january 2017 | vol. 17 | no. 1 hpa resource · 12/27/2016 · 2017 january edition.indd 1...

HPA Resource

Official Publication of the Section on Health Policy & Administration of the American Physical Therapy Association

State-Level Disparities in Caregiver-Reported Unmet Therapy Need Among Infants and Toddlers with Developmental Delay in the United States

The Relationship Between Functional Independence Measure Scores, Socio-Demographics and Site of Discharge for Inpatient Rehabilitation Patients with Stroke at One California Facility

Advocating for a Systems Approach to Enhance Patient Safety in Physical Therapy Practice: A Clinical Commentary

January 2017 | Vol. 17 | No. 1

IN THIS ISSUE: PTJ-PAL / HPA RESOURCE

President's Message

Global Health SIG News

Congratulations Global Health Special Interest Group Chair, Dr. Celia Pechak

Member Services Update: Catalyzing Community Engagement

Nominating Committee Election Report

APTA Payment and Practice Forum Report

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Physical Therapy Journal of Policy, Administration and Leadership

Special Issue: Health Disparities

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2017 January Edition.indd 1 12/21/2016 1:29:35 PM

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HPA The CatalystPresidentIra Gorman, PT, PhD, MSPH [email protected]

Vice PresidentKerry Wood, PT, [email protected]

SecretaryJames Eng, PT, DPT, MS, [email protected]

TreasurerTina Gunaldo, PT, DPT, PhD, [email protected]

EditorEd Dobrzykowski, PT, DPT, ATC, [email protected]

Managing Editor • PTJ-PALSusan Roush, PT, [email protected]

Global Health SIG ChairCelia Pechak, PT, PhD, [email protected]

Technology in Physical Therapy SIG PresidentRobert "Bob" Latz, PT, DPT, [email protected]

Institute for Leadership in Physical Therapy (LAMP)Debora L. Miller, PT, MBA, [email protected]

Section Office2400 Ardmore Boulevard, Suite 302Pittsburgh, PA [email protected]

TABLE OF CONTENTS: PTJ-PAL

State-Level Disparities in Caregiver-Reported Unmet Therapy Need Among Infants and Toddlers with Developmental Delay in the United States ....................................................................................................... J3The Relationship Between Functional Independence Measure Scores, Socio-Demographics and Site of Discharge for Inpatient Rehabilitation Patients with Stroke at One California Facility ....................................... J14Advocating for a Systems Approach to Enhance Patient Safety in Physical Therapy Practice: A Clinical Commentary ............................................. J22

HPA The Catalyst is the Section on Health Policy & Administration, a specialty section of the American Physical Therapy Association.

Articles may be submitted by e-mail attachment to the Section on Health Policy & Administration Office.

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Articles published in HPA Resource/PTJ-PAL are the work of the authors and do not necessarily represent the opinions, research, or beliefs of the Section on Health Policy & Administration of APTA.

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SPECIAL ISSUE ON HEALTH DISPARITIES

In the fall 2015, the Physical Therapy Journal of Health Policy, Administration, and Leadership placed a call for manuscripts to be published in a health disparities special issue. The response was exceptional, with 12 submitted manuscripts. The review process continues for many of the submissions, which has expanded the vision of a special issue to including health disparities articles in each 2017 PTJ-PAL issue. These studies exemplify the unique and diverse ways in which health disparities are being examined by physical therapist researchers. They also provide valuable insight for all physical therapists working with at-risk populations.

The manuscript “State-Level Disparities in Caregiver-Reported Unmet Therapy Need Among Infants and Toddlers with Developmental Delay in the United States” by Dr. Dawn Magnusson and Dr. Beth McManus, included in this issue, is the first of the health disparities articles. We extend our deep appreciation to the authors and manuscript reviewers for their hard work and commitment to moving the body of physical therapy knowledge on health disparities forward.

Sincerely,

Susan E. Roush, PhD., PT & Jennifer G. Audette PhD., PT Co-Managing Editors for Health Disparities Articles

Attention: Future issues of PTJ-PAL will ONLY be mailed to those who OPT-IN to receive a print version. The Resource is transitioning to a separate, electronic-only

edition. Both of these changes support reductions in print expense, paper resources, and timeliness of current information.

Please update the preferences in your profile at www.aptahpa.com.


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Corresponding Author: Dawn Magnusson, School of Medicine, University of Colorado Denver Anschutz Medical Campus, C244 13121 East 17th Avenue, Aurora, CO 80045 | email: [email protected]

Grant Support: Foundation for Physical Therapy, Promotion of Doctoral Studies II Award (Dr. Magnusson); Health Resources and Services Administration of the U.S. Department of Health and Human Services T32HP10004 (Dr. Magnusson); Comprehensive Opportunities in Rehabilitation Research Training (CORRT) K12 Award (K12 HD055931) through the National Institutes of Health (Dr. McManus).

IRB Approval: University of Wisconsin – Madison

Acknowledgments: Dr. Magnusson was supported by a Foundation for Physical Therapy Promotion of Doctoral Studies Award, and the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under HRSA T32HP10004 (NRSA Training for Careers in Pediatric Primary Care Research, $865,647). Dr. McManus was supported by a Comprehensive Opportunities in Rehabilitation Research Training (CORRT) K12 Award (K12 HD055931) through the National Institutes of Health. This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government.a Doctor of Physical Therapy Program, School of Medicine, University of Colorado Denver Anschutz Medical Campus, 13121 East 17th Avenue, Campus Box C244, Aurora, CO 80045; b Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin – Madison, 707 WARF Building, 610 North Walnut Street, Madison, WI 53726 c Department of Health Systems, Management, and Policy; Colorado School of Public Health; 13001 E. 17th Place; Campus Box B119; Aurora, CO 80045

Abstract

Study Design: Secondary analysis of the 2009-10 National Survey of Children with Special Healthcare Needs.

Objectives: Examine state-level differences in unmet need for physical, occupational or speech therapy services among children with developmental delay (DD); Assess whether differences are associated with Part C Early Intervention (EI) eligibility policies.

Background: Part C EI eligibility policies vary considerably across states. Few studies have explored the association between state-level policy variations and child health outcomes.

Methods and Measures: Multi-level logistic regression analyses were conducted. The primary dependent variable was caregiver-reported unmet therapy need. The primary independent variable was state-level EI eligibility policy.

Results: EI eligibility policy explained 15% of the variability in unmet therapy need between states, adjusting for key child and state-level characteristics. Children from states with narrow versus broad EI eligibility policies experienced higher odds of unmet therapy need (aOR 2.44[1.30-3.33]).

