Reform Plans 77.1% 76.1% 77.1% 79.0% 77.7% 79.8% 0.6%

Difference Average Annual Difference (%) Reform-Non Reform 2.1% 4.3% 4.8% 4.1% 4.7% 0.4% 3.4%

Note. The Average Annual Change values are calculated based on raw percentages and may be different than a value calculated manually using this table due to rounding.

Figure E39: Comprehensive Diabetes – Nephropathy (CDC Neph), 2008 – 2013

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013

All Plans

Reform Plans

Non Reform Plans

Linear (All Plans)

Linear (Reform Plans)

Linear (Non Reform Plans)

230

Domain i.c: How has the demonstration increased timeliness of services?

CAHPS Survey Data Counties Over Time

Table E41: Needed Care Right Away, Reform, DY1 – DY8

In the last 6 months, when you needed care right away, how often did you get care as soon as you needed?c

DY1a DY2

b DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p = 0.0167) Sample Size 2,101 2,494 2,229 2,022 1,415 980 1,133

Never/Sometimes 17.8% 16.8% 16.2% 16.9% 17.4% 12.4% 12.3%

Usually 15.1% 10.6% 9.3% 11.1% 8.8% 9.7% 6.9%

Always 67.1% 72.6% 74.5% 72.0% 73.8% 77.9% 80.9%

Rural Counties (p = 0.706) Sample Size 403 290 405 139 81 95

Never/Sometimes 13.5% 14.9% 12.8% 20.3% 6.6% 12.0%

Usually 10.2% 10.6% 12.4% 9.6% 9.7% 5.1%

Always 76.4% 74.5% 74.8% 70.1% 83.7% 83.0%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

cWording of question is different than in previous years.

231

Note. *These findings were statistically significant at p<0.05 across the seven demonstration years. CAHPS Survey fieldwork was not conducted for DY5.

Note. CAHPS Survey fieldwork was not conducted for DY5.

67%

73% 75% 72% 74% 78%

81%

0%

20%

40%

60%

80%

100%

DY1 DY2 DY3 DY4 DY6 DY7 DY8

Always

76% 75% 75% 70%

84% 83%

0%

20%

40%

60%

80%

100%

DY2 DY3 DY4 DY6 DY7 DY8

Always

Figure E40: Respondents Who Reported "Always" with Needed Care Right Away, Broward and Duval Counties, Reform, DY1 – DY8

Figure E41: Respondents Who Reported "Always" with Needed Care Right Away, Rural Counties, Reform, DY1 – DY8

232

Table E42: Appointments When Wanted, Reform, DY1 – DY8 In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor's office or

clinic as soon as you needed?c

DY1a DY2

b DY3 DY4 DY6 DY7 DY8

Broward and Duval Counties (p < .0001) Sample Size 3,615 4,581 4,399 4,029 2,906 2,045 2,440

Never 4.2% 3.1% 3.7% 3.1% 2.9% 2.0% 2.5%

Sometimes 14.7% 16.8% 14.5% 15.1% 18.2% 15.3% 17.0%

Usually 18.5% 13.5% 12.8% 13.3% 12.5% 12.9% 12.3%

Always 62.6% 66.7% 69.0% 68.5% 66.4% 69.8% 68.2%

Rural Counties (p = .0012) Sample Size 745 549 779 286 182 195

Never 3.6% 2.3% 3.0% 1.8% 0.2% 0.5%

Sometimes 9.8% 10.4% 10.0% 17.3% 11.4% 9.5%

Usually 15.0% 11.4% 13.6% 23.6% 12.3% 6.1%

Always 71.7% 75.9% 73.3% 57.4% 76.1% 83.9%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. CAHPS Survey fieldwork was not conducted for DY5. aSince question structure was different for DY1 for Broward and Duval counties, p-values are calculated only for DY2 – DY8.

bSurvey fieldwork started in DY2 for Baker, Clay, and Nassau counties (rural), so p-values are calculated only for DY2 – DY8.

cWording of question is different than in previous years.

233

Notes. *These findings were statistically significant at p<0.05 across the seven demonstration years. CAHPS Survey fieldwork was not conducted for DY5.

Notes. *These findings were statistically significant at p<0.05 across the five demonstration years. CAHPS Survey fieldwork was not conducted for DY5.

63% 67% 69% 69% 66%

70% 68%

0%

20%

40%

60%

80%

100%

DY1 DY2 DY3 DY4 DY6 DY7 DY8

Always

72% 76% 73%

57%

76%

84%

0%

20%

40%

60%

80%

100%

DY2 DY3 DY4 DY6 DY7 DY8

Always

Figure E42: Respondents Who Reported "Always" with Appointments When Wanted, Broward and Duval Counties, Reform, DY1 – DY8

Figure E43: Respondents Who Reported "Always" with Appointments When Wanted, Rural Counties, Reform, DY1 – DY8

234

Reform vs. Non-Reform Counties

Table E43: Needed Care Right Away, Reform vs. Non-Reform, DY6 – DY8

In the last 6 months, when you needed care right away, how often did you get care as soon as you needed?*

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform (p = 0.2702)

Reform vs. Non-Reform (p = 0.0258)

Reform vs. Non-Reform (p = 0.077)

Sample Size 1,559 2,775 1,063 2,556 1,233 1,995

Never/Sometimes 17.6% 14.4% 11.8% 17.4% 12.2% 15.2%

Usually 9.4% 10.3% 10.3% 10.7% 7.0% 9.3%

Always 73.0% 73.5% 74.6% 70.9% 82.2% 74.1%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. *Wording of question is different than in previous years.

Note. *Trend was statistically significant at <.05 for DY7.

73% 75%

82%

74% 71% 74%

0%

20%

40%

60%

80%

100%

DY6 DY7* DY8

Reform Non-Reform

Figure E44: Respondents Who Reported "Always" with Needed Care Right Away, Reform vs. Non-Reform, DY6 – DY8

235

Table E44: Appointments When Wanted, Reform vs. Non-Reform, DY6 – DY8 In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor's office or

clinic as soon as you needed? *

DY6 DY7 DY8

Reform Non-Reform Reform Non-Reform Reform Non-Reform

Reform vs. Non-Reform (p = 0.9055)

Reform vs. Non-Reform (p = 0.0018)

Reform vs. Non-Reform (p = 0.9392)

Sample Size 3,203 5,406 2,230 5,180 2,635 4,117

Never 2.7% 2.5% 1.9% 4.3% 2.3% 2.4%

Sometimes 18.2% 19.0% 15.0% 18.0% 16.5% 16.2%

Usually 13.5% 13.0% 12.9% 13.7% 11.8% 12.6%

Always 65.6% 65.6% 70.3% 64.0% 69.4% 68.8%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. *Wording of question is different than in previous years.

Note. *Trend was statistically significant at <.05 for DY7.

66% 70% 69%

66% 64% 69%

0%

20%

40%

60%

80%

100%

DY6 DY7* DY8

Reform Non-Reform

Figure E45: Respondents Who Reported "Always" with Appointments When Wanted, Reform vs. Non-Reform, DY6 – DY8

236

Domain ii.c: Are there differences in enrollees’ satisfaction with and experiences with care between plans with different benefit packages ? Between plans that offer additional benefits vs. those that do not? Table E45: Health Care Satisfaction Rating (0 – 10), Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

Using any number from 0 to 10, where 0 is the worst and 10 is the best health care possible, what number would you use to rate your (your child’s) health care in the last 6 months?

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.0847)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.7254)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.5412) Sample Size 2,005 1,442 1,410 1,040 1,236 1,566

Level 1 (0 – 6) 12.9% 10.6% 12.1% 10.8% 8.0% 9.4%

Level 2 (7 – 8) 27.2% 23.7% 27.3% 26.0% 25.8% 26.4%

Level 3 (9 -10) 59.8% 65.7% 60.6% 63.2% 66.3% 64.2%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

60% 61% 66% 66% 63% 64%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

OTC Expanded Benefits No OTC Expanded Benefits

Figure E46: Overall Satisfaction with Health Care in the Last 6 Months at the Highest Level (9 – 10), Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

237

Table E46: Health Plan Satisfaction Rating (0 – 10), Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 and DY7

Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would you use to rate your (your child’s) health plan?

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.0942)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.8914)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.8571) Sample Size 2,618 1,838 1,862 1,308 1,628 2,021

Level 1 (0 – 6) 16.2% 12.7% 12.6% 12.1% 13.8% 13.2%

Level 2 (7 – 8) 27.5% 27.1% 27.6% 26.6% 26.9% 27.8%

Level 3 (9 –10) 56.3% 60.2% 59.9% 61.2% 59.3% 59.0%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

56% 60% 59% 60% 61% 59%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

OTC Expanded Benefits No OTC Expanded Benefits

Figure E47: Overall Satisfaction of Health Plan at the Highest Level (9 – 10), Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

238

Table E47: Personal Doctor Satisfaction Rating (0 – 10), Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor?

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.0668)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.6705)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p= 0.0002)

Sample Size 2,317 1,657 1,673 1,199 1,492 1,855

Level 1 (0 – 6) 8.6% 8.8% 7.6% 6.2% 6.1% 8.7%

Level 2 (7 – 8) 20.3% 15.9% 20.0% 20.5% 15.3% 21.2%

Level 3 (9 -10) 71.1% 75.4% 72.4% 73.4% 78.6% 70.1%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Note. *Trend was statistically significant at <.05 for DY8.

71% 72% 79%

75% 73% 70%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8*

OTC Expanded Benefits No OTC Expanded Benefits

Figure E48: Overall Satisfaction of Personal Doctor at the Highest Level (9 – 10), Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

239

Table E48: Would Recommend Health Plan to Family or Friends, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

Would you recommend your health plan to your family or friends? (Adults only)

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.0291)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.4877)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.2418) Sample Size 722 379 1,835 1292 1,606 1,997

Definitely yes 37.9% 48.2% 54.7% 53.0% 55.6% 54.1%

Probably yes 34.2% 33.2% 36.8% 37.6% 34.2% 37.8%

Definitely not 13.6% 11.8% 4.9% 6.6% 6.0% 5.2%

Probably not 14.2% 6.8% 3.6% 2.8% 4.2% 2.9%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. Table reflects responses from adults only.

Note. Trend was statistically significant at <.05 for DY6.

38%

55% 56%

48% 53% 54%

0%

20%

40%

60%

80%

100%

DY6* DY7 DY8

OTC Expanded Benefits No OTC Expanded Benefits

Figure E49: Respondents who reported "Definitely yes" to Recommend Health Plan, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

240

Table E49: Specialist Satisfaction Rating (0 – 10), Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

Using any number from 0 to 10, where 0 is the worst and 10 is the best specialist possible, what number would you use to rate your (your child’s) specialist?

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.2699)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.8481)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.5869) Sample Size 819 708 529 490 456 653

Level 1 (0 – 6) 13.7% 11.1% 12.8% 11.9% 11.7% 10.9%

Level 2 (7 – 8) 23.8% 20.1% 23.0% 25.6% 26.8% 23.4%

Level 3 (9 –10) 62.5% 68.8% 64.3% 62.5% 61.5% 65.8%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

63% 64% 62%

69% 63%

66%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

OTC Expanded Benefits No OTC Expanded Benefits

Figure E50: Overall Satisfaction of Specialist at the Highest Level (9–10), Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

241

Table E50: Health Care Satisfaction Rating (0 – 10), Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

Using any number from 0 to 10, where 0 is the worst and 10 is the best health care possible, what number would you use to rate your (your child’s) health care in the last 6 months?

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.7852)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.4752)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.2842) Sample Size 1,434 2,013 1,065 1,385 1,419 1,383

Level 1 (0 – 6) 12.1% 11.2% 10.2% 12.5% 9.0% 8.7%

Level 2 (7 – 8) 25.7% 24.9% 25.8% 27.2% 24.3% 28.0%

Level 3 (9 –10) 62.2% 64.0% 64.0% 60.4% 66.7% 63.3%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

62% 64% 67%

64% 60%

63%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

Adult Dental Benefits No Adult Dental Benefits

Figure E51: Overall Satisfaction of Health Care in Last 6 Months at the Highest Level (9–10), Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

242

Table E51: Health Plan Satisfaction Rating (0 – 10), Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8 Using any number from 0 – 10, where 0 is the worst and 10 is the best health plan possible, what number would

you use to rate your (your child’s) health plan?

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.2853)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.1749)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.4724) Sample Size 1,867 2,589 1,393 1,777 1,872 1,777

Level 1 (0 – 6) 15.6% 13.0% 10.5% 14.0% 13.9% 13.0%

Level 2 (7 – 8) 27.5% 27.2% 27.2% 26.9% 26.2% 28.7%

Level 3 (9 –10) 57.0% 59.8% 62.3% 59.1% 59.9% 58.3%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

57% 62% 60% 60% 59% 58%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

Adult Dental Benefits No Adult Dental Benefits

Figure E52: Health Plan Satisfaction Rating (0 – 10), Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

243

Table E52: Personal Doctor Satisfaction Rating (0 – 10), Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

Using any number from 0 – 10, where 0 is the worst and 10 is the best personal doctor possible, what number would you use to rate your (your child’s) personal doctor?

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.6018)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.1459)

Adult Dental Benefits vs. No Adult Dental Benefits

(p= <0.0001) Sample Size 1,657 2,317 1,255 1,617 1,703 1,644

Level 1 (0 –6) 8.7% 8.6% 6.1% 7.5% 6.2% 9.1%

Level 2 (7 –8) 18.8% 16.9% 18.1% 22.2% 15.2% 22.4%

Level 3 (9 -10) 72.5% 74.5% 75.8% 70.3% 78.6% 68.5%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Note. *Trend was statistically significant at <.05 for DY8.

73% 76%

79% 75%

70% 69%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8*

Adult Dental Benefits No Adult Dental Benefits

Figure E53: Personal Doctor Satisfaction Rating (0 – 10), Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

244

Table E53: Would Recommend Health Plan to Family or Friends, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

Would you recommend your health plan to your family or friends?

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.0736)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.2254)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.0504) Sample Size 480 621 1,368 1,759 1,844 1,759

Definitely yes 38.6% 47.2% 55.8% 51.9% 56.1% 53.3%

Probably yes 33.0% 34.1% 36.8% 37.7% 34.3% 38.4%

Definitely not 14.7% 11.1% 4.6% 7.0% 5.1% 5.8%

Probably not 13.7% 7.6% 2.8% 3.5% 4.4% 2.5%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Note. *Trend was statistically significant at <.05 for DY8.

39%

56% 56%

47% 52% 53%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8*

Adult Dental Benefits No Adult Dental Benefits

Figure E54: Would Recommend Health Plan to Family or Friends, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

245

Table E54: Specialist Satisfaction Rating (0 – 10), Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

Using any number from 0 – 10, where 0 is the worst and 10 is the best specialist possible, what number would you use to rate your (your child’s) specialist?

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.2485)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.8255)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.8244) Sample Size 612 915 397 622 512 597

Level 1 (0 – 6) 14.5% 10.2% 13.1% 11.6% 10.7% 11.6%

Level 2 (7 – 8) 21.9% 21.5% 23.1% 25.7% 25.9% 23.6%

Level 3 (9 – 10) 63.6% 68.3% 63.8% 62.7% 63.4% 64.8%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

64% 64% 63% 68%

63% 65%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

Adult Dental Benefits No Adult Dental Benefits

Figure E55: Specialist Satisfaction Rating (0 – 10), Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

246

Domain ii.d: Does access to and quality of care vary between plans with different benefit packages? Between plans that offer additional benefits vs . those that do not? Table E55: Ease of Getting Specialist Appointments, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

In the last 6 months, how often was it easy to get appointments for you (your child) with specialists?

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.2049)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.6412)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.4977) Sample Size 795 670 504 465 461 696

Never 10.6% 11.5% 12.6% 13.5% 7.1% 5.6%

Sometimes 20.9% 22.3% 21.3% 22.2% 19.1% 18.0%

Usually 16.1% 21.6% 14.5% 18.8% 21.2% 17.8%

Always 52.4% 44.6% 51.5% 45.5% 52.6% 58.6%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

52% 52% 53%

45% 46%

59%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

OTC Expanded Benefits No OTC Expanded Benefits

Figure E56: Respondents Who Reported “Always” with Ease of Getting Specialist Appointments, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

247

Table E56: Ease of Getting Care, Tests, or Treatment Thought Needed, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

In the last 6 months, how often was it easy to get the care, tests, or treatment you thought you (your child) needed through your health plan?

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.1887)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.5069)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.3339) Sample Size 1,058 818 717 530 1,144 1,475

Never 10.5% 7.8% 7.0% 8.0% 2.4% 1.7%

Sometimes 18.7% 14.5% 19.5% 16.5% 15.1% 13.2%

Usually 17.5% 19.0% 12.6% 16.7% 14.4% 17.2%

Always 53.3% 58.6% 60.8% 58.8% 68.2% 67.8%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

52% 52% 53%

45% 46%

59%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

OTC Expanded Benefits No OTC Expanded Benefits

Figure E57: Respondents Who Reported “Always” with Ease of Getting Care, Tests, or Treatment Thought Needed, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

248

Table E57: Enrollees Who Reported Whether or Not They Had a Personal Doctor, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt. Do you (your child) have a personal doctor?

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.5725)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.4932)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.5635) Sample Size 2,642 1,847 1,885 1,305 1,633 2,020

Yes 89.9% 90.7% 91.0% 92.0% 91.0% 91.7%

No 10.1% 9.3% 9.0% 8.0% 9.0% 8.3%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Note. *Trend was statistically significant at <.05 for DY6 and DY7.

90% 91% 91% 91% 92% 92%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

OTC Expanded Benefits No OTC Expanded Benefits

Figure E58: Enrollees Who Reported They Had a Personal Doctor, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

249

Table E58: Needed Care Right Away, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

In the last 6 months, when you needed care right away, how often did you get care as soon as you needed?*

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.9689)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.2423)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.4192)

Sample Size 904 655 603 459 533 692

Never/Sometimes 17.6% 17.6% 14.7% 9.9% 12.1% 12.0%

Usually 8.6% 9.2% 10.3% 9.3% 8.3% 5.7%

Always 73.8% 73.2% 75.0% 80.9% 79.6% 82.3%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. *Question wording is different in DY8 compared to previous years.

74% 75% 80%

73%

81% 82%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

OTC Expanded Benefits No OTC Expanded Benefits

Figure E59: Respondents Who Reported “Always” with Getting Needed Care Right Away, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

250

Table E59: Appointments When Wanted, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8 In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor’s office or

clinic as soon as you needed?*

DY6 DY7 DY8

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits

No OTC Expanded Benefits

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.4302)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.5316)

OTC Expanded Benefits vs. No OTC Expanded Benefits

(p = 0.0358) Sample Size 1,863 1,340 1,273 956 1,163 1,467

Never 2.5% 2.9% 2.2% 1.6% 1.2% 3.0%

Sometimes 19.1% 17.5% 14.9% 15.1% 18.8% 14.8%

Usually 11.9% 14.8% 14.6% 11.6% 11.7% 11.8%

Always 66.5% 64.8% 68.3% 71.8% 68.3% 70.4%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. *Question wording is different in DY8 compared to previous years.

Note. *Trend was statistically significant at <.05 for DY8.

Figure E60: Respondents Who Reported “Always” Getting Appointments When Wanted, Plans That Offer OTC Pharmacy Expanded Benefits vs. Plans That Do Not Offer OTC Pharmacy Expanded Benefits, DY6 – DY8

67% 68% 68% 65%

72% 70%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8*

OTC Expanded Benefits

251

Table E60: Ease of Getting Specialist Appointments, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

In the last 6 months, how much of a problem, if any/how often was it easy to get appointments for you (your child) with specialists?

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.1848)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.6107)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.3196) Sample Size 586 879 372 597 524 633

Never 12.9% 9.6% 11.9% 14.2% 8.0% 4.5%

Sometimes 20.1% 23.1% 22.5% 21.4% 19.1% 17.9%

Usually 16.4% 21.6% 14.7% 19.1% 19.0% 19.1%

Always 50.5% 45.6% 50.9% 45.4% 53.9% 58.5%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

51% 51% 54%

46% 45%

59%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

Adult Dental Benefits No Adult Dental Benefits

Figure E61: Respondents Who Reported “Always” with Ease of Getting Specialist Appointments, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

252

Table E61: Ease of Getting Care, Tests, or Treatment Thought Needed, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

In the last 6 months, how often was it easy to get the care, tests, or treatment you thought you (your child) needed through your health plan?