Conclusions: Where a child lives is likely associated with unmet need for therapy services among young children with DD. To help ensure that children receive needed therapy services, clinicians should evaluate the coordination of therapy services across systems of care, and advocate for enhanced developmental surveillance of children with DD living in states with narrow EI eligibility policies.

State-Level Disparities in Caregiver-Reported Unmet Therapy Need Among Infants and Toddlers with Developmental Delay in the United StatesDawn M. Magnusson, PT, PhDa,b / Beth McManus, PT, MPH, ScDc


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Introduction

In the United States (U.S.), one in six children under the age of 18 has a developmental delay (DD) character-ized by limitations in their motor, language, cognitive, or behavioral development that impede participation in age-appropriate activities.1 Studies suggest that early provision of physical, occupational, or speech therapy services for infants and toddlers with DD enhances development,2,3 and helps parents feel more confident in their ability to care for and advocate for their children.2,4 Moreover, the U.S. federal government recognizes the benefits of screening children for DD at an early age and increasing the use of therapy services among children with DD.5,6

A primary source for delivery and payment of physical, occupational, and speech therapy services for children under the age of three is Part C of the Individuals with Disability Education Act.7 Part C mandates Early Inter-vention (EI) services and, with federal financial incen-tives, authorizes states to develop comprehensive and multidisciplinary community-based services for infants and toddlers with, or those who are at risk for, DD (i.e., due to specified medical conditions or adverse social circumstances).8 These services are separate from hospi-tal or clinic-based outpatient therapy services. States are encouraged to establish their own policies for determin-ing EI eligibility, and these policies vary considerably.9 Consequently, the proportion of children eligible for and using EI also varies among states.10-12 Children living in states with narrow, as opposed to broad, EI eligibility policies, and children living in states where eligibility policies have narrowed over time are less likely to use EI than children living in states with broad eligibility policies.11,12 Moreover, these state-level policy differences appear to disproportionately affect certain groups of chil-dren. For example, children who are poor11 or who have more severe functional limitations12 and live in states with narrow EI eligibility policies are less likely to receive EI than similar children living in states with broad eligi-bility policies.

Given that EI is a common source of therapy for infants and toddlers with DD, and that narrow EI eligibility policies are associated with lower EI use, EI eligibility is a potential policy lever that can be used to mitigate disparities in access to therapy services for this vulnerable population. Yet, the extent to which EI eligibility poli-cies influence unmet need for therapy services is un-known. Despite a federal mandate for EI, many children with DD under the age of three experience unmet needs for therapy services, with much of this burden falling disproportionately among black and Hispanic chil-dren.13,14 It is unknown whether a similar burden exists among children living in states with more restrictive EI

eligibility policies.

Although EI is a common source of community-based physical, occupational, and speech therapy services for infants and toddlers, families may seek outpatient services in addition or as an alternative to EI services. Understanding the complex dynamic between state-level EI eligibility policies and unmet therapy need could have more far-reaching clinical and programmatic implica-tions. For example, research suggests that high-risk infants with DD are more likely to receive a referral to clinic or hospital-based therapy services than to commu-nity-based EI services. The reasons for this are not well understood (i.e., lack of provider familiarity with EI).15 A positive association between narrow EI eligibility poli-cies and greater unmet need for therapy would suggest that EI eligibility policies might indirectly influence pro-vider behaviors and children’s use of outpatient therapy services, and perhaps serve as a marker for the robust-ness of systems of care for high-risk infants and toddlers within a state.

The first aim of this study was to examine the extent to which unmet therapy need varied across states. The second aim was to assess whether state-level disparities in unmet need for therapy services were associated with EI eligibility policies. It was hypothesized that unmet therapy need would be higher in states with narrow, versus broad, EI eligibility policies.

Methods

Data Source, Population, and Study Sample

Data were obtained from the 2009-10 National Survey of Children with Special Healthcare Needs (CSHCN), a random digit-dial telephone survey funded by the Maternal and Child Health Bureau (MCHB) within the Health Resources and Services Administration, and con-ducted by the Centers for Disease Control and Preven-tion’s National Center for Health Statistics. The survey utilizes the State and Local Area Integrated Telephone Survey mechanism reference to produce a nationally representative cross-sectional sample of non-institution-alized CSHCN in the United States who are younger than 18 years.

The MCHB has defined CSHCN as having or being “at an increased risk for a chronic physical, developmental, behavioral, or emotional condition, and who also require health and related services of a type or amount beyond that required by children generally” (p138).16 To be included in the national sample of CSHCN, children aged 0-17 years were screened for special healthcare needs using a validated screening tool.17 Primary caregiv-ers responded in the affirmative that the child had one or more criteria associated with a chronic health condi-


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tion: ongoing need for prescription medications; need for more medical, mental health or educational services than is usual for most children of the same age; limited ability or inability to do the things most children of the same age can do; need for physical, occupational, or speech therapy; and/or having any emotional, develop-mental, or behavioral problem that requires treatment or counseling. Of the 372,698 children from 196,195 households screened between 2009 and 2010, 40,242 were categorized as having a special healthcare need and whose caregiver completed the full interview. Additional information regarding survey methodology has been previously described.18 The current study sample was limited to children from birth through two years with a caregiver-reported need for therapy services (n=748). A formal review and exemption for this secondary data analysis were obtained from the University of Wisconsin – Madison Institutional Review Board.

Measures

Dependent Variable. The primary outcome variable of interest was caregiver-reported unmet need for physical, occupational, or speech therapy services (among children with caregiver-reported therapy needs). Previous studies suggest that caregiver report can be a valid measure of a child’s need for and utilization of healthcare services.19 Children were classified as having therapy needs if they had a positive response to the following question: “Dur-ing the past 12 months/since [his/her] birth, was there any time when [child’s name] needed physical, occu-pational, or speech therapy?” Children with reported therapy needs were classified as having their therapy needs met or unmet based on responses to the following question: “Did [child’s name] receive all the therapy that he/she needed?”

Child and Family Characteristics. Individual-level covari-ates were identified according to the behavioral model of health services utilization for vulnerable popula-tions,20 and included functional need, predisposing, and enabling healthcare factors. These are presented in Table 1. Functional need factors included the number of caregiver-reported functional difficulties and condition stability. Predisposing factors included the child’s age, gender, race/ethnicity, and parental education. Enabling healthcare factors included urbanicity, family income, whether or not the child was continually covered by insurance over the past year, whether the child had a per-sonal doctor or nurse, and whether the child used Part C EI services in the past year.

State Characteristics. EI eligibility policies for each state were categorized as broad, moderate, or narrow, accord-ing to the Office of Special Education Programs (OSEP) 2010 designations, as presented in Table 2.21 Because the overall economic prosperity and global ratings of

child health for each state are likely associated with our primary dependent variable (unmet therapy need) and independent variable (EI eligibility policy), these mea-sures were adjusted for in subsequent analyses. First, state-level unemployment rates were included for 2010 from the U.S. Bureau of Labor Statistics,22 reported in Table 3. Second, state child well-being rankings were included for 2010 from the Annie E. Casey Foundation “Kids Count” data book,23 reported in Table 4.

Analytic Approach

Descriptive statistics for the study variables were calcu-lated, including the average rate of unmet therapy need for each state. These analyses were conducted in SAS (Statistical Analysis System version 9.4, SAS Institute Inc., Cary, NC) to accommodate the complex sampling design of the NS-CSHCN.24

Large-scale survey data, such as the NS-CSHCN, em-ploy complex sampling plans that produce non-indepen-dent observations (i.e., observations within clusters tend to be more alike than observations between clusters).24 Failure to account for clustering of data can produce biased results. Multi-level models help researchers to overcome this problem by accounting for the clustered nature of the data and allowing researchers to investigate individual and contextual (e.g., state-level) influences on health outcomes. Therefore, to account for clustering of children within states, and to explore the influence of individual-level characteristics and state-level policies on state-level variability in unmet therapy need, four multi-level random intercept logistic regression models were fit to the data:

Model 1 – the null model, included no covariates to estimate state-level variability in unmet therapy need;

Model 2 – included individual-level covariates to estimate the state-level variability in unmet therapy need, adjusting for child and family characteristics;

Model 3 – additionally included state-level covari-ates; and

Model 4 – additionally included state-level Part C EI eligibility policies.

For each multi-level model, an estimate of the state-level variability in unmet therapy need, and the proportion of state-level variability explained by individual-level factors (Model 2) and state-level factors (Models 3-4) are reported. Because of the relatively small within-state sample sizes, only individual-level covariates associ-ated with the dependent variable in bivariate analy-ses (p<0.10) were included in order to produce more parsimonious models. Multi-level models were fit using


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Functional Need Factors

Number of Functional Difficulties

Children categorized as having a functional difficulty reportedly had “a little” or “a lot” of difficulty in their gross motor development, fine motor development, communication, cognition, or self-help skills. The number of caregiver-reported functional difficulties were summed for each child and included as a continuous measure in logistic regression models.

Condition Stability Child’s healthcare needs were categorized as being stable, changing once in a while, or changing all the time over the past year.Predisposing Factors

Child Age Continuous measure in logistic regression modelsGender Female; male

Race/Ethnicity Non-Hispanic White; Non-Hispanic Black; Hispanic; Other (Asian, American Indian, Alaska Native, Native Hawaiian, Pacific Islander)

Parental Education ≤High School Graduate; Some CollegeEnabling Healthcare Factors

Urbanicity Child lived inside or outside of a metropolitan statistical area (MSA)Family Income <100%; 100% to <200%; 200% to <400%; 400% or more of the Federal Poverty LevelInsurance Coverage Whether child was covered by insurance continuously for the past yearPersonal Provider Whether child had a personal doctor or nurseEI Service Use Whether child received Part C IDEA* Early Intervention (EI) over the past year*IDEA: Individuals with Disabilities Education Act

Table 1: Child and Family Characteristics

Table 2: State Part C Early Intervention Eligibility Categories and Descriptions, 201021

Broad Eligibility Moderate Eligibility Narrow Eligbility

At risk for delayAtypical development1 SD* < mean in 1 area25% delay in 1 area20% delay in 2 or more areas

1.5 SD < mean in one area1.3 SD < mean in 2 or more areas30% delay in 1 area25% delay in 2 or more areas

1.75-2 SD < mean in 1 area1.5 SD < mean in 2 or more areas40-50% delay in 1 area33% delay in 2 or more areas

Alabama Idaho Alaska

Arkansas Illinois Arizona

Colorado Indiana California

Delaware Kansas Connecticut

Hawaii Louisiana District of Columbia

Iowa Massachusetts Florida

Maryland Minnesota Georgia

Michigan Nebraska Kentucky

Mississippi New Hampshire Maine

New Mexico North Jersey Missouri

Pennsylvania North Carolina Montana

Texas Ohio Nevada

Vermont Rhode Island New York

Virginia South Dakota North Dakota

Washington Tennessee Oklahoma

Wisconsin Utah Oregon

West Virginia South Carolina

Wyoming


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Table 3: State Unemployment Rates, 201022

State Unemployment Rate State Unemployment Rate

North Dakota 4.1 Maine 8.1Nebraska 4.7 Connecticut 8.2South Dakota 5.2 Massachusetts 8.2Montana 6.0 New York 8.4Iowa 6.2 Wisconsin 8.7New Hampshire 6.2 New Jersey 9.0Wyoming 6.3 Mississippi 9.4Louisiana 6.6 Missouri 9.4Oklahoma 6.7 Washington 9.4New Mexico 6.8 District of Columbia 9.7

Hawaii 6.9 Alabama 9.8

Vermont 6.9 Arizona 9.8Virginia 6.9 Georgia 9.8Kansas 7.1 Illinois 10.0Maryland 7.4 Ohio 10.2Arkansas 7.5 Kentucky 10.3Idaho 7.5 Florida 10.4Texas 7.5 Indiana 10.4West Virginia 7.6 North Carolina 10.4Alaska 7.7 Tennessee 10.5Utah 7.8 Rhode Island 10.9Delaware 7.9 Oregon 11.1Pennsylvania 7.9 California 11.3Minnesota 8.0 South Carolina 11.5Colorado 8.1 Nevada 11.7

Michigan 13.4


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Table 4: Annie E. Casey Foundation "Kids Count" Rankings, 201023

Rankings

High Medium Low

New Hampshire South Dakota North CarolinaVermont Washington OklahomaMassachusetts Idaho West VirginiaMinnesota Colorado FloridaNew Jersey Delaware TennesseeNorth Dakota Illinois ArkansasIowa Ohio CaliforniaNebraska Hawaii TexasConnecticut Rhode Island Georgia