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.2609)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.4434)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.1701) Sample Size 776 1,100 515 732 1,281 1,338

Never 9.2% 8.7% 6.4% 8.6% 2.6% 1.4%

Sometimes 19.0% 13.9% 20.3% 15.6% 15.2% 12.8%

Usually 17.3% 19.3% 13.9% 15.9% 15.0% 17.2%

Always 54.5% 58.1% 59.4% 59.9% 67.3% 68.5%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

55% 59%

67%

58% 60%

69%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

Adult Dental Benefits No Adult Dental Benefits

Figure E62: Ease of Getting Care, Tests, or Treatment Thought Needed, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

253

Table E62: Enrollees Who Reported Whether or Not They Had a Personal Doctor, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

A personal doctor is the one you would see if you need a checkup, want advice about a health problem, or get sick or hurt. Do you (your child) have a personal doctor?

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.3824)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.7344)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.1873) Sample Size 1,886 2,603 1,408 1,782 1,880 1,773

Yes 91.0% 89.8% 91.8% 91.3% 90.5% 92.3%

No 9.0% 10.2% 8.2% 8.7% 9.5% 7.7%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data.

Note. *Trend was statistically significant at <.05 for DY6 and DY7.

91% 92% 91% 90% 91% 92%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

Adult Dental Benefits No Adult Dental Benefits

Figure E63: Enrollees Who Reported They Had a Personal Doctor, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

254

Table E63: Needed Care Right Away, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

In the last 6 months, when you needed care right away, how often did you get care as soon as you needed?*

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.9641)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.6639)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.7748) Sample Size 634 925 451 611 606 619

Never/Sometimes 18.1% 17.2% 13.2% 10.7% 12.6% 11.4%

Usually 9.0% 9.0% 9.0% 10.2% 7.2% 6.3%

Always 72.9% 73.8% 77.7% 79.1% 80.1% 82.3%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. *Question wording is different in DY8 compared to previous years.

73% 78% 80%

74% 79%

82%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8

Adult Dental Benefits No Adult Dental Benefits

Figure E64: Respondents Who Reported “Always” Getting Needed Care Right Away, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

255

Table E64: Appointments When Wanted, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8 In the last 6 months, how often did you get an appointment for a check-up or routine care at a doctor's office or

clinic as soon as you needed?*

DY6 DY7 DY8

Adult Dental

Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits

No Adult Dental

Benefits

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.2404)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.433)

Adult Dental Benefits vs. No Adult Dental Benefits

(p = 0.0045) Sample Size 1,333 1,870 950 1,279 1,339 1,291

Never 2.8% 2.7% 2.5% 1.3% 1.4% 3.2%

Sometimes 17.8% 18.5% 15.0% 15.0% 19.2% 13.5%

Usually 11.5% 15.3% 14.0% 11.9% 12.1% 11.4%

Always 67.8% 63.6% 68.5% 71.9% 67.3% 71.9%

Notes. Table provides unweighted sample sizes and weighted percentages. Statistical tests are based on weighted data. *Question wording is different in DY8 compared to previous years.

Note. *Trend was statistically significant at <.05 for DY8.

68% 69% 67% 64%

72% 72%

0%

20%

40%

60%

80%

100%

DY6 DY7 DY8*

Adult Dental Benefits No Adult Dental Benefits

Figure E65: Respondents Who Reported “Always” Getting Appointments When Wanted, Plans That Offer Adult Dental Expanded Benefits vs. Plans That Do Not Offer Adult Dental Expanded Benefits, DY6 – DY8

256

Appendix F: Claims, Eligibility, and Encounter Data: Analyses

Utilization Analyses

Multivariate Analyses A series of multivariate analyses were conducted to better understand the pattern of changes in utilization of emergency room, inpatient, outpatient, medical, and pharmacy health services, as well as to control for any differences in age, race, or gender between the Control and Reform counties. As with the univariate analysis, the multivariate analysis examined trends in PMPM utilization over time and whether these trends significantly differed between the Reform vs. Control counties. These models were estimated using generalized estimating equations (GEEs) that account for correlation of observations over time using the XTGEE procedure in Stata. Because health services utilization was calculated on a PMPM basis, this analysis used a person-month observation (one observation per person per month). Thus, an individual could provide up to thirty-six observations to the analyses. Because the measures of utilization are counts of encounters/visits, a negative binomial distribution was used in the GEE models to account for the skewed and long-tailed distribution of this type of data. In other words, most individuals had zero or one encounter during a month, but some people had a large number of encounters/visits. This type of data has been shown to be well represented by a negative binomial distribution. First, the impact of Reform was assessed by including an indicator for whether or not the observation was from an individual in one of the Reform counties during the utilization analysis study period (SFY1011 – SFY1213 in Broward and Duval counties). This showed the shift in the intercept associated with Reform (i.e., the average difference in the mean amount of utilization between the Reform and Control counties during the study period). Additionally, the analyses included a variable for month that modeled the overall time trend over the study period. Finally, an interaction of Reform and month was included to assess whether the trend in utilization of health services significantly differed between the Reform and Control counties. A GEE model that assumes a negative binomial distribution with a log link was estimated: PMPM Health Services Utilized = exp(Montht∙β1+ Reformi ∙β2 + Reformi*Montht∙β3 + Age∙β4 + Gender∙β6 + Race∙β7 + εit ) (1) where “exp” indicates the log link and εit is an error term that follows a negative binomial distribution. One advantage of using this specification is that the exponentiated coefficients, which are presented in the results tables, can be interpreted as incidence rate ratios. Thus the coefficients can be interpreted as the percent difference in the average amount of PMPM health service utilization associated with a one-unit change in the covariate. The percent difference in the trend in the average number of health services utilized (by type of service) between the Reform and Control counties can thus be assessed by

examining the coefficient for Reform*Month (3 ). Additionally, to obtain an estimate of the likely difference in health service utilization due to Reform, average PMPM health services utilized were predicted assuming all enrollees were in Control counties using the multivariate models, and then average PMPM health services utilized were calculated again to determine what PMPM health services utilized would have been if the trend in utilization had instead followed the trend observed in the Reform counties. These equations (and all other equations) were estimated separately for enrollees in

257

MEG #1 (SSI) and MEG #2 (TANF) and for each claim/encounter type (emergency room, inpatient, outpatient, medical, and pharmacy health services).

Fiscal Analyses

Multivariate Analyses A series of multivariate analyses were conducted to better understand the pattern of changes in expenditures, as well as to control for any differences in age, race, or gender between the Control and Reform counties. As with the univariate analyses, the multivariate analyses examined trends in PMPM expenditures over time and whether these trends significantly differed between the Reform vs. Control counties. These models were estimated using GEEs that account for correlation of observations over time using the XTGEE procedure in Stata. Because expenditures were calculated on a PMPM basis, this analysis used a person-month observation (one observation per person per month). Thus, an individual could provide up to seventy-two observations to the analyses. Additionally, because approximately 28% of person-months had zero expenditures, a two-part model, as used in the Rand Health Insurance Experiment, was used to obtain unbiased estimates. If an ordinary least squares regression is used with data that has clustering at zero expenditures, the estimated impact of Reform would be biased towards zero. A Rand Two-Part model produces unbiased estimates by first examining the impact of Reform on the probability of having any expenditures using a GEE logistic regression model and then by estimating the impact of Reform on total expenditures on only those observations with expenditures that were greater than zero using a GEE gamma regression, which accounts for the skewed distribution of expenditures. First, the impact of Reform was assessed by including an indicator for whether or not the observation was from an individual in one of the Reform counties during the study period (SFY0708 – SFY1213 in Broward and Duval counties). This showed the shift in the intercept associated with Reform (i.e., the average difference associated with the Reform counties during the Reform period in either the probability of having any PMPM expenditures or total expenditures given that there were non-zero expenditures). Additionally, the analyses included a variable for month that modeled the overall time trend over the study period. Finally, an interaction of Reform and month was included to assess whether the trend in the probability of any expenditures or total expenditures significantly differed between the Reform and Control counties. Because expenditure data were highly skewed, and thus not normally distributed, a gamma distribution was assumed for the equation examining total expenditures. Therefore, the GEE specified a binomial distribution with a logit link for the first equation and a gamma distribution with a log link for the second equation. Both equations included the same covariates: Any PMPM Expendituresit or PMPM Expendituresit = exp(Montht∙β1+ Reformi ∙β2 + Reformi*Montht∙β3 + Age∙β4 + Gender∙β6 + Race∙β7 + εit ) (2) where “exp” indicates the logit or log link and εit is an error term that follows a binomial distribution for the first equation and a gamma distribution for the second equation. One advantage of using these specifications is that the coefficients from the first equation can be interpreted as odds ratios and the coefficients for the gamma regression can be interpreted as the percent change in PMPM expenditures associated with a one-unit change in the covariate. The percent difference in the trend in the odds of having any expenditures or total expenditures between the Reform and Control counties can thus be

assessed by examining the coefficient for Reform*Month (3 ). Additionally, to obtain an estimate of

258

the likely difference in expenditures due to Reform, average PMPM expenditures were predicted assuming all enrollees were in Control counties using the multivariate models, and then average PMPM expenditures were calculated again to determine what PMPM expenditures would have been if the trend in expenditures had instead followed the trend observed in the Reform counties. These equations (and all other equations) were estimated separately for enrollees in MEG #1 (SSI) and MEG #2 (TANF).

259

Appendix G: Plan Benefits Detailed Tables

Table G1: Monthly Medicaid Reform Market Share, Broward County, Plans Existent in both 2007 and 2013

Broward County Plans

Type Year Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

Humana Family

H M O

2007 8.4% 9.4% 10.1% 9.8% 9.6% 9.4% 9.5% 9.3% 5.6% 5.4% 5.3% 5.1%

2011 4.2% 4.0% 3.7% 3.5% 3.4% 3.2% 3.2% 3.1% 3.0% 2.9% 2.8% 2.6%

2012 2.6% 2.5% 2.4% 2.3% 2.7% 3.1% 3.5% 3.9% 4.3% 4.6% 5.0% 5.2%

2013 5.5% 5.8% 6.0% 6.4% 6.4% 6.5% 6.5% 6.6% 6.7% 6.8% 6.8% 6.9%

Children’s Medical Services

P S N

2007 2.5% 2.1% 1.9% 1.9% 1.9% 1.9% 1.8% 1.9% 1.2% 1.1% 1.1% 1.1%

2011 3.1% 3.2% 3.1% 3.2% 3.2% 3.2% 3.2% 3.2% 3.2% 3.3% 3.3% 3.3%

2012 3.0% 1.6% 5.2% 3.3% 3.3% 3.3% 3.3% 3.3% 3.4% 3.4% 3.9% 3.4%

2013 3.4% 3.8% 3.4% 3.6% 3.4% 3.4% 3.4% 3.4% 3.4% 3.4% 3.4% 3.4%

South Florida Community Care Network

P S N

2007 9.2% 8.0% 7.0% 6.9% 6.7% 6.6% 6.3% 6.2% 3.7% 3.5% 3.4% 3.2%

2011 22.3% 22.3% 22.5% 22.6% 22.4% 22.4% 22.3% 22.2% 22.1% 22.0% 22.0% 22.0%

2012 21.9% 21.9% 33.7% 21.8% 21.6% 21.4% 21.3% 21.2% 21.0% 20.8% 20.6% 20.5%

2013 20.3% 20.2% 20.1% 21.1% 20.2% 20.2% 20.1% 20.0% 19.9% 19.8% 19.7% 19.6%

Note. Adapted from AHCA Monthly Enrollment Reports.

Table G2: Medicaid Reform Market Share, Duval County, Plans Existent in both 2007 and 2013

Duval County Plans

Type Year Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

United Healthcare

H M O

2007 10.8% 11.0% 10.6% 10.7% 10.8% 10.9% 11.2% 11.4% 4.5% 4.6% 4.7% 4.9%

2011 2.5% 2.4% 2.2% 2.1% 2.0% 1.9% 1.9% 1.8% 1.8% 1.8% 2.6% 3.3%

2012 3.8% 4.2% 4.0% 3.8% 3.6% 3.4% 3.3% 3.1% 3.0% 2.8% 2.6% 2.6%

2013 2.5% 2.5% 2.5% 2.6% 2.5% 2.5% 2.8% 2.9% 3.2% 3.4% 3.5% 3.7%

First Coast Advantage

P S N

2007 27.7% 24.4% 21.9% 21.9% 22.1% 21.7% 21.9% 21.9% 8.6% 8.3% 8.2% 7.8%

2011 45.4% 45.6% 46.3% 46.9% 47.2% 48.0% 48.5% 49.0% 49.4% 49.7% 49.7% 49.4%

2012 49.2% 49.1% 49.4% 49.7% 49.9% 50.3% 50.5% 50.8% 50.8% 50.9% 50.9% 50.8%

2013 50.7% 49.8% 49.6% 52.6% 49.3% 49.1% 49.5% 49.8% 50.0% 50.0% 49.9% 49.0%

Note. Adapted from AHCA Monthly Enrollment Reports.

Table G3: Medicaid Reform Market Share, Baker County, Plans Existent in both 2007 and 2013

Baker County

Plan

Type Year Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

United Health care

HMO 2007 n/a n/a n/a n/a n/a n/a n/a n/a 0.1% 0.2% 0.2% 0.3%

2011 28.2% 31.3% 31.0% 31.0% 29.9% 30.5% 30.2% 30.3% 30.2% 30.1% 29.5% 28.6%

2012 29.1% 28.0% 27.8% 27.5% 26.0% 25.0% 23.6% 22.2% 21.0% 20.0% 19.0% 18.2%

2013 17.7% 17.5% 17.5% 17.8% 18.5% 18.8% 19.2% 19.3% 19.4% 20.1% 20.7% 21.2%

Note. Adapted from AHCA Monthly Enrollment Reports.

260

Table G4: Medicaid Reform Market Share, Clay County, Plans Existent in both 2007 and 2013 Clay

County Plans

Type Year Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

United Health care

H M O

2007 n/a n/a n/a n/a n/a n/a n/a n/a 0.3% 0.8% 1.0% 1.1%

2011 25.2% 26.9% 27.6% 28.1% 28.2% 26.3% 24.9% 23.6% 21.9% 20.9% 19.7% 17.9%

2012 16.8% 16.0% 15.0% 13.9% 14.2% 15.7% 17.2% 18.5% 20.0% 21.4% 21.9% 22.1%

2013 21.9% 21.8% 21.7% 21.5% 21.6% 21.6% 21.5% 21.4% 21.1% 20.8% 20.9% 21.2%

Note. Adapted from AHCA Monthly Enrollment Reports.

Table G5: Medicaid Reform Market Share, Nassau County, Plan Existent in both 2007 and 2013 Nassau County Plans

Type Year Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

United Health care

H M O

2007 n/a n/a n/a n/a n/a n/a n/a n/a 0.1% 0.3% 0.3% 0.4%

2011 30.7% 36.0% 36.7% 36.0% 35.2% 34.5% 34.4% 33.2% 31.6% 31.1% 30.7% 29.8%

2012 29.7% 29.0% 28.6% 28.2% 27.9% 27.3% 26.9% 26.4% 26.2% 26.6% 26.3% 26.0%

2013 25.8% 25.9% 26.0% 26.0% 25.9% 25.7% 25.5% 24.8% 24.5% 23.3% 23.4% 23.7%

Note. Adapted from AHCA Monthly Enrollment Reports.

261

Table G6: Changes in Copays, Plans Operational from 2007 – 2013 Table G6: Changes in Copays, Plans Operational from 2007 – 2013 (Continued)

Standard Services

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Children’s Medical Services – Broward

P S N

2007 SSI 0

2011 SSI 0

2012 SSI 0

2013 SSI 0

Difference 0

2007 TANF 0

2011 TANF 0

2012 TANF 0

2013 TANF 0

Difference 0

First Coast Advantage – Duval

P S N

2007 SSI 0

2011 SSI 0

2012 SSI 0

2013 SSI 0

Difference 0

2007 TANF 0

2011 TANF 0

2012 TANF 0

2013 TANF 0

Difference 0

Humana Family - Broward

HMO

2007 SSI 1 1

2011 SSI 1 1 1 3

2012 SSI 1 1 1 3

2013 SSI 1 1 1 3

Difference 1 1 +2

2007 TANF 1 1 1 1 1 1 1 7

2011 TANF 1 1 1 1 1 1 6

2012 TANF 1 1 1 1 1 1 6

2013 TANF 1 1 1 1 1 1 6

Difference 1 1 -1 -1 -1 1 -1 -1

South Florida Community Care Network – Broward

P S N

2007 SSI 1 1 1 1 1 1 1 1 1 1 1 1 1 13

2011 SSI 1 1 1 1 1 1 1 1 1 1 1 1 1 13

2012 SSI 1 1 1 1 1 1 1 1 1 1 1 1 1 13

2013 SSI 1 1 1 1 1 1 1 1 1 1 1 1 1 13

Difference 0

2007 TANF 1 1 1 1 1 1 1 1 1 1 1 1 1 13

2011 TANF

1 1 1 1 1 1 1 1 1 1 1 1 1 13

2012 TANF

1 1 1 1 1 1 1 1 1 1 1 1 1 13

2013 TANF 1 1 1 1 1 1 1 1 1 1 1 1 1 13

Difference 0

United Healthcare – Baker, Clay, Nassau, &

HMO

2007 SSI 0

2011 SSI 1 1 1 1 1 1 1 1 1 1 10

2012 SSI 1 1 1 1 1 1 1 1 1 1 10

2013 SSI 1 1 1 1 1 1 1 1 1 1 10

Difference 1 1 1 1 1 1 1 1 1 1 +10

262

Table G6: Changes in Copays, Plans Operational from 2007 – 2013 (Continued) Standard Services

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Duval 2007 TANF 0

2011 TANF 1 1 1 1 1 1 1 1 1 9

2012 TANF 1 1 1 1 1 1 1 1 1 9

2013 TANF 1 1 1 1 1 1 1 1 1 9

Difference 1 1 1 1 1 1 1 1 1 +9

Note. SSI – Aged and/or Disabled, TANF – Children and Families. Adapted from health plan comparison charts from October

2007, April 2011, April 2012, and April 2013.