Maryland Missouri Alabama

Virginia Montana South CarolinaWisconsin New York LouisianaMaine Indiana ArizonaUtah Michigan NevadaWyoming Oregon MississippiKansas Alaska New MexicoPennsylvania Kentucky

Sixteen Key Indicators of Child Well-Being by Domain

Economic Well-Being Education Health Family & Community

• Children in poverty• Children whose parents lack

secure employment• Children living in

households with a high housing cost burden

• Teens not in school and not working

• Children not attending preschool

• Fourth graders not proficient in reading

• Eighth graders not proficient in math

• High school students not graduating on time

• Low-birthweight babies• Children without health

insurance• Child and teen deaths• Teens who abuse alcohol or

drugs

• Children in single-parent families

• Children in families where the household head lacks a high school diploma

• Children living in high-poverty areas

• Teen births

MPlus, a software program that accommodates the com-plex sampling design of the NS-CSHCN, and allows the examination of a sub-sample of CSHCN who are 0 to 2 years old with reported therapy needs. Previously described methods were used to scale the individual-level weights provided in the NS-CSHCN to sum to the cluster sample size.24 As with regular logistic regression, parameter estimates can be transformed into odds ratios. These have a cluster-specific (i.e., state-specific) interpre-tation: the odds of unmet therapy need for a given child/family characteristic, adjusting for state-level differences in unmet therapy need.

Results

Nationally, 37% (weighted n=290,429) of CSHCN who

are 0 to 2 years old needed physical, occupational, or speech therapy services, and 13% of these children had unmet therapy needs. Additional sample characteristics are provided in Table 5. Two-thirds of children with unmet therapy needs had two or more functional limita-tions, and the majority had conditions that were report-ed as stable. Fifty-nine percent of children with unmet therapy needs were 2 years old, and 68% identified as a racial/ethnic minority. The vast majority of children with unmet therapy needs were insured (92%) and had a personal doctor or nurse (88%). Finally, 59% of children with unmet therapy needs did not use EI services in the last year.

The adjusted state estimates for unmet therapy need


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ranged from 0% in several states to 64% in Washing-ton, D.C. Figure 1 provides estimates according to each state’s EI eligibility policy. These estimates were adjusted for children’s number of functional limitations, age, gen-der, race/ethnicity, and parental education.

Table 6 provides the results for the multi-level mod-els. In the null model (Model 1), there was statistically significant variability in unmet therapy need across states (0.922[SE 0.398]). After adjusting for individual-level covariates (Model 2), there remained a statisti-cally significant disparity in unmet therapy need across states (0.921[SE 0.398]). Moreover, the odds of unmet therapy need were higher among non-Hispanic black children, Hispanic children, and children from other racial/ethnic groups as compared to non-Hispanic white children (OR 2.05[1.18-3.55]); among children with-out a personal doctor or nurse compared to children with a personal doctor or nurse (OR 6.13[2.21-17.01]); and among children without EI services compared to children with EI services (OR 2.12[1.31-3.41]). These individual-level estimates were similar in subsequent models. The inclusion of overall state economic prosper-ity (unemployment rates) and child well-being (“Kids Count” rankings) explained a negligible (0%) amount of the residual state-level variability in unmet therapy need (Model 3), and there remained statistically significant variation in unmet therapy need across states (0.919[SE 0.399]). The inclusion of EI eligibility policy explained 15% of the residual state-level variability in unmet ther-apy need (Model 4); children from states with narrow EI eligibility policies experienced higher odds of unmet therapy need than children from states with broad EI eligibility policies (OR 2.44[1.30-3.33]).

Discussion

This secondary data analysis of the NS-CSHCN ex-plored the extent to which unmet therapy need varied across states, and whether state-level differences in unmet therapy need were associated with EI eligibility policies. It was hypothesized that children living in states with narrow EI eligibility policies would experience higher levels of unmet therapy need. Through the use of multi-level models, significant state-level disparities in unmet need for therapy services that were partially explained by state-level variations in EI eligibility poli-cies were found. Moreover, the odds of unmet therapy need were significantly higher for children living in states with narrow versus broad EI eligibility policies. Find-ings also revealed significant racial/ethnic disparities in unmet need for therapy services, with children represent-ing racial/ethnic minorities being more likely than white children to have unmet therapy needs. A discussion of racial/ethnic disparities in unmet therapy need has been previously reported.13,25

This study builds on previous research that revealed significant state-level disparities in the use of Part C EI services,11,12 a primary source of community-based physical, occupational, and speech therapy for children under the age of 3 years with DD. One might expect that children with DD living in states with narrow EI eligibility policies (and not eligible for EI services) could get their therapy needs met in an outpatient setting. For example, in California, a state with narrow EI eligibil-ity policies, high-risk infants with DD are more likely to be referred for outpatient therapy services than for community-based EI services.15 However, the current findings demonstrate that, in general, children living in states with narrow EI eligibility policies were more likely to experience unmet therapy need than children living in states with broad EI eligibility policies.

These findings suggest that children living in states with narrow EI eligibility policies may not be using outpatient services to meet their therapy needs, and that EI eligibility policies likely serve as a marker for the robustness of systems of care for infants and toddlers with DD. To help ensure that young children with DD receive needed therapy services in a timely manner, clini-cians living in states with narrow EI eligibility policies ought to evaluate the coordination of community-based EI and outpatient therapy services, seek to improve com-munication between systems of care for children with DD, and advocate for enhanced developmental surveil-lance of children with DD who are not eligible for EI services. Additional research is required to understand the barriers families face in accessing needed therapy ser-vices for their children, particularly in states with narrow EI eligibility policies, and to inform the development of a standard definition of DD.

There were limitations to this study. First, given the cross-sectional nature of the data, causality cannot be inferred. Second, states with broad EI eligibility policies may have other characteristics that benefit children with DD that were not captured in this study. For example, states receive Title V Maternal and Child Health Services Block Grants to support the health and well-being of women and children, including CSHCN. Funds from these state-level grants are used to design and implement a wide range of programs that reflect each state’s particu-lar needs and priorities. Relatedly, families may go out-of-state for pediatric therapy services or relocate to states with better service coverage. These decisions and actions were not accounted for in the current study. Third, the analytic models assume a uniform rate of unmet need for therapy services within states, which is doubtful. There likely exist important program and community-level dif-ferences, in terms of program structure and implemen-tation, that influence unmet therapy need (e.g., service delivery model or provider availability). Fourth, the NS-CSHCN does not differentiate between community-


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Table 5: Characteristics of Children 0-2 Years Old with Special Healthcare Needs and Caregiver-Reported Therapy Need or Unmet Therapy Need (n=748), United States 2009-2010.

Therapy Need % (Standard Error) Unmet Therapy Need % (Standard Error)

Population Estimate 290,429 38,443Funtional Need

Number of Functional Limitations

0-1 28.5 (2.9) 32.6 (12.0)

2 30.8 (3.2) 32.3 (8.1)

3 15.9 (2.5) 8.8 (4.0)

4 24.8 (2.7) 26.3 (7.9)

Condition Stability

Stable 54.1 (3.2) 56.8 (9.7)

Changes Somtimes 28.9 (3.2) 22.1 (7.1)

Changes Frequently 17.0 (2.3) 21.1 (6.9)

PredisposingAge (Years)

0 18.8 (2.4) 15.1 (5.9)

1 31.6 (3.1) 25.9 (7.0)

2 49.6 (3.3) 58.9 (9.3)

GenderFemale 43.6 (3.3) 52.6 (10.0)

Male 56.4 (3.3) 47.4 (10.0)

Race/EthnicityNon-Hispanic White 49.0 (3.2) 32.4 (7.7)

Non-Hispanic Black 16.5 (2.6) 18.7 (6.3)

Hispanic 26.5 (3.4) 35.5 (12.1)

Other (Asian, American Indian, Alaska Native, Native Hawaiian, Pacific Islander) 8.0 (1.9) 13.4 (5.2)

Parental Education> High School 57.3 (3.4) 49.4 (10.2)

High School Graduate 25.2 (3.2) 42.0 (11.1)

< High School 17.5 (3.0) 8.7 (5.5)

EnablingUrbanicity

Inside MSA 82.7 (2.3) 87.7 (4.1)

Outside MSA 17.3 (2.3) 12.3 (4.1)

Family Income≥400% 16.1 (2.1) 11.0 (3.8)

200%-399% 26.9 (3.0) 46.1 (10.8)

100%-199% 23.4 (2.9) 14.9 (5.2)

<100% 33.7 (3.1) 28.0 (7.9)

Uninsured in Past YearYes 10.7 (2.9) 8.4 (4.7)

No 89.3 (2.9) 91.6 (4.7)

Personal DoctorYes 94.7 (1.1) 88.3 (4.9)

No 5.3 (1.1) 11.7 (4.9)

EI Service UseYes 66.8 (3.1) 41.3 (9.2)

No 33.2 (3.1) 58.7 (9.2)


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based therapy services and those provided in a clinic or hospital. Finally, unmet need for therapy services using the NS-CSHCN was based on caregiver report. Previ-ous research suggests that caregiver report can be a valid measure of a child’s need for and utilization of healthcare services,19 however, important regional and cultural dif-ferences may influence these outcomes at the state level.

Conclusion

Findings from this study indicate that where a young child lives is likely connected to unmet needs for physi-cal, occupational, or speech therapy services, with higher rates of unmet therapy need in states with narrow versus broad EI eligibility policies. To address these state-level disparities and ensure equitable access to therapy services across the country, clinicians ought to evaluate the co-ordination of community-based and outpatient therapy services, seek to improve communication between systems of care for children with DD, and advocate for enhanced surveillance of development in children with DD. In a time when tightening state and local budgets are crafted under the pressure of short-term fiscal con-straints, it is imperative that states continue to contem-plate the long-term effects of their policy decisions for CSHCN.

References

1. Boyle CA, Boulet S, Schieve LA, Cohen RA, Blum-berg SJ, Yeargin-Allsopp M, Visser S, Kogan MD. Trends in the prevalence of developmental dis-abilities in US children, 1997-2008. Pediatrics. 2011;127(6):1034-1042.

2. Berlin LJ, Brooks-Gunn J, McCarton C, McCor-mick MC. The effectiveness of early intervention: examining risk factors and pathways to enhanced development. Prev Med. 1998;27(2):238-245.

3. McManus BM, Carle AC, Poehlmann J. Effective-ness of Part C early intervention physical, occupa-tional, and speech therapy services for preterm or low birth weight infants in Wisconsin, United States. Acad Pediatr. 2012;12(2):96-103.

4. Bailey DB, Jr., Hebbeler K, Spiker D, Scarborough A, Mallik S, Nelson L. Thirty-six-month out-comes for families of children who have disabilities and participated in early intervention. Pediatrics. 2005;116(6):1346-1352.

5. Healthy People 2020. The Office of Disease Preven-tion and Health Promotion website. https://www.healthypeople.gov. Accessed 20 March 2016.

6. Council on Children with Disabilities. Identify-ing infants and young children with developmental

disorders in the medical home: an algorithm for developmental surveillance and screening. Pediatrics. 2006;118:405-420.

7. Individuals with Disabilities Education Act: Part C Early Intervention Statute. United States Depart-ment of Education website. 2004. http://idea.ed.gov/part-c/statutes. Accessed 12 August 2016.

8. Adams RC, Tapia C, and the Council on Children with Disabilities. Early intervention, IDEA Part C services, and the medical home: collaboration for best practice and best outcomes. Pediatrics. 2013;132(4):e1073-1088.

9. Shackelford J. State and jurisdictional eligibility definitions for infants and toddlers with disabilities under IDEA. NECTAC Notes, 21, 43-47.

10. Rosenberg SA, Robinson CC, Shaw EF, Ellison MC. Part C early intervention for infants and tod-dlers: percentage eligible versus served. Pediatrics. 2013;131(1):38-46.

11. McManus B, McCormick MC, Acevedo-Garcia D, Ganz M, Hauser-Cram P. The effect of state early in-tervention eligibility policy on participation among a cohort of young CSHCN. Pediatrics. 2009;124 Suppl 4:S368-374.

12. McManus BM, Magnusson D, Rosenberg S. Re-stricting state Part C eligibility policy is associated with lower early intervention utilization. Matern Child Health J. 2014;18(4):1031-1037.

13. Magnusson D, Palta M, McManus B, Benedict RE, Durkin MS. Capturing unmet therapy need among young children with developmental delay using national survey data. Academic pediatrics. 2016;16(2):145-153.

14. McManus BM, Prosser LA, Gannotti ME. Which children are not getting their needs for therapy or mobility aids met? Data From the 2009-2010 Na-tional Survey of Children With Special Healthcare Needs. Phys Ther. 2016;96(2):222-231.