263

Table G7: Differences in Plan Limits for Plans Operational from 2007 to 2013 Table G7: Differences in Plan Limits for Plans Operational from 2007 to 2013 (Continued)

Plan Limits

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tris

t

Pri

mar

y C

are

Ph

ysic

ian

/AR

NP

/PA

Spe

cial

ty P

hys

icia

n

Tran

spla

nt

Serv

ice

s

Tran

spo

rtat

ion

(n

on

-em

erg

ency

)

Vis

ion

Se

rvic

es

Tota

l Ch

ange

s in

Lim

its

Children’s Medical Services – Broward

PSN (2011)

SSI 0

TANF

0

PSN (2012)

SSI 0

TANF

0

PSN (2013)

SSI 0

TANF

0

First Coast Advantage – Duval

PSN (2011)

SSI 1 1 2

TANF

1 1 1 3

PSN (2012)

SSI 1 1 2

TANF

1 1 1 3

PSN (2013)

SSI 1 1 1 3

TANF

1 1 1 1 4

Humana Family – Broward

HMO (2011)

SSI 1 1 1 3

TANF

1 1 1 3

HMO (2012)

SSI 1 1 2

TANF

1 1 1 3

HMO (2013)

SSI 1 1 1 3

TANF

1 1 1 3

264

Table G7: Differences in Plan Limits for Plans Operational from 2007 to 2013 (Continued)

Plan Limits

Pla

n N

ame

Pla

n T

ype

Cat

ego

ry

Am

bu

lan

ce

Am

bu

lato

ry S

urg

ery

Ch

em

oth

era

py

Serv

ice

s

Ch

iro

pra

cto

r

Clin

ic (

FQH

C/R

HC

)

De

nta

l Se

rvic

es

Dia

lysi

s Se

rvic

es

Du

rab

le M

ed

ical

Eq

uip

me

nt

Eme

rge

ncy

Ro

om

He

arin

g Se

rvic

es

Ho

me

He

alth

Se

rvic

es

Ho

spit

al In

pat

ien

t

Ho

spit

al O

utp

atie

nt

Surg

ery

Lab

/X-r

ay

Mat

ern

ity/

Fam

ily P

lan

nin

g

Serv

ice

s

Me

nta

l He

alth

Se

rvic

es

Ou

tpat

ien

t H

osp

ital

Se

rvic

es

(no

n-e

me

rge

ncy

)

Ou

tpat

ien

t Th

era

py

(ph

ysic

al/r

esp

irat

ory

)

Ph

arm

acy

Po

dia

tris

t

Pri

mar

y C

are

Ph

ysic

ian

/AR

NP

/PA

Spe

cial

ty P

hys

icia

n

Tran

spla

nt

Serv

ice

s

Tran

spo

rtat

ion

(n

on

-em

erg

ency

)

Vis

ion

Se

rvic

es

Tota

l Ch

ange

s in

Lim

its

South Florida Community Care Network – Broward

PSN (2011)

SSI 1 1 1 3

TANF

1 1 2

PSN (2012)

SSI 1 1 1 3

TANF

1 1 2

PSN (2013)

SSI 1 1 1 3

TANF

1 1 2

United Healthcare – Baker, Clay, &Nassau

HMO (2011)

SSI 1 1 1 1 1 1 1 1 8

TANF

1 1 1 1 1 1 1 1 8

HMO (2012)

SSI 1 1 1 1 1 1 1 1 8

TANF

1 1 1 1 1 1 1 1 8

HMO (2013)

SSI 1 1 1 1 1 1 1 1 1 1 10

TANF

1 1 1 1 1 1 1 1 1 1 10

United Healthcare – Duval

HMO (2011)

SSI 1 1 1 1 1 1 1 1 8

TANF

1 1 1 1 1 1 1 1 8

HMO (2012)

SSI 1 1 1 1 1 1 1 1 1 1 10

TANF

1 1 1 1 1 1 1 1 1 1 10

HMO (2013)

SSI 1 1 1 1 1 1 1 1 1 1 10

TANF

1 1 1 1 1 1 1 1 1 1 10

265

Table G8 outlines the changes in expanded benefits for each of the five health plans that were in operation

between 2007 and 2013. A “1” indicates that the plan offered the expanded benefit. If the plan did not offer the

benefit, the cell was left blank.

Table G8: Changes in Expanded Benefits, 2007 vs. 2013

Table G8: Changes in Expanded Benefits, 2007 vs. 2013 (Continued)

Exp

and

ed

Be

n

efi

t

s

Pla

n N

ame

Pla

n T

ype

Cat

ego

ry

Ove

r th

e C

ou

nte

r P

har

mac

y

Cir

cum

cisi

on

Ad

ult

De

nta

l

Ad

ult

Vis

ion

Ad

ult

He

arin

g

Ad

ult

Nu

trit

ion

Th

era

py

Acu

pu

nct

ure

He

alth

& W

elln

ess

Be

ne

fit

Re

spit

e C

are

/Ho

me

H

eal

th S

erv

ice

s

Me

als

on

Wh

ee

ls

Frai

l & E

lde

r

Mat

ern

ity

Ad

ult

Ho

spit

al

Inp

atie

nt

Ad

ult

Ou

tpat

ien

t

Sum

mar

y o

f Ex

pan

de

d B

en

efi

ts

Children’s Medical Services – Broward

PSN 2007 SSI 0

2011 SSI 0

2012 SSI 0

2013 SSI 0

Difference 0

2007 TANF

0

2011 TANF

0

2012 TANF

0

2013 TANF

0

Difference 0

First Coast Advantage – Duval

PSN 2007 SSI 1 1 1 3

2011 SSI 1 1

2012 SSI 1 1

2013 SSI 1 1

Difference -1 -1 -2

2007 TANF

1 1

2011 TANF

1 1

2012 TANF

1 1

2013 TANF

1 1

Difference 0

Humana Family – Broward

HMO 2007 SSI 1 1

2011 SSI 0 0

2012 SSI 0 0

2013 SSI 1 1

Difference -1 1 0

2007 TANF

1 1

2011 TANF

0 0

266

Table G8: Changes in Expanded Benefits, 2007 vs. 2013 (Continued)

Exp

and

ed

Be

n

efi

t

s

Pla

n N

ame

Pla

n T

ype

Cat

ego

ry

Ove

r th

e C

ou

nte

r P

har

mac

y

Cir

cum

cisi

on

Ad

ult

De

nta

l

Ad

ult

Vis

ion

Ad

ult

He

arin

g

Ad

ult

Nu

trit

ion

Th

era

py

Acu

pu

nct

ure

He

alth

& W

elln

ess

Be

ne

fit

Re

spit

e C

are

/Ho

me

H

eal

th S

erv

ice

s

Me

als

on

Wh

ee

ls

Frai

l & E

lde

r

Mat

ern

ity

Ad

ult

Ho

spit

al

Inp

atie

nt

Ad

ult

Ou

tpat

ien

t

Sum

mar

y o

f Ex

pan

de

d B

en

efi

ts

2012 TANF

0 0

2013 TANF

1 1

Difference -1 1 0

South Florida Community Care Network – Broward

PSN 2007 SSI 1 1 2

2011 SSI 1 0 1

2012 SSI 1 0 1

2013 SSI 1 1 1 3

Difference 1 1

2007 TANF

1 1 2

2011 TANF

1 1 2

2012 TANF

1 1 2

2013 TANF

1 1 1 3

Difference 1 1

United Healthcare – Baker, Clay, Nassau, & Duval

HMO 2007 SSI 1 1 1 3

2011 SSI 1 0 0 1

2012 SSI 1 0 0 1

2013 SSI 1 1

Difference -1 -1 -2

2007 TANF

1 1 1 3

2011 TANF

1 1 0 2

2012 TANF

1 1 0 2

2013 TANF

1 1 2

Difference -1 -1

Note. SSI – Aged and/or Disabled, TANF – Children and Families. Adapted from health plan comparison charts from October 2007, April 2011, April 2012, and April 2013.

267

Appendices: Domain iii

Appendix H: EBA Changes Over Time

The Enhanced Benefits Reward$ Program, also known as the Enhanced Benefits Account (EBA) Program, saw some changes between the years 2006, 2009, and 2011, but overall remained relatively stable in the procedures that were eligible for rewards, the number of times an individual could earn a credit for engaging in a behavior, and the credit amount for each procedure. Between 2006 and 2009, four eligible procedures were added: diabetes management, prostate specific antigen screening, healthy start screening, and participating in a behavior change smoking program. There were five instances of change in the credit amounts for a procedure and two changes in the number of occurrences allowed for a procedure. Office visit credit amount decreased from $15 to $7.50 and the number of allowed occurrences decreased from two to one. Preventive care delineated between credit amounts for children and adults, allowing $25 in credit for children but no credit for adults in 2009. However, the amount credited for child and adult preventive services increased for adults, from no credit in 2006 to $15 in 2009; credit given for children was $25 in both years. Adult preventive care procedures increased the credit amount from no credit in 2006 to $15 in 2009, but decreased the number of allowed occurrences from five in 2006 to two in 2009. The credit amount for dental cleaning was also changed from $25 for both children and adults in 2006 to $15 for adults and $25 for children in 2009. Between 2009 and 2011, two eligible procedures were added and one procedure was removed from the list. Additional eligible procedures included one adult BMI assessment and compliance with prescribed maintenance drugs. Participation in a behavior change smoking program was removed from the list. No other changes in occurrences allowed or credit amounts for procedures were recorded. Table H1: EBA Changes – 2006 vs. 2009 vs. 2011 (Continued)

Procedure Occurrence Limit 2006

Occurrence Limit 2009

Occurrence Limit 2011

Credit Amount 2006

Credit Amount 2009

Credit Amount 2011

Adult BMI Assessment

N/A N/A 1 N/A N/A $25.00 Adult

Adult Preventive Care

5 2 2 $0.00 $15.00 Adult $15.00 Adult

Alcoholic Treatment 6 Months success

2 2 2 $15.00 $15.00 $15.00

Alcoholics Anonymous

1 1 1 $25.00 $25.00 $25.00

Asthma Disease Management Program

1 1 1 $25.00 $25.00 $25.00

Behavior Change Smoking

N/A 1 N/A N/A $25.00 N/A

Child & Adult Preventive Care

5 5 5 $0.00 Adult, $25.00 Child

$15.00 Adult, $25.00 Child

$15.00 Adult, $25.00 Child

Childhood Preventive Care

5 5 5 $25.00 $25.00 Child $25.00 Child

Colorectal Screening 1 1 1 $25.00 $25.00 $25.00

Compliance with Prescribed Maintenance Drugs

N/A N/A 4 N/A N/A $7.50

268

Table H1: EBA Changes – 2006 vs. 2009 vs. 2011 (Continued) Procedure Occurrence

Limit 2006 Occurrence Limit 2009

Occurrence Limit 2011

Credit Amount 2006

Credit Amount 2009

Credit Amount 2011

Congestive Heart Failure Disease Management Program

1 1 1 $25.00 $25.00 $25.00

Dental Cleaning –Adult/Child

1 1 1 $25.00 $15.00 Adult, $25.00 Child

$15.00 Adult, $25.00 Child

Dental Preventive Services – Adult/Child

2 2 2 $15.00 Adult, $25.00 Child

$15.00 Adult, $25.00 Child

$15.00 Adult, $25.00 Child

Diabetes Disease Management Program

1 1 1 $25.00 $25.00 $25.00

Diabetes Management

N/A 1 1 N/A $15.00 $15.00

Exercise Program 1 1 1 $25.00 $25.00 $25.00

Exercise Program 6 Months success

2 2 2 $15.00 $15.00 $15.00

Flu Shot 1 1 1 $25.00 $25.00 $25.00

Healthy Start Screening with Modifier TG

N/A 1 1 N/A $15.00 $15.00

HIV/AIDS Disease Management Program

1 1 1 $25.00 $25.00 $25.00

Hypertension Disease Management Program

1 1 1 $25.00 $25.00 $25.00

Mammogram 1 1 1 $25.00 $25.00 $25.00

Narcotics Anonymous

1 1 1 $25.00 $25.00 $25.00

Narcotics Treatment 6 Months success

2 2 2 $15.00 $15.00 $15.00

Office Visit – Adult/Child

2 1 1 $15.00 $7.50 $7.50

Other Disease Management Program

1 1 1 $25.00 $25.00 $25.00

Pap Smear 1 1 1 $25.00 $25.00 $25.00

Prostate Specific Antigen

N/A 1 1 N/A $15.00 Adult $15.00 Adult

Smoking Cessation 1 1 1 $25.00 $25.00 $25.00

Smoking Cessation 6 Months success

2 2 2 $15.00 $15.00 $15.00

Vision Exam – Adult/Child

1 1 1 $25.00 $25.00 $25.00

Weight Management

1 1 1 $25.00 $25.00 $25.00

Weight Management 6 Months success

2 2 2 $15.00 $15.00 $15.00

269

Appendix I: Description of Preventive Service Category

Table I1: Description of Preventive Service Category Table I1: Description of Preventive Service Category (Continued)

Service Category CPT Code Description

Childhood Preventive Care 99381 Initial comprehensive preventive medicine evaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, new patient; infant (age younger than 1 year)

Childhood Preventive Care 99382 Initial comprehensive preventive medicine evaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, new patient; early childhood (age 1 through 4 years)

Childhood Preventive Care 99383 Initial comprehensive preventive medicine evaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, new patient; late childhood (age 5 through 11 years)

Childhood Preventive Care 99384 Initial comprehensive preventive medicine evaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, new patient; adolescent (age 12 through 17 years)

Childhood Preventive Care 99391 Periodic comprehensive preventive medicine reevaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, established patient; infant (age younger than 1 year)

Childhood Preventive Care 99392 Periodic comprehensive preventive medicine reevaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, established patient; early childhood (age 1 through 4 years)

Childhood Preventive Care 99393 Periodic comprehensive preventive medicine reevaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, established patient; late childhood (age 5 through 11 years)

Childhood Preventive Care 99394 Periodic comprehensive preventive medicine reevaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of

270

Table I1: Description of Preventive Service Category (Continued)

Service Category CPT Code Description

laboratory/diagnostic procedures, established patient; adolescent (age 12 through 17 years)

Childhood Preventive Care 99431 No longer in use (newborn care services)

Childhood Preventive Care 99432 No longer in use (newborn care services)

Childhood Preventive Care 99435 No longer in use (newborn care services)

Childhood Preventive Care 99460 Initial hospital or birthing center care, per day, for evaluation and management of normal newborn infant

Childhood Preventive Care 99463 Initial hospital or birthing center care, per day, for evaluation and management of normal newborn infant admitted and discharged on the same date

Childhood Preventive Care 99461 Initial care, per day, for evaluation and management of normal newborn infant seen in other than hospital or birthing center

Childhood Preventive Care V20.2 Routine infant or child health check

Childhood Preventive Care V20.3 Newborn health supervision

Childhood Preventive Care V70.0 Routine general medical examination at a health care facility

Childhood Preventive Care V70.3 Other general medical examination for administrative purposes

Childhood Preventive Care V70.5 Health examination of defined subpopulations

Childhood Preventive Care V70.6 Health examination in population surveys

Childhood Preventive Care V70.8 Other specified general medical examinations

Childhood Preventive Care V70.9 Unspecified general medical examination

Child & Adult Preventive Care 99385 Initial comprehensive preventive medicine evaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, new patient; 18–39 years

Child & Adult Preventive Care 99395 Periodic comprehensive preventive medicine reevaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, established patient; 18–39 years

Child & Adult Preventive Care 99403 Preventive medicine counseling and/or risk factor reduction intervention(s) provided to an individual (separate procedure); approximately 45 minutes

Adult Preventive Care 99386 Initial comprehensive preventive medicine evaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, new patient; 40–64 years

Adult Preventive Care 99387 Initial comprehensive preventive medicine evaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, new patient; 65 years and older

271

Table I1: Description of Preventive Service Category (Continued)

Service Category CPT Code Description

Adult Preventive Care 99396 Periodic comprehensive preventive medicine reevaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, established patient; 40–64 years

Adult Preventive Care 99397 Periodic comprehensive preventive medicine reevaluation and management of an individual including an age and gender appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures, established patient; 65 years and older

272

Appendix: Domain iv

Appendix J: MED150: Florida International University, Evaluation of the Florida Medicaid Reform Demonstration to Deter Fraud and Abuse, Final Report, Year 1 and Year 2 Evaluation Findings

Appendix J is the complete final report of the Year 1 and Year 2 Domain iv evaluation findings submitted by FIU to AHCA (AHCA Contract MED150). The MRE Team at UF collaborated with FIU to utilize specific portions of the report into this document.

273

EVALUATION OF THE FLORIDA MEDICAID

REFORM DEMONSTRATION TO DETER

FRAUD AND ABUSE

FINAL REPORT YEAR 1 AND YEAR 2

EVALUATION FINDINGS

State of Florida

Agency for Health Care Administration

Contract No. MED150

Project title: Evaluation of the Florida Medicaid Reform Demonstration to Deter Fraud and Abuse Evaluating Deterrence of Medicaid Fraud and Abuse Project purpose: The purpose of this project is to measure the effectiveness of Florida’s Medicaid Reform Demonstration program in serving as a deterrent against fraud and abuse and in maintaining oversight of the managed care plan policies and procedures that deter fraud and abuse.

Prepared by: Gloria J. Deckard, Ph.D., Florida International University

Gulcin Gumus, Ph.D.

Acknowledgement: The researchers would like to acknowledge the invaluable contribution and support provided by student research assistants Carolina Moscoso and Maximilian Staedtler.

274

Table of Contents

EXECUTIVE SUMMARY ........................................................................................................................................... 1 AIMS AND METHODS 1

KEY FINDINGS 1

INTRODUCTION ..................................................................................................................................................... 3 LITERATURE REVIEW .............................................................................................................................................. 4

DEFINING HEALTH CARE FRAUD AND ABUSE 4

THE IMPACT OF FRAUD AND ABUSE IN HEALTH CARE 7

HISTORY OF NATIONAL FRAUD CONTROL EFFORTS 8

FLORIDA’S EFFORTS TO PREVENT AND DETECT MEDICAID FRAUD AND ABUSE 8

FLORIDA STATUTORY REQUIREMENTS FOR MEDICAID MANAGED CARE PLANS 9

METHODS ............................................................................................................................................................ 10 QUALITATIVE ANALYSES 10

Content Analysis .................................................................................................................................................. 10

Supplemental Structured Interviews .................................................................................................................... 12

QUANTITATIVE ANALYSES 13

Annual Fraud and Abuse Reports (AFAAR) .......................................................................................................... 13

Quarterly Fraud and Abuse Reports (QFAAR) ...................................................................................................... 14

FINDINGS ............................................................................................................................................................. 14 INTEGRATED QUALITATIVE ANALYSES 14

Detection and Prevention Tools ........................................................................................................................... 15

Education and Training ........................................................................................................................................ 17

Internal and External Investigations .................................................................................................................... 18

Internal and External Reporting .......................................................................................................................... 19

Corrective Actions ................................................................................................................................................ 20

Synopsis of Qualitative Findings .......................................................................................................................... 20

QUANTITATIVE ANALYSES – TRENDS FROM FISCAL YEARS 2010-2013 22

Trends in AFAAR Data .......................................................................................................................................... 24

Trends in QFAAR Data ......................................................................................................................................... 46

Synopsis of Quantitative Findings ........................................................................................................................ 25

SUMMARY AND DISCUSSION ............................................................................................................................... 26 REFERENCES ......................................................................................................................................................... 28

MED150 Evaluation of Medicaid Reform to Deter Fraud and Abuse i

275

Executive Summary

Florida’s Medicaid Reform is a comprehensive demonstration program that seeks to improve the value of the Medicaid delivery system. The program, operated under an 1115 Research and Demonstration Waiver initially approved by the Centers for Medicare and Medicaid Services (CMS) on October 19, 2005, requires most Medicaid recipients to enroll in a managed care plan as a condition for receiving Medicaid. One of the objectives of the Demonstration Waiver is to serve as an effective deterrent against fraud and abuse by moving from a fee-for-service to a managed care delivery system. In conjunction with this objective, the State engaged a research team from Florida International University (FIU) in Miami, Florida to perform this evaluation and seek to gauge the program’s effectiveness as a deterrent against fraud and abuse.

Aims and Methods

This evaluation focused on two specific objectives of the demonstration program: 1) To serve as an effective deterrent against fraud and abuse by moving from a fee-for-service to a managed care delivery system; and 2) To maintain strict oversight of managed care plans including adapting efforts to the surveillance of fraud and abuse within the managed care system.

The evaluation included both qualitative and quantitative analysis of data from a sample of the Medicaid managed care plans in the demonstration program. The qualitative study included: 1) content analysis of the fraud and abuse plans submitted to the Bureau of Medicaid Program Integrity (Bureau of MPI) by the managed care plans and 2) analyses and integration of supplemental interview data obtained through phone interviews with Compliance Officers from the managed care plans. The quantitative analyses utilized secondary data for the sample of managed care plans obtained through the 1) Annual Anti-Fraud and Abuse Activity Report (AFAAR) and 2) the Quarterly Anti-Fraud and Abuse Activity Report (QFAAR) that are required from each managed care plan by the Bureau of MPI.

Key Findings

The Agency maintains regulatory control and oversight of Medicaid managed care plans and the anti-fraud policies and procedures utilized by these plans in the deterrence, detection and surveillance of fraud and abuse within the managed care system. The specific research questions associated with these efforts and the responses found through the qualitative analyses include:

What are the program integrity-related measures employed by the health plans in the Demonstration related to: deterring fraud and abuse by network and non-network providers; deterring fraud and abuse by recipients; detecting fraud and abuse by network and non-network providers; and detecting fraud and abuse by recipients?

The Agency requires Quarterly Anti-Fraud and Abuse Reports which include information on the specific detection tools utilized for each suspected fraud and abuse incident.

The Agency requires an Annual Fraud and Abuse Report which include investigations of potentially fraudulent or abusive acts during the prior fiscal year. These reports must include, at a minimum: The dollar amount of health plan losses and recoveries attributable to overpayment, abuse and fraud; and the number of health plan referrals to the Bureau of MPI.

MED150 Evaluation of Medicaid Reform to Deter Fraud and Abuse 1

276

Within the managed care programs, a variety of internal policies and procedures were identified. Overall, there was general agreement that data analysis and the use of pre-payment and post-payment fraud detection tools were the most effective methods for both prevention and detection. The use of fraud detection software is increasing both within plans and by the Agency itself.