15. Tang BG, Feldman HM, Huffman LC, Kagawa KJ, Gould JB. Missed opportunities in the referral of high-risk infants to early intervention. Pediatrics. 2012;129(6):1027-1034.

16. McPherson M, Arango P, Fox H, Lauver C, McMa-nus M, Newacheck PW, Perrin JM, Shonkoff JP, and Strickland B. A new definition of children with special healthcare needs. Pediatrics. 1998;102(1 Pt 1):137-140.


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Figure 1: Percent of CSHCN 0-2 Years Old with Caregiver-Reported Therapy Need and Unmet Need by State and Corresponding Part C Eligibility Category, United States 2009-2010.*

FIGURE LEGENDS

Figure 1. Percent of CSHCN 0-2 Years Old with Caregiver-Reported Therapy Need and Unmet Need by State and Corresponding Part C Eligibility Category, United States 2009-2010.*

*Adjusted for the overall composition of the underlying population reflecting child and family characteristics.

AK

AL

AR

AZ

CA

CO

CT

DC

DE FL

GA

HIIA

ID

IL

IN

KS

KY

LA MA

MD

ME

MI MN

MO

MS

MT

NC

ND

NENH

NJ

NM

NV

NY

OH

OK

OR

PA

RI

SCSD

TN

TXUTVA

VTWA

WIWVWY

0

10

20

30

40

50

60

70

30 40 50 60 70 80 90 100

% Unmet Therapy Need

% Therapy Need

*Adjusted for the overall composition of the underlying population reflecting child and family characteristics.

17. Bethell CD, Read D, Stein RE, Blumberg SJ, Wells N, Newacheck PW. Identifying children with special healthcare needs: development and evalua-tion of a short screening instrument. Ambul Pediatr. 2002;2(1):38-48.

18. Blumberg SJ, Welch EM, Chowdhury SR, Upchurch HL, Parker EK, Skalland BJ. Design and opera-tion of the national survey of children with special healthcare needs, 2005-2006. Vital Health Stat 1. 2008(45):1-188.

19. Glascoe FP, Dworkin PH. The role of parents in the detection of developmental and behavioral problems. Pediatrics. 1995;95(6):829-836.

20. Gelberg L, Andersen RM, Leake BD. The behavioral model for vulnerable populations: application to medical care use and outcomes for homeless people. Health Serv Res. 2000;34(6):1273-1302.

21. U.S. Department of Education Offfice of Special Education Programs website. http://www2.ed.gov/about/offices/list/osers/osep/index.html. Accessed April 28 2016.

22. State Unemployment Rates. US Bureau of Labor Statistics website. 2010; http://www.bls.gov/. Ac-cessed 6 April 2016.

23. Kids Count Data Book. Annie E. Casey website. 2010; http://www.aecf.org/~/media/Pubs/Initia-tives/KIDS%20COUNT/123/2013KIDSCOUNTDataBook/2013KIDSCOUNTDataBookr.pdf. Accessed 30 January 2016.

24. Carle AC. Fitting multilevel models in complex survey data with design weights: recommendations. BMC Med Res Methodol. 2009;9:49.

25. Magnusson DM and Mistry KB. Racial and eth-nic disparities in unmet need for pediatric therapy services: the role of family-centered care. Academic Pediatrics. 2016 [Epub ahead of print].


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Table 6: Adjusted Odds Ratios, 95% Confidence Intervals, State-Level Variability (σ2), and Standard Error (SE) of State-Level Variability for Each Logistic Regression Multilevel Model of Unmet Therapy Need Among CSHCN 0-2 Years Old, United States 2009-2010.

Model 1Null Model

Model 2Individual Covariates

Model 3Individual + State Covariates

Model 4Individual + State Covariates + EI Category

Individual-Level CovaritiesRace

Non-Hispanic White REF REF REF

Other 2.05 2.02 2.10

(1.18-3.55) (1.15-3.55) (1.19-3.71)

Parental EducationSome College REF REF REF

High School Graduate 1.17 1.16 1.17

(0.61-2.22) (0.61-2.22) (0.61-2.23)

Less than High School 1.42 1.42 1.43

(0.68-2.31) (0.68-2.31) (0.68-2.31)

Personal Doctor or NurseYes REF REF REF

No 6.13 6.17 6.23

(2.21-17.01) (2.22-17.13) (2.31-16.80)

EI Service UseYes REF REF REF

No 2.12 2.11 2.08

(1.31-3.41) (1.31-3.42) (1.30-3.33)

State-Level CovaritiesUnemployment Rate 1.04 1.01

(0.89-1.22) (0.87-1.19)

Child Well-Being (Tertiles)High REF REF

Medium 1.08 0.93

(0.49-2.39) (0.43-2.00)

Low 1.09 0.94

(0.49-2.42) (0.41-2.13)

EI EligibilityBroad REF

Moderate 1.13

(0.49-2.57)

Narrow 2.44

(1.11-5.32)

State-Level Variability 0.922 0.921 0.919 0.782

SE of State-Level Variability 0.398 0.398 0.399 0.377

Proportion of State-Level Variability Explained by State-Level Factors*

-- 0.0% 0.0% 15.0%

*The formula (σ21-σ

22)/σ

21 was used to calculate the relative contribution of each model to the between-state variance in unmet therapy need,

where σ21 is the state-level variance in Model 1 and σ2

2 is the state-level variance of Model 2.


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The Relationship Between Functional Independence Measure Scores, Socio-Demographics and Site of Discharge for Inpatient Rehabilitation Patients with Stroke at One California FacilityKimberly L. Burdge, PT, MS, DHSc, NCS / Mary Beth Kaylor, PhD, MSN, MPH / Kathleen Mathieson, PhD, CIP

Abstract

Study Design: Retrospective correlational.

Objective: Examine relationships between Functional Independence Measure (FIM) scores, socio-demographics, and site of discharge for patients with stroke in an inpatient rehabilitation facility.

Background: Community living optimizes quality of life, preserving access to friends, family, and leisure activities. To increase community discharges for patients with stroke, rehabilitation professionals must understand factors leading to non-community discharge.

Methods and Measures: FIM and socio-demographics data were collected from the Uniform Data System for Medical Rehabilitation for 181 participants.

Results: Seventy-five percent of all reviewed charts indicated the participant was discharged to a community; 83% of whites, Hispanics, or Latinos, and 67% of blacks/African Americans, Asians, and other minorities were discharged to the community. Mean discharge FIM scores were higher among participants discharged to non-community settings (86.3) than participants discharged to the community (80.1). Only 70% of participants with discharge FIM scores at the optimal cutoff of 78 or greater were discharged to a community, compared to 83% with scores less than 78.

Conclusions: Race/ethnicity was related to site of discharge. Research examining the relationship between race/ethnicity, economic disparities, and family readiness for discharge will determine next steps to increase community discharges after stroke rehabilitation.

Corresponding Author: Kimberly L. Burdge, PT, MS DHSc, NCS, A.T. Still University, College of Graduate Health Studies, 5850 E. Still Circle, Mesa, AZ 85206 | [email protected]

Approval: Approved by Dignity Health IRB on September 17, 2014. Approved by A.T. Still University IRB on December 15, 2014.

A. T. Still University, College of Graduate Health Studies, 5850 E. Still Circle, Mesa, AZ 85206


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Introduction

Each year, more than 700,000 people experience a stroke in the United States,1 and the consequences of this condition grow yearly.2 With an increase in stroke survival rates, the focus of stroke rehabilitation is opti-mizing quality of life, achieving independent functional mobility, and reducing the burden of care.1 Promoting quality of life is a pillar of the overarching goals outlined for Americans in Healthy People 2020.3 Community living for survivors of stroke optimizes quality of life by preserving access to friends, family, and leisure activities. Inpatient rehabilitation facilities (IRFs) are freestand-ing rehabilitation units that provide intensive therapy for patients who can tolerate three hours of therapy per day.4 As primary sites for the delivery of stroke rehabili-tation services, IRFs provide intensive, multidisciplinary services to optimize function and to discharge patients to the community. Community discharge is an impor-tant quality indicator for IRFs, playing a part in internal program evaluation, as well as external accreditation.5

Community discharge for Medicare patients with stroke declined after the Centers for Medicare and Medicaid Services (CMS) established a prospective payment sys-tem (PPS) for IRFs in 2002.6,7 A PPS is a reimbursement system in which Medicare payments are predetermined based on IRF patient assessment instrument (IRF PAI) outcomes. The IRF PAI is a tool that describes the clini-cal characteristics and expected resource needs of each patient. The PPS uses IRF PAI information to catego-rize patients into groups, and payments are calculated for each group.8 Length of stay (LOS) and functional status at discharge were reduced with implementation of the PPS.7 While a PPS may have achieved cost-savings through a reduction in IRF LOS, the cost of care for patients discharged to a medical facility, rather than a community, should be considered.6

The decreased LOS associated with a PPS requires increased efficiency in the application of rehabilitation services for best outcomes. Rehabilitation professionals must understand risk factors that pre-dispose patients for non-community discharge.9 With this knowledge, a re-habilitation team can allocate support resources early in a patient’s IRF stay to improve the chance of a commu-nity discharge. Additionally, if variables predicting site of discharge are identified, rehabilitation can be tailored toward the most likely discharge plan.10

Nguyen et al.11 found patients with higher Functional Independence Measure (FIM)12 motor scores on admis-sion were more likely to be discharged to a community, and that patients who were older, divorced, or separated had dysphagia or cognitive deficits, or those covered by Medicare were more likely to be discharged to a skilled

nursing facility (SNF). These findings build upon evi-dence linking low admission FIM scores, advanced age, and unmarried status to non-community discharge of patients with stroke from IRFs.9, 13-16

Pereira et. al.17 found that younger patients with high admission FIM scores who had severe stroke were more likely to be discharged home than to a long-term care facility, but only if caregivers were available. Lutz9 con-cluded that while admission FIM scores and patient age were related to site of discharge, these variables were not absolute determinants. Caregiver resources and readi-ness played an important part in family decisions about site of discharge. Some young patients with high admis-sion FIM scores were discharged to SNFs due to lack of caregiver resources or readiness, and some older patients with low admission FIM scores were discharged home because caregivers were well-prepared.9

Discharge FIM cut-off points for community discharge, along with admission FIMs and socio-demographic factors, can help clinicians create patient-centered plans of care with a goal of increasing community discharge rates.5,11 Reisetter et. al.5 used a receiver operating char-acteristic curve analysis to demonstrate a discharge FIM score of 78 as the optimal cut-off point for inpatient re-habilitation patients with stroke discharged to a commu-nity versus an institution. The sensitivity and specificity of the cut-off point were equal at 0.77. It is not known why site of discharge outcomes do not always coincide with this cut-off score. Site-specific studies might reveal geographic variations in socio-demographics that influ-ence caregiver readiness for community discharge.

The purpose of this study was to examine relationships between FIM scores, socio- demographics, and site of discharge for patients with stroke after inpatient rehabili-tation in a San Francisco IRF. It was hypothesized that admission FIM scores, discharge FIM scores, and change in FIM scores would be associated with site of discharge. It was also hypothesized that socio-demographics includ-ing age, sex, race/ethnicity, marital status, third-party payer, and pre-admit co-habitation would be associated with site of discharge.

Methods

Design and Setting

This retrospective, correlational study investigated the relationship between FIM scores, socio-demographic factors, and site of discharge for patients in an IRF with a primary diagnosis of stroke. Secondary data from the Uniform Data System for Medical Rehabilitation (UDSMR) records for one 22-bed IRF in San Francisco, California, were used for analysis. The UDSMR, estab-lished in 1987, is a comprehensive rehabilitation data-


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base associated with the University at Buffalo, The State University of New York. The UDSMR, reviewed by Centers for Medicare and Medicaid Services, maintains an outcome measure database for rehabilitation facilities across the globe.18

Participants

Inclusion criteria included initial rehabilitation diag-nosis of stroke (Rehabilitation Impairment Code 01) and discharge from the 22-bed IRF in San Francisco between Dec. 1, 2011, and Nov. 30, 2014. During this time, 287 met these inclusion criteria. Exclusion criteria were discharge in fewer than 10 days or transfer within three days. Due to these exclusion criteria, 106 of the 287 records were deleted from the sample. The analysis proceeded on the remaining 181 patient records. The study was deemed exempt human subjects research by the organization’s institutional review board (IRB) and by A.T. Still University IRB.