How often do health plan compliance officers/teams interact with providers in the health plan networks? What types of contact and interactions do the compliance officers/teams have with providers? How do plans document and track their efforts to deter fraud and abuse?

The frequency and the nature of interactions between managed care health plan compliance officers/teams and providers focused primarily on targeted educational sessions and communication of updated policies and procedure rather than being restricted to specific scheduled times. The communication frequency also depended on the needs of the providers – compliance officers indicated continuous and immediate availability to providers as needed and/or requested.

Various methods of communication between compliance officers and providers were utilized depending on the type and urgency of the issues. Communication between parties occurred by mailed correspondence, such as newsletters, as well as by phone and email.

Procedures for tracking and documenting of efforts above and beyond those required by the Agency to deter fraud and abuse vary across managed care plans. While some use manual systems, others utilize computer software programs. The use of data analysis is an increasingly important tool in detection and deterrence of fraud and abuse.

How do health plan compliance officers/teams measure the effectiveness of the health plan policies and procedures related to program integrity?

A variety of techniques and measures were identified by the compliance officers as means to gauge the effectiveness including: tracking soft dollar savings, periodic reviews and updates to their policies in response to the previous year’s activities, monitoring volume of cases, time constraints on cases, amounts recovered from providers, internal audit outcomes, and monthly audits of their investigators.

While the techniques for measuring the effectiveness of policies and procedures varied amongst plans, there was general agreement that data analysis and the use of pre-payment and post-payment anti-fraud tools were the most effective methods for both prevention and detection.

General trends emerged from the quantitative analyses that provide rudimentary indication that the managed care plans in the sample are increasingly effective in deterring fraud and abuse. Trends examined in the amount of reported overpayments and reported fraud and abuse by the Medicaid managed care plans included in the sample indicate overall that these numbers have decreased during the FYs included in the analyses. Accompanying these decreases, is a general increase in the proportion of cases identified in Reform contract types through the utilization of detection tools incorporating data analytical techniques such as data mining, data analytics, data matching, pre- and post-payment reviews, and the utilization of specialized fraud and abuse software. While a causal relationship cannot be established due to the multiplicity of factors that may impact this change, indications are clear that plans identified as Reform contract types are demonstrating increased use of data analytical tools over time and are increasingly effective in detecting or deferring fraud and abuse within the Medicaid

MED150 Evaluation of Medicaid Reform to Deter Fraud and Abuse 2

277

demonstration program.

Introduction

Florida’s Medicaid Reform program is a comprehensive demonstration that seeks to improve the value of the Medicaid delivery system. The program is operated under an 1115 Research and Demonstration Waiver initially approved by the Centers for Medicare and Medicaid Services (CMS) on October 19, 2005. State authority to operate the program is located in section 409.91211, Florida Statutes (F.S.), which authorizes a statewide pilot program that began in Broward and Duval counties on July 1, 2006. The program expanded to Baker, Clay, and Nassau counties on July 1, 2007 (Agency for Health Care Administration [AHCA] n.d., a).

On June 30, 2010, the Agency for Health Care Administration (the Agency) submitted a 3-year waiver extension request to maintain and continue operations for the period July 1, 2011 through June 30, 2014. CMS approved the 3-year waiver extension request on December 15, 2011. The waiver extension period was December 16, 2011 through June 30, 2014. Under the Medicaid Reform Demonstration, most Medicaid recipients are required to enroll in a managed care plan as a condition for receiving Medicaid. Participation is mandatory for TANF-related populations, i.e., those receiving Temporary Assistance for Needy Families (TANF) and the aged and disabled with some exceptions. The Demonstration allows health plans to offer customized benefit packages and to reduce cost sharing, although each plan is required to cover all mandatory services and all State plan services for children and pregnant women (AHCA, n.d., a).

One of the objectives under the Demonstration Waiver is to “serve as an effective deterrent against fraud and abuse by moving from a fee-for-service to a managed care delivery system.” In conjunction with this objective, the State must evaluate the impact of the demonstration program as a deterrent against Medicaid fraud and abuse (CMS, 2011a). To this end, the State engaged a research team from Florida International University (FIU) in Miami, Florida to perform this evaluation and seek to gauge the program’s effectiveness as a deterrent against fraud and abuse. The evaluation focused on two specific objectives of the demonstration program: 1) To serve as an effective deterrent against fraud and abuse by moving from a fee-for-service to a managed care delivery system; and 2) To maintain strict oversight of managed care plans including adapting efforts to the surveillance of fraud and abuse within the managed care system.

The evaluation utilized a mixed-methods approach (Driscoll, Appiah-Yeboah, Salib and Rupert, 2007; Lieber, 2009), that is, the design and analysis include both qualitative and quantitative methods. The qualitative approach focused on a systematic content analysis of the anti-fraud, waste and abuse policies and procedures from a sample of nine Medicaid managed care plans. This qualitative data was expanded and supplemented by voluntary interviews with Compliance Officers from 11 managed care plans to further explore perceptions of the effectiveness of varying strategies to prevent and detect managed care fraud and abuse. The quantitative analyses focused on secondary data from the annual and quarterly fraud and abuse reports that each managed care plan must submit to the Bureau of Medicaid Program Integrity (Bureau of MPI).

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This report first presents a literature review to frame the issues which impact health care fraud and abuse at both the national and state levels. The integrative qualitative analyses then investigate the oversight and surveillance of fraud and abuse as well as the commonalities and differences in the fraud and abuse plan policies and procedures across a sample of the managed care plans participating in the demonstration program. The quantitative analyses provide an examination of the trends in reported fraud and abuse in the mandatory reports submitted to the Bureau of MPI from managed care plans during the period of 2010 – 2013. The report’s summary explores the implications of these findings in concert with the evaluation objectives as well as the ongoing efforts of the State of Florida to prevent and detect fraud and abuse as managed care expands throughout the State of Florida.

Literature Review

Defining Health Care Fraud and Abuse

Legal definitions of fraud generally incorporate five major elements (Busch, 2012):

Misrepresentation of a material fact;

Knowledge of the falsity of the misrepresentation or ignorance of its truth;

Intent to deceive another;

A victim acting on the misrepresentation; and

Damage to the victim.

Health care fraud is defined by similar elements. CMS (2012) defines fraud as “making false statements or representations of material facts to obtain some benefit or payment for which no entitlement would otherwise exist. These acts may be committed either for the person’s own benefit or for the benefit of some other party. In other words, fraud includes the obtaining of something of value through misrepresentation or concealment of material facts”. Florida Statute defines fraud (s. 409.913) as “an intentional deception or misrepresentation made by a person with the knowledge that the deception results in unauthorized benefit to herself or himself or another person”. The term includes any act that constitutes fraud under applicable federal or state law.

Abuse, on the other hand, is defined as incidents or practices that are not consistent with generally accepted practices and lead to unnecessary costs, improper payments, unnecessary services, substandard quality of care or failure to meet coverage requirements (Busch, 2012). Florida Statute’s definition of abuse (s.409.913) is as follows: “1. Provider practices that are inconsistent with generally accepted business or medical practices and that result in an unnecessary cost to the Medicaid program or in reimbursement for goods or services that are not medically necessary or that fail to meet professionally recognized standards for health care. 2. Recipient practices that result in unnecessary cost to the Medicaid program.” Overpayment, in the context of the Medicaid program (s. 409.913 F.S.), is defined as “any amount that is not authorized to be paid by the Medicaid program whether paid as a result of inaccurate of improper cost reporting, improper claiming, unacceptable practices, fraud, abuse, or mistake.”

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Fraud and abuse against the Medicaid program can take various forms. According to the National Association of Medicaid Fraud Control Units (NAMFCU, 2012), there are ten typical schemes of Medicaid fraud by providers:

Billing for services not provided

Double billing

Billing for phantom visits

Billing for more hours than there are in a day

Falsifying credentials

Substitution of generic drugs

Billing for unnecessary services or tests

Billing for more expensive procedures than were performed

Kickbacks

False cost reports

Even though the existing literature focuses heavily on providers, fraud and abuse also can be committed by suppliers/subcontractors, insured patients, plan personnel, other individuals or organized entities. Rashidian, Joudaki and Vian (2012) categorize health care fraud into three categories: provider fraud, consumer fraud (patient or insured) and insurer or payer fraud. Given the complex nature of the health care industry, measures and tools to detect Medicaid fraud should address preventing, detecting, monitoring, auditing, and investigating fraudulent activities by all agents.

AHCA’s Managed Care Fraud and Abuse Subcommittee has compiled a more complete list of managed care fraud and abuse schemes based on national guidelines (CMS, 2000; 2002) as follows:

Falsification of financial solvency

Falsified or inadequate provider network

Fraudulent subcontract

Fraudulent subcontractor

Bid-rigging or self-dealing

Collusion among providers

Contracts with related parties

Illegal tying agreements

Provider subcontract level: misleading providers re: fees paid, services allowed, number of enrollees, etc.

Marketing and Enrollment Fraud/Abuse

Misrepresentation of covered benefits to enrollees

Discouraging enrollment by elderly or chronically ill

Offering incentives (money, items) to attract enrollees

Colluding with providers to attract healthy enrollees

Enrollment/Disenrollment Fraud

Enrolling ineligible individuals

Enrolling nonexistent individuals

Enrolling nonexistent or ineligible family members

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"Cherry-Picking" or selecting the healthiest segment of the enrollment population

Kickbacks for referrals

Dis-enrolling undesirable members (“Lemon-Dropping”)

Failing to notify state of deceased or incarcerated members, members admitted to hospice, other ineligible populations

Underutilization

Untimely first contact with clients

Untimely assignment of PCP

Delay in reassigning PCP upon an individual's request

Discouragement of treatment using geographic or time barriers

Engagement in any federally-prohibited discrimination activities

Failure to serve individuals with cultural or language barriers

Failure to provide educational services

Failure to provide outreach/follow-up care or federally-required referrals

Failure to provide court-ordered treatment

Failure to provide managed care beneficiaries comparable services such as those provided to commercial or fee-for-service beneficiaries

Defining "appropriateness of care" and/or "experimental procedures" in a manner inconsistent with standards of care

Slow or nonexistent drug formulary updates

Strict utilization review (UR) standards

Cumbersome appeal process for enrollees

Ineffective grievance process

Inadequate prior authorization "hotline"

Unreasonable prior authorization requirements

Cumbersome appeal process for providers

Delay or failure of the PCP to perform necessary referrals for additional care

"Gag Orders"

Incentives to PCPs and specialty providers to illegally limit services or referral

Routine denial of claims

Encounter Data/Financial Reporting

Inaccurate or false encounter data (plan or provider level)

Misrepresenting/falsifying cost data (e.g., medical/loss ratio, inflating IBNR – incurred but not reported)

Claims Submission and Billing Procedures

Balance billing

Inflating bills for services and/or goods provided

Double-billing

Improper coding (up coding and unbundling)

Billing for ineligible consumers or services never rendered

Inappropriate physician incentive plans

Reporting phantom patient visits and improper cost reporting

Inappropriate cost-shifting to carved-out services

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Beneficiary fraud and abuse

Fee-for-Service Fraud in Managed Care

Billing for unnecessary services or overutilization

Ghost billing or billing for services not provided

Embezzlement, Theft, and Related Fee-for-Service Fraud

Diversion of funds for medical service to unnecessary administrative costs

"Bust Outs" - premiums are paid to the MCO, but MCO avoids paying vendors/providers by deliberately declaring bankruptcy

The listing provided above demonstrates the types of managed care fraud and abuse activities that can waste limited resources and threaten the fiscal integrity of public health care programs. They may also result in higher prices of health services for all consumers (Morris, 2009). The impact of fraud and abuse on beneficiaries, taxpayers, and the health care system calls for proactive detection and deterrence strategies.

The Impact of Fraud and Abuse in Health Care

There are many credible sources that estimate the national loss due to health care fraud to be tens of billions (e.g., Federal Bureau of Investigation [FBI], 2010 and National Health Care Anti-Fraud Association [NHCAA], 2013a); however, the full extent cannot be measured precisely (Morris, 2009). Fraud drains resources from the health care system, drives up costs for everyone, and threatens the solvency of critical public programs (CMS, 2011b; Morris, 2009). Based upon the national pervasiveness of Medicaid fraud, it is no surprise that the State of Florida would also suffer from such losses. Recent convictions or awards of restitution in South Florida alone have demonstrated the State’s commitment to prosecute and recover losses due to criminal rings fabricating claims from non-existent clinics using genuine patient-insurance and provider-billing information (NHCAA, 2013b). While arguably the most visible examples of Medicaid fraud and abuse in the State may arise in South Florida, egregious practices are being identified and criminally investigated throughout the state.

The Fiscal Year (FY) 2011-2012 and 2012-2013 joint annual reports of AHCA and the Medicaid Fraud Control Unit (MFCU) within the Office of the Attorney General demonstrate the extent of fraud and abuse across the State as well as the effectiveness of prevention and recovery efforts. Implementing both prevention and recovery efforts pays off. As presented in the 2011- 2012 report, the MFCU generated approximately $7.39 for every dollar spent resulting in a considerable return on investment (ROI) of 7.39:1 (AHCA, 2012, p. 9). The ROI for recovery efforts by the AHCA Bureau of Medicaid Program Integrity (Bureau of MPI) was 7.9:1 and was 5.3:1 for prevention efforts (p. 43). In the following FY (2012-2013), the MFCU generated approximately $9.63 for every dollar spent (p. 4). The ROI for recovery efforts by the Bureau of MPI for FY 2012-2013 was 7.6:1 and 5.8:1 for prevention efforts (AHCA, 2013, p. 41).

In light of these successful efforts, curbing fraud, abuse, and waste is one of the most promising strategies for states to control Medicaid spending without sacrificing quality. Controlling the costs of delivering Medicaid services is especially critical at a time of increasing program enrollment and further transitioning to managed care plans. As noted by Secretary Dudek, “as Florida transitions to managed care, it is imperative that the realized savings are not reduced by fraud and abuse” (Dudek, 2012).

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History of National Fraud Control Efforts

Health care fraud, particularly in the Medicare and Medicaid programs, has always existed. When Medicare and Medicaid were enacted in 1965, deterring fraud was addressed with only a provision about making of false statements to obtain reimbursement (Goldman, 2011). In 1977, the Medicare-Medicaid Anti-Fraud and Abuse amendments established Medicaid Fraud Control Units (MFCU) that became mandatory for states after 1995. While the MFCUs included attorneys, auditors and investigators, these individuals worked separately in each state to investigate and prosecute Medicaid fraud (Goldman, 2011).

Collaborative efforts to fight health care fraud through federal, state and local partnerships were first established by the Health Insurance Portability and Accountability Act (HIPAA) of 1996 (Morris, 2009). Passage of HIPAA federalized the crime of health care fraud and created the national Health Care Fraud and Abuse Program (HCFAC) to coordinate federal, state and local law enforcement efforts against health care fraud and abuse. The Deficit Reduction Act of 2005 established the federal Bureau of Medicaid Integrity Program (MIP) as the first federal program to combat fraud and abuse specifically in Medicaid programs. While the MIP increased the federal government’s role in addressing Medicaid fraud and abuse, the primary responsibility still falls on the states.

Florida’s Efforts to Prevent and Detect Medicaid Fraud and Abuse

In the State of Florida, the Bureau of Medicaid Program Integrity (Bureau of MPI) and the Fraud Prevention and Control Unit (FPCU) within AHCA work with Medicaid health plans to increase coordination and anti-fraud efforts to prevent, reduce and mitigate health care fraud, waste and abuse. These units collaborate closely with each other as well as other partners, including the Medicaid Fraud Control Unit (MFCU) in the Attorney General’s Office, to combat Medicaid fraud. Pursuant to Section 409.913, Florida Statutes, the Office of Medicaid Program Integrity (MPI) audits and investigates providers suspected of overbilling or defrauding Florida’s Medicaid program, recovers overpayments, issues administrative sanctions, collects liquidated damages (2012 – 2015 Core Contract), and refers cases of suspected fraud for criminal investigation. Any suspected fraud is referred to the MFCU for full investigation and prosecution.

The FPCU works to increase coordination and communication with managed care plans to aid in fraud prevention efforts as well as to coordinate the exchange of information to maximize provider network controls (AHCA, n.d., a). During FY 2012-2013, the FPCU met with each area office and identified issues of concern, including risks of fraud and abuse that may be unique to each geographic area or provider-type specific. These issues were then used to prioritize provider reviews and to identify needs for provider education by the Bureau of MPI (AHCA, n.d., a).

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In addition, in March 2011, the Medicaid and Public Assistance Fraud Strike Force, established under F.S. 624.351, was organized to inventory resources available to combat fraud and identify the processes and strategies in place (Strike Force, 2013). In 2012, the Strike Force prepared and distributed a survey to key states with experience in statewide Medicaid managed care. The purpose of the survey was to identify what other states have done to address fraud as they transitioned from fee for service to managed care in Medicaid. In addition, AHCA staff reviewed information available through the Centers for Medicare and Medicaid Services (CMS) to evaluate how Florida’s existing anti-fraud activities compared to what other states are doing. This review indicated that AHCA already had the appropriate program integrity strategies for managed care in place (Office of the Inspector General, 2013).

Florida Statutory Requirements for Medicaid Managed Care Plans

Under Florida Statute 409.91212, each managed care plan, as defined in s.409.920 (1)(e), shall adopt an anti-fraud plan addressing the detection and prevention of overpayments, abuse, and fraud relating to the provision of and payment for Medicaid services and submit the plan to the Office of Medicaid Program Integrity within the Agency for approval. At a minimum, the anti-fraud plan must include:

(a) A written description or chart outlining the organizational arrangement of the plan’s personnel who are responsible for the investigation and reporting of possible overpayment, abuse, or fraud;

(b) A description of the plan’s procedures for detecting and investigating possible acts of fraud, abuse, and overpayment;

(c) A description of the plan’s procedures for the mandatory reporting of possible overpayment, abuse, or fraud to the Office of Medicaid Program Integrity within the Agency;

(d) A description of the plan’s program and procedures for educating and training personnel on how to detect and prevent fraud, abuse, and overpayment;

(e) The name, address, telephone number, e-mail address, and fax number of the individual responsible for carrying out the anti-fraud plan; and

(f) A summary of the results of the investigations of fraud, abuse, or overpayment that were conducted during the previous year by the managed care organization’s fraud investigative unit.

By September 1 of each year, each managed care plan must report to the Bureau of MPI its experience in implementing an anti-fraud plan, and, conducting, or subcontracting for, investigations of possible fraudulent or abusive acts during the prior state fiscal year. The report must include, at a minimum: The dollar amount of Health Plan losses and recoveries attributable to overpayment, abuse and fraud; and the number of Health Plan referrals to the Bureau of MPI (2012-2015 Core Contract).

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Methods

The evaluation utilized a mixed-methods approach (Driscoll, Appiah-Yeboah, Salib and Rupert, 2007; Lieber, 2009), that is, the design and analyses include both qualitative and quantitative methods. The qualitative study included: 1) content analysis of the fraud and abuse plans submitted to the Bureau of MPI by managed care plans and 2) analyses and integration of supplemental interview data. The quantitative analyses utilized secondary data obtained through the 1) Annual Anti-Fraud and Abuse Activity Report (AFAAR) and 2) the Quarterly Anti-Fraud and Abuse Activity Report (QFAAR) that are required from each managed care plan by the Bureau of MPI. As noted above, the annual report, submitted by September 1 of each year, includes investigations of potentially fraudulent or abusive acts during the prior fiscal year. The QFAAR reports include information on the specific detection tools utilized for each suspected fraud and abuse incident.

Qualitative Analyses

The qualitative study focused on obtaining a comprehensive comparative analysis of the anti-fraud, waste and abuse plan policies and procedures for Medicaid managed care plans. Specific perceptions of efficiency and effectiveness as well as insights into the value of specific strategies were obtained through semi-structured interviews.

Content Analysis

Content analysis is a set of procedures for transforming non-structured information into a format that allows analysis (U.S. General Accounting Office, 1996). The intent of this method was to summarize and list the major themes or categories contained within the written anti-fraud plans in a manner that allowed comparison of fraud and abuse measures across the managed care plans. Anti-fraud policies from 24 Medicaid managed care plans were provided to the research team via a secure password-protected SharePoint portal in Year 1. In Year 1, the research team conducted qualitative comparative content analysis of four managed care plans and provided a Year 1 Preliminary Content Analysis report to the Agency in June 2013. The Medicaid managed care anti-fraud plans were updated in Year 2 and five additional managed care plans were chosen for review. The research team conducted qualitative comparative content analysis of the five managed care plans and provided a Year 2 Preliminary Content Analysis report to the Agency in April 2014.

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The anti-fraud plans from the selected managed care plans for each year were uploaded into Dedoose, a secured qualitative analysis software program for content analysis (Dedoose, 2013). The anti-fraud policy documents were then separately coded and analyzed in Dedoose by members of the evaluation team following accepted and recognized qualitative methods (King, 1998; Stemler, 2001; U.S. General Accounting Office, 1996). The reliance on coding and categorizing of data makes content analysis a particularly rich and meaningful tool to find critical information (Stemler, 2001). These methods involve a number of steps and a robust iterative process to ensure reliability and validity of the data analysis.

Coding is the process through which text can be categorized. First, a preliminary coding template is established and the coding is applied to the data. The coding template is then modified multiple times through several iterations. Revisions are made as necessary, and the categories are tightened to maximize mutual exclusivity and exhaustiveness (Weber, 1990). Codes are eventually grouped into themes or categories, and the data are summarized in tables that facilitate the summary of findings into a narrative form.

For the content analysis of the fraud and abuse plans, the researchers created a preliminary coding template after an initial read of the documents. The initial coding template developed through this process in Year 1 also served as the initial guide for the qualitative analysis of the fraud and abuse plans reviewed in Year 2. All steps following this initial review were repeated in both years. Fraud and abuse policies from each of the selected managed care plans were independently coded line-by-line by at least three members of the evaluation team. The three members assigned to independently code each plan were randomly selected so that the mix of independent coders varied across the plans. Reconciliation of the independent coding across all team members is performed to assure reliability, i.e., to assure that individual team members coded the same text in the same way (Weber, 1990). All four evaluation team members met to systematically review the coding and discuss any areas of disagreement, stopping when agreement was reached on all codes. All documents were then re-coded based on the revised coding template.

The four members of the evaluation team iteratively reviewed and reconciled coding numerous times as a reliability and quality control check (Stemler, 2001). Agreement of codes was facilitated by analysis tools available within the Dedoose software program that allow compilation and comparison of the findings by independent coders. Documents and coding were then reviewed again to isolate broad themes or categories to capture the best representation of the data. The coding excerpts were then compiled within Dedoose and downloaded to Excel. Finally, a comparative table was developed by managed care plan (columns) and major themes (rows) to provide ease in examining similarities and differences in the anti-fraud and abuse plans. This comparison table was the foundation for the qualitative findings reported below. To assure confidentiality, the plans reviewed were not identified and all reference to managed care plan names in the excerpts was removed. The plans were denoted by letters (A-I) only.

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Supplemental Semi-Structured Interviews

To obtain more specific perceptions and details regarding topics incorporated by managed care plan policies and procedures, a semi-structured interview protocol was developed for telephone interviews of Compliance Officers. The questions largely focused on measures requested by CMS and the Agency in conjunction with the demonstration waiver evaluation request, e.g., frequency and types of interactions with providers, documentation and tracking of efforts to deter fraud and abuse, and methods to measure effectiveness of such efforts.

The respondents for October 2013 pilot interviews were selected from the list of Compliance Officers of Medicaid managed care plans provided by AHCA. Respondents were scheduled for phone interviews that were recorded and then responses were transcribed to assist in analyses and synthesis of all of the interviews. Respondents to the pilot interviews suggested that the interview questions be provided in advance so that the respondents could prepare in advance and assure accuracy of responses. This suggestion was incorporated into the protocol for future interviews and respondents were provided an option to send written responses to the interview questions in order to minimize the time required to complete the interview. Two additional questions were also suggested and added to the interview: 1) use of commercially available anti-fraud software and 2) use of organizationally developed software or analytical tools. For respondents providing written responses, the phone time was minimized as the interviewer sought clarification of responses rather than obtaining all the information on the phone. A request for interviews was emailed to Compliance Officers and seven additional interviews were conducted in January and February of 2014. The total number of semi-structured interviews was 11, including the four selected for the pilot.

The evaluation team listened to the recorded interviews, reviewed the written responses, and transcribed the responses into a table to allow synthesis of results. These results were then incorporated into the content analysis themes, where appropriate, to provide more comprehensive information. Thus, the narrative of the integrated comparative qualitative analysis presented in the findings below includes both a summary of the themes/categories identified in the content analysis, describes key differentiating factors, and provides illustrative examples as well as the more detailed information from the semi-structured interviews.

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Quantitative Analyses

The quantitative analyses utilized data from the annual and quarterly reports submitted to the Bureau of MPI. The aim was to examine trends in overpayments and recoveries, fraud and abuse as well as the utilization of specific detection tools for suspected fraud and abuse incidents reported to the Bureau of MPI by the sample of managed care plans. The qualitative analysis did not identify any significant differences in fraud and abuse policies and procedures across the plans, with the exception of potential differences based on sophisticated analytic software that coincides with the size of the plans. Due to the resources required to develop and obtain more sophisticated software as well as indications in the qualitative analysis, the use of such tools appears to be more prevalent in medium and large sized managed care plans. Therefore, the sample of nine managed care plans included in the qualitative analysis were divided into three categories, small, medium and large, according to their total Medicaid enrollment in Florida provided by the Agency (AHCA, n.d., b) for the analyses. The small category included plans with Medicaid managed care enrollment under 10,000 members; the medium category included plans with enrollment greater than 10,000 but less than 100,000; and the large category included plans with enrollment greater than 100,000.

Annual Fraud and Abuse Reports (AFAAR)

The AFAAR report must include, at a minimum: The dollar amount of health plan losses and recoveries attributable to overpayment, abuse and fraud; and the number of health plan referrals to the Bureau of MPI (2012-2015 Health Plan Model Contract). The AFAAR includes plan-specific data on various measures including total overpayments identified for recovery, total overpayments recovered, total dollars identified as lost to fraud and abuse, total dollars identified as lost to fraud and abuse that were recovered, and the total number of referrals. These data are also identified by Medicaid contract type, i.e., Reform versus Non-Reform.

AFAAR data for three fiscal years, 2010 – 2011, 2011 – 2012, and 2012 – 2013, were provided to the researchers by the Agency for quantitative analyses to explore potential trends and relationships to differential policies and procedures. Even though data collection efforts have improved over time, complete data are not available over the three fiscal years considered for all nine plans. The incomplete data is due to several reasons including: a) some plans participated in Medicaid only in some fiscal years but not in all three; and b) some plans did not operate under both Reform and Non-Reform contracts in each of the three fiscal years. As a result, plans A and I were excluded from the AFAAR plan-specific analyses due to the inability to track trends over time for these two plans. Using the available data, the researchers examined the trends over time as the Reform efforts were expanded.

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Quarterly Fraud and Abuse Reports (QFAAR)

The QFAAR reports include information on the specific detection tools utilized for each suspected fraud and abuse incident reported. Multiple Excel files containing QFAAR data were provided by AHCA to the researchers. The files spanned the third quarter of 2009 through the second quarter of 2013. Prior to performing analyses, all identifying information associated with the specific managed care plans was removed and relabeled by letters (consistent with the qualitative analyses) for the purpose of confidentiality. Comparative analysis was performed on the QFAAR data which includes Medicaid contract type (Reform versus Non-Reform) as well detection tools for the eight quarters corresponding to FY 2010-2011and FY 2012-2013. This subset yielded a total of 1,927 fraud and abuse cases. As explained above, two plans were excluded from the analysis due to insufficient data points.

Analysis of the QFAAR data examined trends over time in terms of the primary detection tools reported utilized by the managed care plans in identifying the reported fraud and abuse cases. Detection tools were grouped into categories by studying the narrative elements of cases. Specifically, the researchers examined whether cases were identified using data analysis methods as the primary detection tool or not. These techniques include data mining, data analytics, data matching, pre- and post-payment reviews, sometimes with the use of specialized software, etc. The research team then compared the frequency of their use over time for both Reform and Non-Reform contract types. Percentage changes over time are reported rather than the specific number of incidents in each fiscal year in an effort to provide better comparability and protect confidentiality of the managed care plans.

Findings

Integrated Qualitative Analyses

This section presents the integration of the content analysis of the fraud and abuse plans with the more detailed information obtained through the semi-structured interviews. The narrative below follows five major themes or categories identified through the iterative coding of excerpts from the fraud and abuse plans using Dedoose content analysis tool:

1. Detection and Prevention Tools 2. Education and Training 3. Internal and External Investigations 4. Internal and External Reporting 5. Corrective Actions

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Not surprisingly, the first four categories align with the Agency’s contractual requirements in F.S. 409.91212. The fifth category, Corrective Actions, captures information consistent with Agency and plan procedures following the identification of suspected and/or confirmed overpayments, fraud and abuse. The level of detail regarding the specific policies and procedures within each of the categories differed across the individual anti-fraud plans; therefore, examples provided below generally derive from plans providing more specific information. Examples should be considered illustrative but not exhaustive, as the descriptions provided do not signify exclusive policies. As such, an attempt was made to pull the descriptive examples from each of the anti-fraud plans. The examples demonstrate differences in the conveyance of the written policy but not necessarily in application or practice. In addition, the categories themselves are not exclusive as tools may be utilized for multiple purposes.

Detection and Prevention Tools

Under the theme of Detection and Prevention Tools, the coded excerpts from each anti-fraud plan describe the policies and procedures designed to prevent and detect waste, abuse, and fraud among their providers, members, and employees as well as vendors and subcontractors. Generally defined, prevention refers to “processes to avoid fraud and/or abuse prior to payments” while detection refers to processes to monitor, detect, or investigate fraud after an instance of fraud and/or abuse has occurred” (Strike Force, 2013, pp. 45-46). Detection can result from computerized detection tools, leads from incoming complaints and referrals, information from other regulatory agencies, newspaper articles and advertisements, and Explanation of Medical Benefits forms, as well as from data mining and audit reviews (AHCA and Attorney General of the State of Florida, 2013). The distinction between prevention and detection is not absolute. In the semi-structured interviews as well as in the content analysis, some plans noted the overlap of the two. As stated by one Compliance Officer, “detection leads to prevention”. While some plans discuss specific tools that are clearly aimed at either prevention or detection, the “commingling” of detection and prevention was evident. For example, retrospective analytics and data mining often identify outliers and practices that point to a need for preventive intervention and/or educational initiatives. Similarly, retrospective approaches can also identify predictive modeling to prevent future fraudulent activities. All plans, as required, include an anonymous compliance hotline (toll-free phone number and/or online forms) as a means to obtain tips. In addition to their own hotlines, plans also refer to AHCA’s Consumer Complaint Hotline as well as to the Florida Attorney General’s Hotline. The hotlines serve as a referral tool for both internal (i.e., employees) and external (i.e., members, providers, vendors/suppliers, contractors, other third-persons) individuals. At least one plan, (Plan C), goes a step further to encourage member involvement in detection by offering a reward for reporting suspected fraud and abuse. Beyond hotlines, a few of the anti-fraud plans mention notifications and mailings as detection tools focused on their members. For instance, mailing an Explanation of Medical Benefits (EOMB) provides a means of members “being informed of their recent utilized services through the plan’s explanation of benefits, allowing them an opportunity to question unauthorized services”, for Plan A. While Plan B states, “Use of this card by any person other than the member is fraud” on member ID cards as a means of fraud prevention.

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Other primarily preventive strategies include monthly checks of providers, subcontractors, vendors and employees against federal and state excluded parties systems. Many plans reference (re)credentialing procedures with particular attention to identifying entities excluded from participation in federal healthcare programs. Plan H, for example, states it “actively monitors various government reports besides federal and state reporting systems for listings of suspended or terminated entities.” Plans include pre-authorization or prospective utilization management by either manual or electronic review as a means to control potential fraud, waste or abuse. Plan I mentions pre-payment verification systems that check the accuracy of benefits and eligibility before a payment is approved. The managed care plans also utilize provider profiling based on past patterns of falsified or overstated claims including up-coded levels of service, misrepresented medical information, and charging for services not rendered. While some anti-fraud plans provide extensive description of their fraud and abuse analytical detection strategies and tools, other plans are not as detailed. The majority of the information regarding these detection tools focuses on medical utilization review, auditing, monitoring, and other types of data analysis and data mining. Some simply mention that the reviews of payments for non-covered or non-rendered services, reviews of falsified encounters, and reviews of overpayments/upcoding are conducted by a Registered Nurse (RN). Others detail more specific strategies targeting pre-payment and post-payment utilization reports, provider billing pattern reviews, reviews of gender- and age-appropriate billing, reviews of multiple-item billing and bundled/unbundled billing, “blitz” audits, and pharmacy reports. Following the feedback from the pilot interviews, the second round of semi-structured interviews specifically asked for information regarding the use of organizationally developed and/or commercially purchased analytical software in fraud detection efforts. Information obtained from interviews with the Compliance Officers as well as data from QFAARs and AFAARs demonstrate that the use of data analysis methods, including commercially available anti-fraud software as a tool for prevention and detection is relatively more common among large and some medium managed care plans. In alignment with the CMS and Agency area of specific interest, the Compliance Officers were asked to provide their perception about the most effective policies and procedures intended to deter and identify fraud and abuse. Respondents from all medium and large plans either had a system in place, or were in the process of implementing one, to measure the effectiveness of their efforts to control program integrity. A variety of techniques and methods were identified by the respondents, including: tracking soft dollar savings, periodic reviews and updates to their policies in response to the previous year’s activities, monitoring volume of cases, time constraints on cases, amounts recovered from providers, internal audit outcomes, and monthly audits of their investigators. Only one small plan stated that they do not formally measure the effectiveness of their policies and procedures related to fraud detection efforts. While the methods for measuring the effectiveness of policies and procedures varied amongst plans, there was general agreement that data analysis and the use of pre-payment and post-payment fraud detection tools were the most effective methods for both prevention and detection.

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Education and Training

Under the theme Education and Training, the content analysis concentrated on the anti-fraud plan policies, procedures and activities that seek to educate employees, providers, members and others. All managed care plans have handbooks, manuals, and guidelines that contain various information regarding fraud and abuse detection and prevention policies and procedures. These materials are made available to employees, members, and providers electronically, physically, or both.

Internal education of employees must contractually occur within 30 days after hiring, and is followed by periodic training (at least annually). The majority of anti-fraud plans, however, disclosed that initial education occurred in conjunction with new employee orientation. Further commitment to the educational process is evident through references to on-going training, formal tracking of education, and communicating timely updates on anti-fraud policy changes to relevant parties, including employees, members, providers and vendors, through newsletters. At least one managed care plan (Plan H) not only tracks training but goes further by stating that they require their employees to pass a test with a perfect score.

Managed care plans utilize a variety of educational tools and methods including lectures, videos, self-study, conferences/seminars, and interactive computer based training. Additionally, specific educational materials are developed and available for differing groups, i.e., members, employees, providers and vendors. For example, Plan B features member and provider newsletters as a means to provide educational updates; and Plan C mentions specific training for employees within specialized departments such as Compliance and Finance. Similarly, Plan D mentions “specialized sessions specific to the individual risk area.” Additionally, some plans mention the inclusion of disciplinary guidelines and whistleblower protection as part of their educational program.

Provider education receives particular attention. Examples include sending provider education letters, holding post training Q & A sessions, and conducting group as well as one-on-one training sessions. The importance of provider education is highlighted by mention that non-compliant providers (30 days past anniversary date of becoming active and who have not received training) receive one-on-one fraud and abuse training (Plan C). The importance of provider education was also emphasized by some Compliance Officers during the interviews.

Health care fraud and abuse training is often supplemented with training in business ethics and/or specific training based on specific needs, e.g., differentiated education tailored for specific departments (e.g., claims, credentialing, member services) or Plan D’s “specialized sessions specific to the individual risk area”. In addition to internal educational programs, some plans also mention that they encourage individuals to seek more education through professional associations or industry training.

Responsibility for the fraud and abuse education of vendors and suppliers is assumed internally by some fraud and abuse plans while not by others. One anti-fraud plan (Plan D) states, “delegated vendors conduct their own training programs related to fraud and abuse”. While all managed care plans utilize education and training as a method of prevention and deterrence of waste, fraud and abuse, most also mention education incorporated within corrective actions. The utilization of education in corrective actions is addressed below.

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Semi-structured interviews addressed the frequency and methods of interactions with providers. Responses focused mostly on communications that target education and updating of policies and procedures. To do so, the communication between both parties occurs by mailed correspondence such as newsletters, by phone or email, or during an audit process. In addition to planned communications and education sessions, “as needed” communication of a more personal nature occurs in response to detection of aberrant activity. As one Compliance Officer explained (Plan E), “I may be called in to provide guidance and meet directly with a provider when requested by the SIU [Special Investigative Unit]…, or when necessary to resolve a compliance matter”.

Internal and External Investigations The theme Internal and External Investigations encompasses content analysis excerpts that identified reference to internal investigations, which refer to those that occur within the managed care plan and external investigations, which involve an outside entity conducting the entire or part of investigation. The anti-fraud plans provided information regarding the internal organizational structure, the composition of their investigative team, and a description of their investigative operations of each of the sample of managed care plans to varying degrees of specificity. All managed care plans must have a Special Investigative Unit (SIU) and/or a Compliance Officer that is ultimately responsible for conducting the investigation and overseeing suspected misconduct in a timely, confidential, and reasonable manner. Some managed care plans employ their own investigators within a SIU, and others mention using subcontractors to conduct the investigations (e.g., Plan G) or to aid with plan’s internal SIU’s investigative work (e.g., Plan H). If a plan chooses to subcontract investigations, filing of information on the subcontracting party must be provided to the Bureau of MPI at least 60 days before contract execution (2012-2015 Core Contract). The steps involved in an internal investigation, as outlined in some anti-fraud plans, include: detection and referral, initial assessment to determine the merit of an allegation, development of investigative strategy, information gathering, evaluation of evidence, determination of corrective action and recommendations, and civil/criminal proceedings. Procedures for tracking and documenting of investigations vary across managed care plans. While some use manual systems, others utilize computer software programs. As mentioned under the theme detection and prevention tools, the use of data analysis is an increasingly important tool in detection and deterrence of fraud and abuse. Analytical software is also becoming an important tool in the investigation process, most commonly among large and medium managed care plans, as its use requires ample resources. Software tools can both assist in the investigative process, and serve as tracking mechanisms for ongoing investigations. One of the larger managed care plans (Plan I) noted success in utilizing software to differentiate between billing errors and suspected fraud and abuse. Another plan (Plan H) prepares detailed internal reports that show all of SIU’s activity as part of their tracking and documenting efforts to prevent fraud and abuse. Other medium plans mention using case management systems to track and document investigative cases. Smaller managed care plans, on the other hand, tend to maintain manual logs and securely store all physical documentation of fraud and abuse.

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All plans highlight collaborative efforts across various departments internally during investigations and emphasize cooperation with state and federal regulatory agencies as well as law enforcement agencies in the case of external investigations. External investigations may result from internally reported incidents to the appropriate agencies or may be initiated externally.

Internal and External Reporting Under the theme, Internal and External Reporting, information was compiled regarding the internal and external policies for the reporting of suspected fraud, waste or abuse. In accordance with the Agency’s requirements, all nine managed care plans in the sample require their members, providers, and employees to report any suspected fraud and abuse. Internal reporting policies focus on reporting within the managed care plan to senior management or the governing body, whereas external reporting policies focus on post-investigative reporting to government (e.g., Bureau of MPI, CMS) or law enforcement agencies. For external reporting, each anti-fraud plan outlines the required procedures for reporting suspected or confirmed fraud and abuse to the appropriate regulatory or law enforcement agencies established through contractual and/or regulatory guidelines. Most managed care plans specify the contractual time requirement (within 15 calendar days of detection). Some of the managed care plans mention required periodic internal reports that correspond to the required time for submission of their external reports, e.g., the AFAAR and QFAAR, to appropriate oversight agencies. While all plans are required to submit the annual and quarterly reports to the Bureau of MPI, Plan E indicates external reporting of suspected fraudulent activities to the state’s insurance department. Information on the organization as well as specific policies for internal reporting vary widely across plans as the internal policies on reporting are not subject to regulatory guidelines. Some plans describe internal compliance committees to handle reports of fraud and abuse – notwithstanding that the Compliance Officer can always go directly to higher administration. These internal committees often receive and compile any required internal reports that may be submitted to the managed care plan’s executive team and/or governing body. Internal reports are also, generally, the basis for the submission of external reports to appropriate oversight agencies.

As discussed above under Detection and Prevention Tools and Internal and External Investigations, there appears to be a movement towards online reporting both internally and externally. Indeed, the 2012 – 2015 Health Plan Model Contract states that “unless otherwise specified, all reports are to be submitted electronically”.

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Corrective Actions

The final theme, Corrective Actions, includes the actions taken upon confirmation of any misconduct in terms of fraud and abuse. Corrective actions can take the form of disciplinary steps or termination in the case of employee misconduct. In the case of providers, vendors/suppliers, and contractors, corrective actions may first include education and training to address the underlying problem and prevent future non-compliance as long as it is only a billing error. If subsequent monitoring reveals that non-compliance still continues, then more formal corrective actions follow such as possible suspension of payments, recovery of losses through repayments, and eventually termination or amendment of the contract. Depending on the severity of the misconduct, civil litigation and additional reporting to appropriate authorities and law enforcement can occur (as discussed under the Internal and External Reporting theme above). Some anti-fraud and abuse plans describe the time frames for specific achievements in corrective action plans and some provide further specifics in terms of types of corrective actions and how they are enforced. For example, Plan C states that “Monitoring and evaluation of a corrective action(s) is usually done within six (6) months following the implementation of a corrective action(s)”. Plan I also states that indication of fraudulent or abusive practices can trigger an “in-depth retrospective review” of providers. These providers are also automatically placed on pre-payment reviews in order to minimize further potential damages. Another plan (Plan F) lists additional corrective actions such as referral of non-compliant providers to the medical board or to network management for disciplinary action while another plan (Plan G) allows for voluntary withdrawal of providers to avoid formal sanctions. Corrective actions for members do not receive significant attention in the majority of the anti-fraud plans analyzed. However, all plans state that confirmed member misconduct can result in claim suspension or denial. Plan I also mentions using enrollee education as an initial corrective action.

Synopsis of Qualitative Findings

As the above discussion indicates, common themes consistent with the statutory requirements emerged in the content analysis of the anti-fraud plans from the sample of nine Medicaid managed care plans. Semi-structured interviews added depth and specificity to the themes in areas of interest identified for inclusion in the evaluation by CMS and the Agency, e.g., types and frequency of interactions with providers, and tracking and the effectiveness of fraud and abuse policies and procedures. Across the anti-fraud and abuse plans analyzed, the amount of detail provided varied greatly, however, all met the statutory requirements of the State of Florida and demonstrated a strong commitment to extensive efforts to deter and detect fraud, waste and abuse by providers and members.

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The purpose of the qualitative analysis was two-fold. First, to assist in evaluating the Agency’s oversight of managed care plans including adapting efforts to the surveillance of fraud and abuse within the managed care system. Included in this purpose is the identification of key policies and procedures utilized in detecting and deterring fraud and abuse by providers and members. The second purpose was to determine if managed care plans displayed any key differences that might identify differential impact on measures of fraud and abuse. Two other potential streams of inquiry could not be pursued. Due to lack of historical data from managed care plans, changes in policies and procedures over time could not be investigated. Nor was data available to assess differences in policies and procedures for detecting fraud in fee-for-service or other commercial or Medicare managed care plans. While the semi-structured interviews sought to obtain this information, the responses indicated that the organizations either offered only Medicaid managed care plans or essentially used the same policies and procedures for all plans administered by their organizations.

The State of Florida through the Bureau of MPI and consistent with the Medicaid Health Plan Standard Contract (AHCA, n.d., a) maintains strict regulatory control oversight of managed care plans and the anti-fraud policies and procedures utilized by these plans in the deterrence, detection and surveillance of fraud and abuse within the managed care system. The content analysis of anti-fraud plans that each Medicaid managed care plan must submit annually to the Bureau of MPI demonstrates that all plans conform to the standards of the contract and rigorously adhere to the reporting requirements.

Due, at least in part, to the managed care plans’ adherence to the strict requirements established in the Standard Contract, no major differences in policies, procedures and/or compliance activities were found across nine managed care plans included in the sample. Therefore, no attempt to identify differential impact of any distinguishing policy on fraud and abuse measures could be assessed. The only potential difference explored was the utilization of more sophisticated data analytical software packages by larger managed care organizations, i.e., those with larger numbers of enrollees.

It is important to note that the description of claims surveillance and reviews in anti-fraud plans conveys the complementary nature of prospective and retrospective strategies rather than substituting one for another. Retrospective data mining combined with clinical assessments can help to identify aberrant billing patterns, and these in turn can be used in predictive modeling to develop algorithms to detect and prevent future fraudulent practices and payments. As such, findings of past investigation and recovery efforts feed data analytics, which assist with future auditing, monitoring, and detection capabilities. At the same time, prospective detection and audit efforts can help identify opportunities for retrospective investigation of potential violations (e.g., plan F).

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Quantitative Analyses – Trends from Fiscal Years 2010-2013

This section presents the findings from the quantitative analyses of data submitted to the Bureau of MPI in the AFAAR and QFAAR reports from the managed care plans. Based on the findings from the qualitative analyses, the trends were explored to determine if the size of plans, linked to more sophisticated data analytic tools, had an impact on the detection and deterrence of fraud and abuse.

Trends in AFAAR Data

The AFAAR data provided by the Agency contained reported overpayments identified and recovered, fraud and abuse identified and recovered, and number of referrals for the nine managed care plans in the sample for FYs 2010-2011; 2011-2012; and 2012-2013. These data were examined to identify trends over time in order to provide a proxy measure of the impact of participation in the demonstration reform program on the deterrence and detection of fraud and abuse.

Based on the growing popularity of data mining, business analytics and analytical fraud detection software packages currently available, and upon the qualitative data finding that larger plans were more likely to use sophisticated software as a detection tool, the AFAAR data was assembled by size of Medicaid managed care enrollment: the small category included enrollment under 10,000 members (Plans A and G); the medium category included enrollment greater than 10,000 but less than 100,000 (Plans C, D, E and H); and the large category included enrollment greater than 100,000 (Plans B, F and I). In addition, the AFAAR data includes a descriptor for contract type, i.e., Reform and Non-Reform. Therefore, the data were examined in concert with reported contract type as well as size by enrollment.

Table 1 provides a comparison of small, medium and large managed care plans distinguished between Reform (top panel) and Non-Reform (bottom panel) contract types. The direction of changes over time during this period are provided for each of the measures included in AFAAR. The “+” symbol indicates increases and the “-“ symbol indicates decreases in proportion of reported measures over time. In addition to the AFAAR measures listed above, a total amount identified for recovery and total amount recovered was created by combining the overpayment with fraud and abuse figures for each managed care plan.

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Table J1: Trends in Overpayments, Fraud, and Abuse Over the Period of FY2010-2011 and FY2012-2013, by Plan Size

Overpayments Identified

(1)

Overpayments Recovered

(2)

Fraud & Abuse

Identified (3)

Fraud & Abuse

Recovered (4)

Total Identified

(1+3)

Total Recovered

(2+4)

Number of

Referrals

Reform

Small plans

G - - -

Medium plans

C - - +

H + + + + + + +

Large plans

B - - - - - - -

F - - - - - - -

Non-Reform

Medium plans

D + + + +

E - - + - - +

H + + + + + + same

Large plans

B - - - - - - same

F + + + - + + +

The empty cells in Table 1 are due to missing data as explained above (see page 25), there are several reasons including: a) some plans participated in Medicaid only in some fiscal years but not in all three; and b) some plans did not operate under both Reform and Non-Reform contracts in each of the three fiscal years. As a result, plans A and I were excluded from the AFAAR plan-specific analyses due to the inability to track trends over time for these two plans. While a cursory review of Table 1 suggests that larger plans (those with higher enrollments) provide more consistent and complete reports when compared to smaller health plans, empty cells may simply be due to lack of incidents in the smaller plans or a plan not participating in the demonstration program during earlier years. Based on the total numbers, three general trends can be detected over the time period of 2010-2013. First, overpayments as well as fraud and abuse amounts have decreased over time under the Reform contracts with the sole exception of plan H (see the top panel of Table 1). Second, this decrease was particularly notable for larger and more established plans, e.g., Plans B and F. Taken together, these two plans account for approximately two-thirds of the total enrollment among the nine plans in the sample, and therefore, are critical in the analysis and interpretation of findings. Third, these first two trends (decreased amounts in both overpayments as well as fraud and abuse, and the notably larger decreases in the larger plans) were not observed when examining the data for the Non-Reform contracts.

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While these overall trends suggest that the managed care plans with Reform contracts are indeed improving detection and deterrence of fraud, waste and abuse, there are several limitations that must be taken into account when examining these trend analyses. First, case investigations often extend across fiscal years. Second, whatever the magnitude, increases and/or decreases in overpayments or in fraud and abuse identified can be due to a rise (decline) in fraudulent activities or detection efforts or a combination of the two. The analyses cannot determine causality due to the multiplicity of factors that may impact the activity and the amounts. As such, reductions in these measures over time should not be assessed in isolation but considered as one of several factors that may determine the success of detection, prevention and recovery efforts occurring during the years of the demonstration program.

Trends in QFAAR Data

The QFAAR data provided the opportunity to examine trends over time in terms of the detection tools utilized in identifying fraud and abuse cases. As discussed above, the plans were grouped into small, medium and large categories. When reviewing the detection tools reported in the QFAAR data, the researchers identified which detection tools would fall under a broad data analysis category including those that utilized data mining, data analytics, data matching, pre- and post-payment reviews, sometimes with the use of specialized software, etc. This grouping of analytical tools was then compared over time, i.e., FY 2010-2011 and FY 2012-2013, to explore for trends in frequency of use for both Reform and Non-Reform contract types. To assure the confidentiality of the managed care plans as well as to provide accurate comparative data, the number of incidents in each have not been reported, but only percentage changes over time.

The trend analyses for the utilization of data analysis in detection techniques demonstrate a clear difference between the Reform and Non-Reform contract types. The overall proportion of fraud and abuse cases identified using data analysis tools, decreased from 51% in FY 2010-2011 to 30% in FY 2012-2013. However, this overall finding conceals the differential between Reform and Non-Reform contract types. In an effort to allow for a meaningful comparison, it is important to evaluate changes over time within each plan and contract type.

Under the Non-Reform contract type, from FY 2010-2011 to FY 2012-2013, all plans have decreased the proportion of fraud and abuse cases identified using data analysis tools with the exception of Plan I. This proportion went down from 37% to 15% for plan B, from 63% to 21% for plan D, from 41% to 18% for plan E, from 67% to 41% for plan F, and from 64% to 56% for plan H. Chart 1 displays all these plan-specific proportional changes within the Non-Reform Contracts. Plan I was the only Non-Reform Contract type that increased the proportion of fraud and abuse cases detected using data analysis tools, showing an increase from 48% to 80%. Interestingly, the increase in Plan I for use of data analytical tools parallels the findings in the content analysis of fraud and abuse plans as well as the supplemental interviews that suggests that large plans are more likely to incorporate sophisticated data analysis in their efforts to detect and deter fraud and abuse.

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Figure J1: Proportion of Fraud and Abuse Cases Identified Using Data Analysis Tools by Contract Type

Examination of the trend of reported utilization of data analysis tools for identification of fraud and abuse cases by plans for the Reform contract type demonstrated increased adoption of these tools. Here, again, however, there was one exception, Plan H. Plan B experienced an increase from 31% to 50%, Plan C experienced an increase from 0% to 8%, and Plan I experienced an increase from 54% to 82%. Plan H, which is categorized as a medium plan based on Medicaid managed care enrollment, reported a decrease from 79% to 56% for the proportion of fraud and abuse cases identified using data analysis tools. Coincidentally, Plan H is also the only plan under the Reform contract type that experienced higher overpayments as well as fraud and abuse amounts during this the same time period (see the top panel of Table 1).

Synopsis of Quantitative Findings

Trends examined in the amount of reported overpayments and reported fraud and abuse by the Medicaid managed care plans included in the sample indicate overall that these numbers have decreased during the FYs included in the analyses. Accompanying these decreases, is a general increase in the proportion of cases identified in Reform contract types through the utilization of detection tools incorporating data analytical techniques such as data mining, data analytics, data matching, pre- and post-payment reviews, and the utilization of specialized fraud and abuse software. While a causal relationship cannot be established due to the multiplicity of factors that may impact this change, indications are clear that plans identified as Reform contract types are demonstrating increased use of data analytical tools over time and are increasingly effective in detecting or deferring fraud and abuse within the Medicaid demonstration program.

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Summary and Discussion

This report presents qualitative and quantitative analyses to evaluate the impact of Florida’s Demonstration Waiver program as a deterrent against Medicaid fraud and abuse. Based on a sample of nine managed care plans participating in the demonstration program, the evaluation sought to evaluate these two specific objectives of the demonstration program:

1) To serve as an effective deterrent against fraud and abuse by moving from a fee-for-service to a managed care delivery system; and

2) To maintain strict oversight of managed care plans including adapting efforts to the surveillance of fraud and abuse within the managed care system.

While the multiplicity of factors impacting the program over the years prevents establishment of direct causality by any specific factor, three general trends emerged from the quantitative analyses using the sample of data provided for evaluation. These three trends provide rudimentary indication that the managed care plans in the sample are increasingly effective in deterring fraud and abuse. First, overpayments as well as fraud and abuse amounts have decreased over time under the Reform contracts with one exception. Second, this decrease was particularly notable for larger and more established plans, e.g., plans with enrollments greater than 100,000. Third, these first two trends (decreased amounts in both overpayments as well as fraud and abuse, and the notably larger decreases for the larger plans) were not observed when examining the data for the Non-Reform contracts. This difference provides preliminary support for the impact of managed care plans operating under the auspices of Reform contracts versus Non-Reform contracts. More conclusive support would require addressing limitations of the current analyses, examples of which are provided below. Several limitations must be taken into account when examining these trend analyses. First, the magnitude of increases and decreases over time cannot be accurately determined due to the fact that case investigations often extend across FYs. Second, whatever the magnitude, increases and/or decreases in overpayments or in fraud and abuse identified can be due to a rise (decline) in fraudulent activities or detection efforts or a combination of the two. As such, reductions in these measures over time should not be assessed in isolation but considered as one of several factors that may determine the success of detection, prevention and recovery efforts taking place during the years of the demonstration program. These limitations should be taken into account for future research in order to provide more conclusive evidence. The qualitative analyses revealed a complementarity between prospective and retrospective strategies which highlights several important points mentioned in various anti-fraud plans. First, it is important to record continuous data mining queries in electronic libraries in order to be able to effectively exploit them as part of future detection and prevention tools. Second, fighting fraud and abuse requires pooling of data and a coordinated team effort within each plan across various departments and staff members including compliance personnel, investigators, data analysts, and information technology officers. Third, it also requires close collaboration within the industry with other plans and various anti-fraud associations. This collaboration would allow for sharing of best practices, effective detection and prevention tools, as well as trends in fraudulent schemes across the entire healthcare industry.

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Finally, staying current with the latest research, methodologies, innovative technologies, and updates in state guidelines seems to be crucial to successful integration of proactive and retrospective efforts to combat Medicaid fraud, abuse, and waste. Therefore, effective communication and cooperation among the contracted health plans and state agencies plays a key role. Florida’s efforts in establishing such cooperation is evidenced by the Bureau of MPI identifying and developing education programs in conjunction with the FPCU’s identification of issues of concern, including risks of fraud and abuse that may be unique to each geographic area or provider-type specific (AHCA, n.d., a). The Agency maintains regulatory control and oversight of managed care plans and the anti-fraud policies and procedures utilized by these plans in the deterrence, detection and surveillance of fraud and abuse within the managed care system. This control is founded in the details and requirements established by the 2012-2015 Health Plan Model Contract that requires the submission of an annual anti-fraud plan as well as quarterly and annual reports on the managed care plan’s efforts to prevent and deter fraud and abuse. The content analysis of anti-fraud plans that each Medicaid managed care plan must submit annually to the Bureau of MPI demonstrates that all plans conform to the standards of the contract and adhere to the reporting requirements. The State of Florida’s efforts to deter and detect fraud and abuse in Medicaid managed care extend beyond regulatory and contractual requirements regarding fraud and abuse policies and procedures for managed care plans in the demonstration program. The State has reviewed anti-fraud systems and resources across state agencies to enhance collaboration and advance the effectiveness of prevention, detection and recoupment (Strike Force, 2013). As a result of the efforts, the Agency is procuring a new case management system that will incorporate advanced detection systems as well as a new Public Benefits Integrity Data Analytics and Information Sharing Initiative that will detect and deter fraud, waste and abuse in Medicaid (Strike Force, 2013). In concert with the increased utilization of data analysis techniques within managed care plans, the State has likewise added new technology and software detection tools, specifically predictive modeling software. This new software based approach enhances the capability to detect fraud in Medicaid claims by identifying data anomalies not found using traditional detection tools. In 2013, the Bureau of MPI was appropriated $3 million to procure a data analytics service to assist in the identification of noncompliant, abusive or possibly fraudulent providers and recipients (Strike Force, 2013, p. 20). Further, the Agency has developed a training program for the Bureau of MPI investigators designed to help them better understand a variety of aspects of managed care in Florida that are integral to success at early detection of fraud (Strike Force, 2013).

As Florida moves to statewide managed care, the Bureau of MPI and the MFCU are preparing for the

transition. These units will continue to monitor, review, audit and inspect managed care plans to ensure

that Florida’s Medicaid enrollees receive appropriate, medically necessary and high quality healthcare

(Strike Force, 2013).

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References

Agency for Health Care Administration (AHCA). (n.d., a). Florida Medicaid reform pilot. Retrieved from

http://www.fdhc.state.fl.us/medicaid/medicaid_reform/. Agency for Health Care Administration (AHCA). (n.d., b). Comprehensive Medicaid Managed Care

Enrollment Reports. Retrieved from http://ahca.myflorida.com/mchq/Managed_Health_Care/MHMO/med_data.shtml

Agency for Health Care Administration (AHCA). (September 1, 2013). 2012 - 2015 Health Plan Model

Contract Attachment II – Core Contract Provisions. Retrieved from http://ahca.myflorida.com/mchq/managed_health_care/MHMO/docs/contract/1215_Contract/Sept2013/SEP2013ContractAttII-AMEND-CLEAN.pdf

Agency for Health Care Administration (AHCA) and Attorney General of the State of Florida. (2012). The

state’s efforts to control Medicaid fraud and abuse FY 2011-2012. Retrieved from http://edocs.dlis.state.fl.us/fldocs/ahca/medicaid/fraud/ar/2011-2012.pdf

Agency for Health Care Administration (AHCA) and Attorney General of the State of Florida. (2013). The

state’s efforts to control Medicaid fraud and abuse FY 2012-2013. Retrieved from http://edocs.dlis.state.fl.us/fldocs/ahca/medicaid/fraud/ar/2012-2013.pdf.

Busch, R. (2012). Healthcare fraud: Auditing and detection guide. Hoboken, New Jersey: John Wiley &

Sons, Inc. Centers for Medicare and Medicaid Services (CMS). (2000). Guidelines for Addressing Fraud and Abuse

in Medicaid Managed Care. U.S. Health Care Financing Administration's (now the CMS) National Medicaid Fraud and Abuse Initiative. Washington, D.C.: CMS.

Centers for Medicare and Medicaid Services (CMS). (2002). Guidelines for Constructing a Compliance

Program for Medicaid Managed Care Organizations and PrePaid Health Plans. Medicaid Alliance for Program Safeguards. Atlanta, GA: CMS.

Centers for Medicare and Medicaid Services (CMS). (2011a). CMS waiver authorities and special terms

and conditions. Retrieved from http://www.fdhc.state.fl.us/medicaid/medicaid_reform/. Centers for Medicare and Medicaid Services (CMS). (2011b, July). CMS fraud prevention initiative.

Retrieved from http://www.cms.gov/Outreach-and-Education/Outreach/Partnerships/downloads/BackgrounderFraudPreventionInitiative.pdf.

Centers for Medicare and Medicaid Services (CMS). (2012, November). Medicare fraud & abuse:

Prevention, detection, and reporting. Retrieved from: https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/Fraud_and_Abuse.pdf.

MED150 Evaluation of Medicaid Reform to Deter Fraud and Abuse 28

303

Cheung-Larivee, K. (2012, June 14). CMS: Medicaid fraud audits 'disappointing'. FierceHealthcare. Retrieved from http://www.fiercehealthcare.com/story/cms-medicaid-fraud-audits-disappointing/2012-06-14.

Dedoose Version 4.5, web application for managing, analyzing, and presenting qualitative and mixed method research data (2013). Los Angeles, CA: SocioCultural Research Consultants, LLC (www.dedoose.com).

Driscoll, D., Appiah-Yeboah, A., Salib, P. and Rupert, D. (2007). Merging qualitative and quantiative data in mixed methods research: How to and why not. Ecological and Environmental Anthropology, 3(1), 19-28.

Dudek, E. (2012, January 24). AHCA press release: Secretary Dudek, Attorney General Bondi release annual Medicaid fraud report. Retrieved from http://www.fdhc.state.fl.us/Executive/Communications/Press_Releases/archive/docs/2012/fraud_and_abuse_press_release1-24-12.pdf.

Federal Bureau of Investigation (2010). Rooting out health care fraud is central to the well-being of both our citizens and the overall economy. Published 4/16/2010. Retrieved from http://www.fbi.gov/about-us/investigate/white_collar/health-care-fraud.

Goldman, T.R. (2011). Health policy brief: Eliminating fraud and abuse. Health Affairs, July 31, 2011. King, N. (1998). Template Analysis. Qualitative Methods and Analysis in Organizational Research. G.

Symon and C. Cassell. London: Sage Publications. Lieber, E. (2009). Mixing qualitative and quantitative methods: Insights in design and analysis issues.

Journal of Ethnographic & Qualitative Research, 3, 218-227. Lieber, E. (2012). Dedoose: Great research made easy. Retrieved from

http://wiki.dedoose.com/index.php/Main_Page. Medicaid & Public Assistance Fraud Strike Force. (October, 2013). Strike Force Annual Report.

Retrieved from http://www.flstrikeforce.com/Documents/Strike-Force-Annual-Report-2013(3).pdf

Morris, L. (2009). Combating fraud in health care: An essential component of any cost containment

strategy. Health Affairs, 28(3), 1351-1356. National Association of Medicaid Fraud Control Units (NAMFCU). (2012). What is Medicaid fraud?

Retrieved from http://www.namfcu.net/about-us/what-is-medicaid-fraud. National Health Care Anti-Fraud Association (NHCAA). (n.d.). The challenge of health care fraud.

Retrieved from http://nhcaa.org/resources/health-care-anti-fraud-resources/the-challenge-of-health-care-fraud.aspx.

National Health Care Anti-Fraud Association (NHCAA). ( 2013a). Health care fraud – Everyone pays.

Published 3/1/2013. Retrieved from http://nhcaa.org/news/health-care-anti-fraud-everyone-pays.aspx.

MED150 Evaluation of Medicaid Reform to Deter Fraud and Abuse 29

304

National Health Care Anti-Fraud Association (NHCAA). ( 2013b). Health care fraud and organized criminal groups. Published 3/13/2013. Retrieved from http://nhcaa.org/news/health-care-fraud-and-organized-criminal-groups.aspx.

Office of the Inspector General. (n.d.). Medicaid Fraud Control Units – MFCUs. Retrieved from

https://oig.hhs.gov/fraud/medicaid-fraud-control-units-mfcu/index.asp. Office of the Inspector General. (September, 2013). Office of the Inspector General Annual Report FY

2012-2013. Retrieved from http://www.fdhc.state.fl.us/Executive/Inspector_General/ Rashidian, A., Joudaki, H., and Vian, T. (2012). No evidence of the effect of interventions to combat

health care fraud and abuse: A systematic review of the literature. PLoS One, 7(8), e41988. Stemler, S. (2001). An overview of content analysis. Practical Assessment, Research & Evaluation,

7(17). Retrieved from http://pareonline.net/getvn.asp?v=7&n=17 U.S. General Accounting Office. (1996). Content Analysis: A Methodology for Structuring and Analyzing

Written Material. Washington, D.C.: GAO/PEMD. Weber, R.P. (1990). Basic content analysis. (2nd ed.). Sage: Newbury Park, CA. Young, J. (2013, March 11). Florida Medicaid expansion hits another snag. Huffington Post. Retrieved

from http://www.huffingtonpost.com/jeffrey-young/florida-medicaid-expansion-hits_b_2854670.html

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Appendices: Domains v - ix

Appendix K: Summary of Approved Tier-One Milestone Initiatives: New and Enhanced

2012–2013 LIP Tier-One Milestone (STC #84) Approved Initiatives

Implement Patient Centered Medical Home (PCMH) model in 4 partner community health centers or county health departments in Polk County.

Implement Virtual Suite, an electronic health information exchange to expand care coordination in three Jackson Health System (JHS) primary care centers in Miami-Dade County.

Readmission Reduction Program (RRP) – use Transition Coaches who will assist individuals discharged into the community through in-home visits and follow-up calls with the goal of lower ED visits and readmissions.

Enhancing Primary Care Obstetrical Services in Palm Beach County. New primary care service site in Hillsborough County. New discharge clinic for high medical needs patients in Alachua County. New primary healthcare facility in South Hillsborough County. Expansion of primary care medical and dental services in Pinellas County. Satellite primary care clinic at homeless center; create dental clinic and expand dental services at

two health centers; institute an Emergency Department Diversion (EDD) program; expand capacity at maternity clinic, expand women’s health program, and launch national diabetes prevention program at multiple health centers in Palm Beach County.

Establish a dental program in Franklin County. Purchase chronic disease management software module, implement patient centered medical home

(PCMH) model, offer provider education and support, patient education and support, and case management services at three JHS primary care centers in Miami-Dade County.

Open 2 new health center sites for Community Health Services in North Broward County. Add 2 physicians to implement PCMH at 3 primary care sites and 2 pharmacists to increase access to

Coumadin clinics and medication counseling at Tampa General Hospital. Specialty Care Coordination Program at Memorial Hospital West. Capital development project – Jessie Trice Community Health & Wellness Center at Miami Gardens. West Kendall Health Care Center (WKHC). Enhanced Hours for Primary Care, Pediatric Urgent Care, and Dental Services for children in foster

care. Expansion of Obstetric/Gynecological services at Tampa Family Health Centers. Hire 1 FTE Psychiatrist. Expansion of Existing Primary Access to Health clinic in Brevard County. Expansion of Primary Access to Dental clinic in Brevard County. Lake Health Partnership’s North Lake County enhanced primary care services. Expand primary care services to Walton CHD Coastal Branch facility. PCMH pilot project for Diabetes in Alachua County. Enhanced women’s health and pediatric services and expansion of infrastructure with specialty

providers in Liberty and Calhoun counties.

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Appendix L: Summary of Approved Tier-Two Initiatives

2012–2013 LIP Tier-Two Milestone (STC #85), Top 15 Approved Initiatives

The following section is based on the information provided by AHCA in the document titled “Top 15 Facilities Tier 2 Milestone STC #85 List of Initiatives.” The initiatives were grouped into infrastructure development projects, innovation and redesign projects, and population-focused improvement projects, respectively. Jackson Memorial Hospital

Reductions of Surgical Site infections and Other Surgical Complications – Improve safety culture and integrate safety practices into the daily work of a unit or clinical setting, and therefore reduce surgical site infections by 10% by the year 2014.

Reductions of Readmissions – Schedule follow-up appointments on a patient-preferred day, distribute medications for at least 30 days, and send prescription to patient’s preferred pharmacy and have follow-up calls, with goal of reducing readmissions to 10%.

Improve primary care capacity – Adjust the Teaching Residency Model. Increase number of patients seen by 30% (the current no-show rate) by increasing scheduled appointments. Move some of the OB/GYNs to work in Primary Care. Increase Primary Care services at the County Employee Clinics.

Broward General Medical Center

Primary Care Residency Program Expansion – Increase the number of residents being trained for Primary Care. Start a new Pediatric program and rotating internship.

Post-Discharge Support Services – Consists of follow-up calls and appointments for patients with Congestive Heart Failure as they transition from inpatient to home. The plan is to reduce readmission to 21%.

Sickle Cell Day Treatment Program – Start an outpatient Sickle Cell Treatment program. The plan is to reduce admissions by 10%.

Memorial Hospital West

Readmission Reduction Program (RRP) – Use Transition Coaches who will assist individuals discharged into the community through in-home visits and follow-up calls with the goal of lower ED visits and readmissions.

Emergency Department Diversion (EDD) – Upon discharge, Patient Negotiators will educate emergency room (ER) patients who meet certain criteria on the importance of having follow-up appointments and a Medical Home. The Community Health Center in Hollywood will also extend clinic hours.

Acute Care at Home (ACAH) – Treat patients with certain conditions at home rather than in the hospital setting.

Shands At Jacksonville

City Contract Primary Care Redesign – Plan on having all types of health care in one or two locations with the goals of improving access, delivering services in an appropriate setting, changing health seeking behaviors, and reducing cost.

No-show Physician Appointments – Hire employees to call and remind patients of upcoming appointments and identify the patients’ transportation barriers and develop a web page “Catch

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a Caravan to Improved Health.” Reduce no-shows by 10–15% and decrease unnecessary emergency room (ER) visits.

Post Discharge Primary Care Visit – Schedule a follow-up visit at discharge and develop program based on number of patients that attend the follow-up visit; may include after-hour clinic hours, transportation, and patient education.

Memorial Regional Hospital

Acute Care at Home (ACAH) – Treat patients with certain conditions at home rather than in the hospital setting.

Enhancement of Patient-Centered Medical Home at 5 Community Health Centers Operated by Memorial Regional Hospital and Memorial Hospital Pembroke – Expand staff to improve availability and educate the patients. Will also purchase a web-based business intelligence tool (database of patients’ medical data that multiple physicians have access to) to improve care coordination and reduce cost.

Readmission Reduction Program (RRP) – Use Transition Coaches who will assist individuals discharged into the community through in-home visits and follow-up calls with the goal of lower ED visits and readmissions.

Broward Health – Imperial Point Hospital

Readmission Pneumonia Reduction – Create a group of individuals to evaluate the hospital’s processes impacting the readmissions and form a plan to lower readmissions to 18%.

Improve Emergency Department Turn Around Time – A team will work on reducing the average Turn-Around-Time (from the moment the patient is seen to discharge) of 390 minutes by 10%.

Inpatient Fall Reduction – National Patient Safety Goal Performance Improvement Team to evaluate current fall prevention processes and lower the fall rate by 3%.

Halifax Health

Center For Women & Infant Health – Make certain every woman in the Labor & Delivery department sees a physician (24/7 OB), with the goal of lowering non-medically required caesareans and inductions, along with reducing lengths of stay for infants in the neonatal unit.

Congestive Heart Failure Observation Services – Develop a specialized observation protocol for patients with congestive heart failure, with the goal of reducing readmission rates and lengths of stay, thus reducing per-capita cost and increasing effectiveness of care.

Expanded ED Diversion Program – Build a second location for ED medical triage and extend the hours at existing locations with the goal of diverting patients from the ED.

Tampa General Hospital

Practice Connect – Investment in Technology – Provide surrounding physician practices with access to the Electronic Health Record (EHR) system with the goal of increasing the effectiveness of care. Costs related to transcription will be eliminated and the number of unnecessary tests ordered will be reduced.

Primary Care Clinics Extended Hours – Will extend hours in TGH’s Primary Care Clinics to afternoons, weekends, and even offer same-day appointments and walk-ins.

Population Focused Initiative to Reduce Central Line–Associated Blood Stream Infections (CLABSI) in NICU – Join an eight-state collaborative committed to reducing the CLABSI in NICUs. The goal is to reduce the rate by following evidence-based practices and engaging in continuous quality improvement.

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Memorial Hospital Pembroke Acute Care at Home (ACAH) – Treat patients with certain conditions at home rather than in the

hospital setting. Enhancement of Patient-Centered Medical Home at one Community Health Center Operated by

Memorial Hospital Pembroke – Expand staff to improve availability and educate the patients. Will also purchase a web-based business intelligence tool (database of patients’ medical data that multiple physicians have access to) to improve care coordination and reduce cost.

Readmission Reduction Program (RRP) – Use Transition Coaches who will assist individuals discharged into the community through in-home visits and follow-up calls with the goal of lower ED visits and readmissions.

Sarasota Memorial Hospital

mHealth (mobile health) – Trial Use of Mobile Phone Technology to Improve Monitoring of Heart Failure Patients – Familiarize the target group with their mobile devices. Cell phones will be used several times a day to send updates of patients’ blood pressure cuff and external scale to nurse practitioners; nurse practitioner will respond to patients outside of pre-prescribed ranges.

Decrease Average Length-of-Stay (ALOS) for Neonatal Abstinence Syndrome (NAS) Patients – Help infants born to mothers that used illicit or prescription medications while in utero and have developed a dependency. The goal is to more effectively care for these infants by identifying and administering the most appropriate pharmaceutical intervention.

Improve Quality, Safety, and Experience of Care Through Patient-Centered Rounding –Staff will be specially educated and trained to make hourly rounds to check on patients. Sarasota intends for patient-centered rounding to reduce patient reliance on call lights by 10% and to provide proactive care rather than reactive care in the inpatient setting, thus reducing falls and hospital-induced ulcers.

Lee Memorial Hospital

Improve Health Status of Low-Income Patients with Chronic Obstructive Pulmonary Disease (COPD) – Improve the health status for low-income patients with COPD with an evidenced-based plan of care developed and monitored by the assignment of patient to a patient-centered primary care home physician that provides the evaluation, education, care coordination, and support system to best equip patients to manage their disease and improve their quality of life.

Improve Health of Obese Low-Income Population – Improve the health status for low-income patients that are obese (BMI greater than or equal to 30) by initiating a motivational medical program based on proven methods to improve diet and exercise habits and progress to a point where they are no longer obese.

Improve Health of Dual Diagnosis Low-Income Behavioral Health Patients – Improve the health status through appropriate screening for and treating behavioral health issues for low-income patients with dual diagnosis of behavioral health disorder such as depression with comorbid medical conditions.

Broward Health – North Broward Medical Center

Decrease Catheter Associated Urinary Tract Infections (CAUTI) – Increase education on appropriate Foley care and handling for all staff. Daily assessment of Foley catheter need for appropriate discontinuation. Reduce CAUTIs to 2.0/catheter day X 1,000.

Decrease Readmissions in COPD Patient Population – Reduce readmissions through improved patient education about their disease process and lifestyle changes required to reduce acute

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exacerbations; improve patient compliance with appropriate medication usage; and primary care follow-up.

Admitted Patient Flow – Develop a team that analyzes and cuts down on delays associated with the time between the doctor giving the order to admit the patient to when the patient is transported to nurse station for inpatient care. The goal is to reduce time from 152 minutes to 137 minutes.

Memorial Hospital Miramar

Readmission Reduction Program (RRP) – Use Transition Coaches who will assist individuals discharged into the community through in-home visits and follow-up calls with the goal of lower ED visits and readmissions.

Acute Care at Home (ACAH) – Treat patients with certain conditions at home rather than in the hospital setting.

Emergency Department Diversion (EDD) – Upon discharge, Patient Negotiators will educate ER patients who meet certain criteria on the importance of having follow-up appointments and a Medical Home. The Community Health Center in Hollywood will also extend clinic hours.

Broward Health – Coral Springs Medical Center

Mislabeled Specimens – Reduce number of specimens mislabeled. Currently, it is over 1.0/1000 patient days; goal is to reduce it to .3/1000 patient days by DY8.

Admitted Patient Flow from Emergency Department – Improve patient flow by optimizing timing. By expediting the transportation procedure, they hope to reduce time from Emergency Department admission order to patient transport to an inpatient nursing unit from 226 minutes to 200 minutes.

Reducing 30-day Congestive Heart Failure (CHF) Readmissions – Reduce readmissions by 2 percentage points for patients with CHF, as well as prepare the family and patient for discharge.

Indian River Medical Center

Outpatient Heart Failure Clinic – Clinic designed to provide patients with information to modify their lifestyles in order to reduce their symptoms and prevent progression of their disease. The program consists of a team of health providers. The clinic health providers are available to patients 24 hours a day by telephone.

Health Information Exchange – Create an electronic record system that can be accessed by at least 40% of the 200 community physicians; utilize Health Information Exchange in order to improve patient care and patient flow, and reduce healthcare costs by reducing duplicative testing and improving patient flow efficiencies.

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Appendix M: State Specific Quality Measures and Federal Core Measures Detail

Tracking Quality of Care in Hospitals

The data for Quality Measures are based on two quality indicators: readmission and mortality. The data for readmission is based on the 3M methodology for Potentially Preventable Readmissions (PPRs). The Agency for Health Care Administration (AHCA) has pulled three variables from this data: Acute Myocardial Infarction (AMI) readmission, Heart Failure (HF) readmission, and Pneumonia (PN) readmission. The data for mortality is based on the Agency for Healthcare Research and Quality (AHRQ) Inpatient Quality Indicators (IQIs) for 2010. All of the facilities were included that were shown on Florida Health Finder, as well as statewide information. For the mortality data, AHCA has pulled three variables: AMI without a transfer mortality, CHF mortality, and PN mortality. Potentially Preventable Readmissions (PPRs) PPRs identify hospitalizations that are the result of patients being readmitted within 15 days of a previous hospitalization for causes that could be a result of a breakdown in the process of care and treatment. For example, this could include a readmission for a surgical wound infection or for a lack of post-admission follow-up such as a prescription not being filled. These readmission hospitalizations are distinguished from a hospitalization in the same time frame for an event that occurred post discharge, such as a broken leg due to trauma. In order to use PPR as a quality indicator, a PPR rate is developed for each individual hospital based on its unique mix of diagnoses and severity of illness (using 3M’s APR-DRGs) within the hospital and is subsequently further refined by age. Two measures are created, an observed rate and an expected rate. The observed rate is the actual rate at a given hospital calculated for each condition or procedure at a given level of severity. The rate is calculated by dividing the number of hospital discharges for a PPR by the total number of discharges that were at risk for a PPR. The expected rate is the normative rate, or the rate of PPRs that one would expect to see at a given hospital with a given patient mix and overall health status. The expected rate is derived much the same way as the observed rate, but from a group of similarly situated, reference hospitals. Statistically comparing the observed rate with the expected rate, and noting statistically significant differences, provides a means of tracking the general level of quality of care for inpatient stays at a given hospital. Via AHCA’s Florida Center for Health Information and Policy Analysis, Florida is tracking and measuring readmission rates for AMI, CHF, and PN. More information is available online at http://www.floridahealthfinder.gov/Researchers/Reference/Methodology/Methodology.aspx#hreadmit

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Inpatient Quality Indicators (IQIs) IQIs are a set of measures using hospital administrative data to reflect quality of care inside hospitals. These IQIs include inpatient mortality for certain procedures and medical conditions; utilization of procedures for which there are questions of overuse, underuse, and misuse; and volume of procedures for which there is some evidence that a higher volume of procedures is associated with lower mortality. As of August 2011, AHRQ had developed 34 separate IQIs. Three of these will be used as quality indicators for Florida Medicaid, including the mortality rates for AMI, CHF, and PN. The rates are calculated for all persons age 18 or older by dividing the number of patient deaths for a patient with a particular diagnosis by the total number of discharges for all patients with that diagnosis. More information on the technical specifications for the individual IQIs may be obtained online at http://www.qualityindicators.ahrq.gov/Modules/iqi_resources.aspx Core Measures The Joint Commission and federal Centers for Medicaid and Medicare Services (CMS) have worked since 2003 to develop a unified set of hospital inpatient quality measures. The aligned set, known as the Hospital Inpatient Quality Core Measures (Core Measures), captures the quality and appropriateness of care given at the point of delivery. The Florida initiative focuses on four areas of care:

Acute Myocardial Infarction (AMI)

Pneumonia (PN)

Heart Failure (HF)

Surgical Care Improvement Project (SCIP) Each area of care has several individual measures that are combined to create composite scores which are known as measurement sets. For example, the individual measures in each area of care include ones such as the Percent of Heart Attack Patients Given Aspirin at Arrival, Percent of Surgery Patients Who Were Given the Right Kind of Antibiotic to Help Prevent Infection, or Percent of Pneumonia Patients Given the Most Appropriate Initial Antibiotic(s). The individual measures in each of the four areas are combined to create a composite score (i.e., measurement set) for each area of care. The Core Measure data is reported as a composite score that includes the scores for each measurement set for the four areas of care. The composite score is calculated by dividing the sum of the numerators of all four of the measures used by CMS in a measure set by the sum of the denominators of all the measures in a measurement set. More information regarding the National Hospital Quality Indicators program is available online at http://www.jointcommission.org/specifications_manual_for_national_hospital_inpatient_quality_measures/ Composite scores by state (including Florida) are available online at http://www.medicare.gov/download/DownloaddbInterim.asp

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Sources

Florida Agency for Health Care Administration. (2007a). Baker, Clay, and Nassau Counties Medicaid

Reform health plan comparison charts [Updated October 2007]. Florida Agency for Health Care Administration. (2007b). Broward County Medicaid Reform aged and/or

disabled health plan comparison chart [Updated October 2007]. Florida Agency for Health Care Administration. (2007c). Broward County Medicaid Reform children and

families health plan comparison chart [Updated October 2007]. Florida Agency for Health Care Administration. (2007d). Duval County Medicaid Reform aged and/or

disabled health plan comparison chart [Updated October 2007]. Florida Agency for Health Care Administration. (2007e). Duval County Medicaid Reform children and

families health plan comparison chart [Updated October 2007]. Florida Agency for Health Care Administration. (2007f). Florida Medicaid Reform Year 1, Final Annual

Report, July 1, 2006 – June 30, 2007. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_Reform/index.shtml

Florida Agency for Health Care Administration. (2008a). Florida Medicaid Reform Year 2, Final Annual

Report, July 1, 2007 – June 30, 2008. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_Reform/index.shtml

Florida Agency for Health Care Administration. (2008b). Previous Medicaid Reform HMO Enrollment

Reports. Retrieved from http://ahca.myflorida.com/MCHQ/Managed_Health_Care/MHMO/med_RF_data_prev.shtml

Florida Agency for Health Care Administration. (2012a). Baker, Clay, Duval, and Nassau Counties

Medicaid Reform aged and/or disabled health plan comparison chart [Updated April 2012]. Florida Agency for Health Care Administration. (2012b). Baker, Clay, Duval, and Nassau Counties

Medicaid Reform children and families health plan comparison chart [Updated April 2012]. Florida Agency for Health Care Administration. (2012c). Broward County Medicaid Reform aged and/or

disabled health plan comparison chart [Updated April 2012]. Florida Agency for Health Care Administration. (2012d). Broward County Medicaid Reform children and

families health plan comparison chart [Updated April 2012]. Florida Agency for Health Care Administration. (2011a). Centers for Medicare & Medicaid Services

special terms and conditions. Retrieved from http://ahca.myflorida.com/medicaid/medicaid_Reform/pdf/CMS_STCs_and_Authorities_12-15-2011.pdf.

313

Florida Agency for Health Care Administration. (2011b). Florida Medicaid Reform Year 5, Annual Report, July 1, 2010 - June 30, 2011. Retrieved from http://www.fdhc.state.fl.us/medicaid/medicaid_Reform/pdf/FL_1115_YR_5_Final_Annual_Report_07-01-10_06-30-11_sent_11-18-11.pdf.

Florida Agency for Health Care Administration. (2012c). Previous comprehensive enrollment reports.

Retrieved from http://ahca.myflorida.com/MCHQ/Managed_Health_Care/MHMO/med_comp_enroll_prev.shtml.

Florida Agency for Health Care Administration. (2012). Florida Medicaid Reform Year 6, Annual Report,

July 1, 2011 - June 30, 2012. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_Reform/pdf/FL_1115_YR_6_Final_Annual_Report_07-01-11_06-30-12.pdf

Florida Agency for Health Care Administration. (2013a). Comprehensive Medicaid Managed Care

Enrollment Reports. Retrieved from http://ahca.myflorida.com/MCHQ/Managed_Health_Care/MHMO/med_data.shtml.

Florida Agency for Health Care Administration. (2013b). Performance management action plan reports:

2008-2012. [Data file]. Phoenix, AZ: Health Services Advisory Group.

Florida Agency for Health Care Administration. (2013c). Baker, Clay, Duval, and Nassau Counties Medicaid Reform aged and/or disabled health plan comparison charts [Updated September 2013].

Florida Agency for Health Care Administration. (2013d). Baker, Clay, Duval, and Nassau Counties

Medicaid Reform children and families health plan comparison charts [Updated September 2013].

Florida Agency for Health Care Administration. (2013e). Broward county Medicaid Reform aged and/or

disabled health plan comparison charts [Updated September 2013]. Florida Agency for Health Care Administration. (2013f). Broward county Medicaid Reform children and

families health plan comparison charts [Updated September 2013].

314

References

Domains i and ii

Agency for Healthcare Research and Quality. (2011). Health plan survey and guidance. Retrieved from

https://cahps.ahrq.gov/surveys-guidance/hp/index.html

Duncan, R. P., Hall, A. G., Brumback, B., Zhang, J., Bell, L. L., Chorba, L. P., Thompson, K., Elliott, K. (2011). Medicaid Reform enrollee satisfaction year two follow-up survey volume 2: Plan type estimates. Gainesville, FL: University of Florida, Department of Health Services Research, Management and Policy

Duncan, R. P., Hall, A. G., Brumback, B., Zhang, J., Chorba, L. P., Bell, L. L., & Tomlin, K. (2011). Medicaid Reform enrollee satisfaction year two follow-up survey volume 3: Race and ethnicity estimates Gainesville, FL: University of Florida, Department of Health Services Research, Management and Policy

Duncan, R. P., Hall, A. G., Brumback, B., Zhang, J., Chorba, L. P., Bilello, L., & Bell, L. L. (2011). Medicaid Reform enrollee satisfaction year two follow-up survey volume 1: County estimates. Gainesville, FL: University of Florida, Department of Health Services Research, Management and Policy.

Duncan, R. P., Hall, A. G., Harman, J. S., McKay, N. L., Lemak, C. H., Landry, A. Y., & Robst, J. (2011). Evaluating Florida’s Medicaid Reform Demonstration Pilot: 2006 - 2011 Summary Report. Gainesville, FL: University of Florida, Department of Health Services Research, Management, and Policy

Florida Agency for Health Care Administration. (2008). Florida Medicaid Reform Year 2, Final Annual Report, July 1, 2007 – June 30, 2008. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_Reform/index.shtml

Florida Agency for Health Care Administration. (2011). Centers for Medicare & Medicaid Services special terms and conditions. Retrieved from http://ahca.myflorida.com/medicaid/medicaid_Reform/pdf/CMS_STCs_and_Authorities_12-15-2011.pdf.

Florida Agency for Health Care Administration. (2012). Florida Medicaid Reform evaluation plan: 2011 – 2014. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_Reform/pdf/FL_Medicaid_Reform_Evaluation_Plan_08-09-2012_final.pdf

Harman, J. S., & Duncan, R. P. (2009). An analysis of Medicaid expenditures before and after implementation of Florida's Medicaid Reform pilot demonstration Gainesville, FL: University of Florida, Department of Health Services Research, Management, and Policy.

Harman, J. S., Hall, A. G., Lemak, C. H., & Duncan, R. P. (2013). Do Provider Service Networks result in lower expenditures compared with HMOs or Primary Care Case Management in Florida's Medicaid program? Health Services Research, n/a-n/a. doi: 10.1111/1475-6773.12129

315

Harman, J. S., Lemak, C. H., Al-Amin, M., Hall, A. G., & Paul Duncan, R. (2011). Changes in Per Member Per Month Expenditures after Implementation of Florida's Medicaid Reform Demonstration. Health Services Research, 46(3), 787-804. doi: 10.1111/j.1475-6773.2010.01226.x

Domain iii

Agency for Healthcare Research and Quality. (2013). Prevention quality overall composite: Technical specifications. Rockville, MD: Retrieved from http://www.qualityindicators.ahrq.gov/Downloads/Modules/PQI/V45/TechSpecs/PQI%2090%20Prevention%20Quality%20Overall%20Composite.pdf.

Centers for Medicare & Medicaid Services. (2011). Centers for Medicare & Medicaid Services special terms and conditions. Retrieved from http://ahca.myflorida.com/medicaid/medicaid_Reform/pdf/CMS_STCs_and_Authorities_12-15-2011.pdf

Centers for Medicare & Medicaid Services. (2013). Approved 1115 MMA Waiver Amendment, June 14, 2013. Retrieved from http://www.fdhc.state.fl.us/Medicaid/statewide_mc/index.shtml#FCA

Duncan, R. P., Hall, A. G., Harman, J. S., McKay, N. L., Lemak, C. H., Landry, A. Y., & Robst, J. (2011). Evaluating Florida's Medicaid Reform Demonstration Pilot: 2006 – 2011 Summary Report. Gainesville, FL: University of Florida, Department of Health Services Research, Management, and Policy

Florida Agency for Health Care Administration. (2008). Florida Medicaid Reform Year 2, Final Annual Report, July 1, 2007 – June 30, 2008. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_Reform/index.shtml

Florida Agency for Health Care Administration. (2012). Florida Medicaid Reform evaluation plan: 2011 – 2014. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_Reform/pdf/FL_Medicaid_Reform_Evaluation_Plan_08-09-2012_final.pdf

Harman, J. S., & Duncan, R. P. (2009). An analysis of Medicaid expenditures before and after implementation of Florida's Medicaid Reform pilot demonstration Gainesville, FL: University of Florida, Department of Health Services Research, Management, and Policy.

Harman, J. S., Hall, A. G., Lemak, C. H., & Duncan, R. P. (2013). Do Provider Service Networks result in lower expenditures compared with HMOs or Primary Care Case Management in Florida's Medicaid program? Health Services Research, n/a-n/a. doi: 10.1111/1475-6773.12129

Harman, J. S., Lemak, C. H., Al-Amin, M., Hall, A. G., & Paul Duncan, R. (2011). Changes in Per Member Per Month Expenditures after Implementation of Florida's Medicaid Reform Demonstration. Health Services Research, 46(3), 787-804. doi: 10.1111/j.1475-6773.2010.01226.x

Domain iv

Agency for Health Care Administration (AHCA). (n.d., a). Florida Medicaid reform pilot. Retrieved from

http://www.fdhc.state.fl.us/medicaid/medicaid_reform/.

316

Agency for Health Care Administration (AHCA). (n.d., b). Comprehensive Medicaid Managed Care Enrollment Reports. Retrieved from http://ahca.myflorida.com/mchq/Managed_Health_Care/MHMO/med_data.shtml

Agency for Health Care Administration (AHCA). (September 1, 2013). 2012 - 2015 Health Plan Model

Contract Attachment II – Core Contract Provisions. Retrieved from http://ahca.myflorida.com/mchq/managed_health_care/MHMO/docs/contract/1215_Contract/Sept2013/SEP2013ContractAttII-AMEND-CLEAN.pdf

Agency for Health Care Administration (AHCA) and Attorney General of the State of Florida. (2012).

The state’s efforts to control Medicaid fraud and abuse FY 2011-2012. Retrieved from http://edocs.dlis.state.fl.us/fldocs/ahca/medicaid/fraud/ar/2011-2012.pdf

Agency for Health Care Administration (AHCA) and Attorney General of the State of Florida. (2013).

The state’s efforts to control Medicaid fraud and abuse FY 2012 – 2013. Retrieved from http://edocs.dlis.state.fl.us/fldocs/ahca/medicaid/fraud/ar/2012-2013.pdf.

Busch, R. (2012). Healthcare fraud: Auditing and detection guide. Hoboken, New Jersey: John Wiley &

Sons, Inc. Centers for Medicare and Medicaid Services (CMS). (2000). Guidelines for Addressing Fraud and Abuse

in Medicaid Managed Care. U.S. Health Care Financing Administration's (now the CMS) National Medicaid Fraud and Abuse Initiative. Washington, D.C.: CMS.

Centers for Medicare and Medicaid Services (CMS). (2002). Guidelines for Constructing a Compliance

Program for Medicaid Managed Care Organizations and PrePaid Health Plans. Medicaid Alliance for Program Safeguards. Atlanta, GA: CMS.

Centers for Medicare and Medicaid Services (CMS). (2011a). CMS waiver authorities and special terms

and conditions. Retrieved from http://www.fdhc.state.fl.us/medicaid/medicaid_reform/. Centers for Medicare and Medicaid Services (CMS). (2011b, July). CMS fraud prevention initiative.

Retrieved from http://www.cms.gov/Outreach-and-Education/Outreach/Partnerships/downloads/BackgrounderFraudPreventionInitiative.pdf.

Centers for Medicare and Medicaid Services (CMS). (2012, November). Medicare fraud & abuse:

Prevention, detection, and reporting. Retrieved from: https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/Fraud_and_Abuse.pdf.

Cheung-Larivee, K. (2012, June 14). CMS: Medicaid fraud audits ‘disappointing’. FierceHealthcare.

Retrieved from http://www.fiercehealthcare.com/story/cms-medicaid-fraud-audits-disappointing/2012-06-14.

Dedoose Version 4.5, web application for managing, analyzing, and presenting qualitative and mixed

method research data (2013). Los Angeles, CA: SocioCultural Research Consultants, LLC (www.dedoose.com).

317

Driscoll, D., Appiah-Yeboah, A., Salib, P. and Rupert, D. (2007). Merging qualitative and quantitative data in mixed methods research: How to and why not. Ecological and Environmental Anthropology, 3(1), 19-28.

Dudek, E. (2012, January 24). AHCA press release: Secretary Dudek, Attorney General Bondi release

annual Medicaid fraud report. Retrieved from http://www.fdhc.state.fl.us/Executive/Communications/Press_Releases/archive/docs/2012/fraud_and_abuse_press_release1-24-12.pdf.

Federal Bureau of Investigation (2010). Rooting out health care fraud is central to the well-being of

both our citizens and the overall economy. Published 4/16/2010. Retrieved from http://www.fbi.gov/about-us/investigate/white_collar/health-care-fraud.

Goldman, T.R. (2011). Health policy brief: Eliminating fraud and abuse. Health Affairs, July 31, 2011. King, N. (1998). Template Analysis. Qualitative Methods and Analysis in Organizational Research. G.

Symon and C. Cassell. London: Sage Publications. Lieber, E. (2009). Mixing qualitative and quantitative methods: Insights in design and analysis issues.

Journal of Ethnographic & Qualitative Research, 3, 218-227. Lieber, E. (2012). Dedoose: Great Research Made Easy. Retrieved from

http://wiki.dedoose.com/index.php/Main_Page. Medicaid & Public Assistance Fraud Strike Force. (October, 2013). Strike Force Annual Report.

Retrieved from http://www.flstrikeforce.com/Documents/Strike-Force-Annual-Report-2013(3).pdf

Morris, L. (2009). Combating fraud in health care: An essential component of any cost containment

strategy. Health Affairs, 28(3), 1351-1356. National Association of Medicaid Fraud Control Units (NAMFCU). (2012). What is Medicaid fraud?

Retrieved from http://www.namfcu.net/about-us/what-is-medicaid-fraud. National Health Care Anti-Fraud Association (NHCAA). (n.d.). The challenge of health care fraud.

Retrieved from http://nhcaa.org/resources/health-care-anti-fraud-resources/the-challenge-of-health-care-fraud.aspx.

National Health Care Anti-Fraud Association (NHCAA). ( 2013a). Health care fraud – Everyone pays.

Published 3/1/2013. Retrieved from http://nhcaa.org/news/health-care-anti-fraud-everyone-pays.aspx.

National Health Care Anti-Fraud Association (NHCAA). ( 2013b). Health care fraud and organized

criminal groups. Published 3/13/2013. Retrieved from http://nhcaa.org/news/health-care-fraud-and-organized-criminal-groups.aspx.

Office of the Inspector General. (n.d.). Medicaid Fraud Control Units – MFCUs. Retrieved from

https://oig.hhs.gov/fraud/medicaid-fraud-control-units-mfcu/index.asp.

318

Office of the Inspector General. (September, 2013). Office of the Inspector General Annual Report FY

2012 – 2013. Retrieved from http://www.fdhc.state.fl.us/Executive/Inspector_General/ Rashidian, A., Joudaki, H., and Vian, T. (2012). No evidence of the effect of interventions to combat

health care fraud and abuse: A systematic review of the literature. PLoS One, 7(8), e41988. Stemler, S. (2001). An overview of content analysis. Practical Assessment, Research & Evaluation,

7(17). Retrieved from http://pareonline.net/getvn.asp?v=7&n=17 U.S. General Accounting Office. (1996). Content Analysis: A Methodology for Structuring and

Analyzing Written Material. Washington, D.C.: GAO/PEMD. Weber, R.P. (1990). Basic content analysis. (2nd ed.). Sage: Newbury Park, CA. Young, J. (2013, March 11). Florida Medicaid expansion hits another snag. Huffington Post. Retrieved

from http://www.huffingtonpost.com/jeffrey-young/florida-medicaid-expansion-hits_b_2854670.html

Domains v – ix

2012-13 General Appropriations Act, 2012-119 Fl. Stat. § 20.04

Billings, J., Zeitel, L., Lukomnik, J., Carey, T. S., Blank, A. E., & Newman, L. (1993). Impact of socioeconomic status on hospital use in New York City. Health Affairs, 12(1), 162-173. doi: 10.1377/hlthaff.12.1.162

Centers for Medicare & Medicaid Services. (2011). Centers for Medicare & Medicaid Services special terms and conditions. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_reform/pdf/CMS_STCs_and_Authorities_12-15-2011.pdf.

Centers for Medicare & Medicaid Services. (2013). Approved 1115 MMA Waiver Amendment, June 14, 2013. Retrieved from http://www.fdhc.state.fl.us/Medicaid/statewide_mc/index.shtml#FCA

Draper, D. A. (2011). Hospital emergency departments: Health center strategies that may help reduce their use. (Report No. GAO-11-643T). Retrieved from the U.S. Government Accountability Office website: http://www.gao.gov/new.items/d11643t.pdf.

Duncan, R. P., Hall, A. G., Harman, J. S., Bell, L. L., & Kinsell, H. S. (2014). Low Income Pool milestone statistics and findings report for DY7: SFY 2012-13. Gainesville, FL: University of Florida, Department of Health Services Research, Management, and Policy.

Florida Agency for Health Care Administration. (2011). Top 15 hospitals as of 11/15/11 Retrieved from http://ahca.myflorida.com/medicaid/medicaid_reform/lip/docs/Top_15_hospitals_as_of_111511.pdf

Florida Agency for Health Care Administration. (2012a). Florida Medicaid Reform Demonstration Low Income Pool tier-two milestones implementation plan. Retrieved from http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CC8QFjAA&url=http%3A%2F%2Fahca.myflorida.com%2Fmedicaid%2Fmedicaid_reform%2Flip%2Fdocs

319

%2FFL_Tier_Two_Milestone_Report_Template_02162012_approved.doc&ei=rjK6UejHD5Dy8ATJroH4Ag&usg=AFQjCNFvhK43Eq7jJh6vVhP1kTcEQveT1w&sig2=puOuDcR3sF1lT3lm0DyCUA&bvm=bv.47883778,d.eWU

Florida Agency for Health Care Administration. (2012b). Florida Medicaid Reform evaluation plan: 2011 - 2014. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_reform/pdf/FL_Medicaid_Reform_Evaluation_Plan_08-09-2012_final.pdf

Florida Agency for Health Care Administration. (2013a). $35 million LIP Primary Care Award applicants. Retrieved from http://ahca.myflorida.com/Medicaid/medicaid_reform/lip/award.shtml

Florida Agency for Health Care Administration. (2013b). Low Income Pool reimbursement and funding methodology. Retrieved from http://ahca.myflorida.com/medicaid/medicaid_reform/lip/docs/fl_lip_reimbursement_and_funding_02012013.pdf.

Harman, J. S., Hall, A. G., & Zhang, J. (2007). Changes in health care use and costs after a break in Medicaid coverage among persons with depression. Psychiatric Services, 58(1), 49-54. doi: 10.1176/appi.ps.58.1.49