Data Collection

Access was granted to the UDSMR database by the IRF PPS Coordinator. Data collection occurred between Jan. 5, 2015, and March 15, 2015. Data collected from the UDSMR database included admission and discharge FIM scores, FIM change scores, primary payer, age, sex, race/ethnicity, marital status, pre-admission co-habitation, and site of discharge. The outcome tool used by UDSMR to capture functional independence and burden of care is the FIM. The FIM, an 18-item instru-ment, is endorsed by the American Academy of Physical Medicine and Rehabilitation as a valid and reliable tool for measuring functional status and describing caregiver burden.12 Specifically, the FIM instrument is reliable for assessing disability and functional outcomes in inpatient rehabilitation when testers are trained and tested.19

Thirteen FIM items cover motor functions, including self-care and functional mobility, and five items cover cognitive functions. Overall scores range from 18 to 126, with higher scores associated with greater independence. Functional Independence Measure scores contribute to the IRF PAI classification of patients into groups based on expected service needs. Prospective payment levels are assigned to each distinct group, which dictate Medicare payments and affect facility reimbursement.8

Facilities that report FIM scores to UDSMR must be credentialed through UDSMR. The credentialing pro-cess requires that 80% of clinicians using the FIM pass the certification exam with a score of 80% or higher. Clinicians must recertify every two years.20

Data Analysis

Frequencies, percentages, means, and standard devia-

tions were used, as appropriate, to describe patient demographics, pre-hospital cohabitation, and payer source. Because age was not normally distributed, a Mann-Whitney U test was used to compare age by dis-charge setting. All other demographic, pre-hospital, and payer variables were nominal and, therefore, chi-square tests were used to evaluate differences for these variables by discharge setting. Admit, discharge, and change in FIM scores were tested for normality using Shapiro-Wilk tests. Admit and changes in FIM scores were normally distributed, so independent t-tests were used to compare mean scores by discharge setting. Because discharge FIM scores were not normally distributed, a Mann-Whitney U test was used to compare discharge FIM scores by dis-charge setting. In addition, means and 95% confidence intervals for admit, discharge, and change in FIM scores were calculated.

Discharge FIM scores were divided into two groups, with scores of 77 and lower in one group and scores of 78 and higher in another group, which is consistent with research defining 78 as a cut-off score between commu-nity and non-community discharge.5 Admission FIM scores were used to divide the sample into four groups based on suggested cut-off scores from past research5,21,22 to describe a range of disability at admission. The groups were 78 or greater, 58 to 77, 38-57, and 37 or below. Participant records were also divided into two groups based on FIM change scores, with scores of 28 and lower in one group, and scores of 29 and higher in another group, to be consistent with regional FIM gain averag-es.23 Differences in these nominal admit, discharge, and change in FIM scores by discharge setting were evalu-ated using chi-square analyses. All statistical testing was two-tailed and p<0.05 was used to determine statistical significance. This is a generous alpha level, but it was deemed appropriate given the exploratory nature of the current research. All analyses were conducted with IBM SPSS Statistics for Windows, version 23.0a.

Results

Records of 181 participants were analyzed. Demo-graphic, pre-hospital co-habitation, and payer variables by discharge setting are presented in Table 1. Of the 181 participant records, 136 (75%) indicated discharge to a community, and 45 (25%) indicated discharge to a non-community setting. Mean age of the sample was 74 years (±11.7) and older adults comprised the majority of the sample, with 140 (77%) older than 65 years. Mean age did not differ significantly by discharge setting, t (df=179) = -1.00, p = .318. The participant records in-dicated the sample was predominantly white (44%) and Asian (40%). An overall analysis of discharge setting by race/ethnicity was not statistically significant, χ2 = 6.22 a IBM Corp., Armonk, N.Y., USA


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(df=4,177), p = .183. However, when race/ethnicity was collapsed into two categories, more whites, Hispanics, or Latinos were discharged to a community (87%), com-pared to blacks/African Americans, Asians, and Native Hawaiians or other Pacific Islanders (67%), χ2 = 6.44 (df=1,181), p = .011. Forty-eight percent of patients were married, 26% were widowed or divorced, and 26% were never married. Most patients (68%) lived alone prior to hospitalization. There were no statistically sig-nificant differences in discharge setting by marital status, χ2 = 0.98 (df=3,181), p = .807, or living alone versus co-habitation prior to hospital admission, χ2 = 0.55 (df=1, 176), p = .456. Primary payer source was missing in 23% of records; among the 39 records remaining, 62% had Medicare Fee for Service and 14% had Medicare

Advantage. There were no statistically significant differ-ences in discharge setting by payer source, χ2 = 0.53 (1, N = 139), p = .466. In summary, the only sociodemo-graphic variable associated with discharge setting was race/ethnicity.

As shown in Figure 1, mean admit FIM scores did not differ significantly by discharge setting, t (179) = 1.29, p = .200. In addition, FIM change scores did not dif-fer significantly by discharge setting, t (179) = 1.46, p = .145. Mean discharge FIM scores for those discharged to non-community and those discharged to community settings were not statistically different with the current sample size (Mann-Whitney U = 2,468.5, p = .06). Fur-ther analyses comparing discharge setting by admit, dis-

Table 1: Descriptive Statistics and Tests for Differences for Demographic, Pre-Hospital Cohabitation, and Payer Variables by Discharge Setting (n=181)

Discharge Setting

VariableCommunity

(n=136)n (s.d)

Non-Community(n=45)n (s.d)

p value

Mean Age ± Standard Deviation 73.64 ± 11.61 71.62 ± 12.07 .321

Sex

Male 63 (79.7) 16 (20.3) .212

Female 73 (71.6) 29 (28.4)

Race/Ethnicity

White 66 (82.5) 14 (17.5) .182

Hispanic 9 (90.0) 1 (10.0)

Black 7 (58.3) 5 (41.7)

Asian 51 (70.8) 21 (29.2)

Pacific Islander 2 (66.7) 1 (33.3)

Marital Status

Married 67 (77.0) 20 (23.0) .812

Never Married 36 (76.6) 11 (23.4)

Divorced 10 (66.7) 5 (33.3)

Widowed 23 (71.9) 9 (28.1)

Pre-Hospital Cohabitation

Lived Alone 91 (74.0) 32 (26.0) .462

Lived with Other(s) 42 (79.2) 11 (20.8)

Primary Payer†

Medicare Fee for Service 86 (76.1) 27 (23.9) .472

Medicare Advantage 18 (69.2) 8 (30.8)

1 Mann-Whitney U test2 Chi-square test†The primary payer variable had 42 cases (23.2%) with missing data. All other variables had <5% missing cases.


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charge, and change FIM score categories are presented in Table 2. Descriptively, and counterintuitively, only 70% of patients with discharge FIM scores at the optimal cut-off of 78 or greater were discharged to community settings, compared to 83% of patients with discharge scores less than the optimal cutoff (p < .05).

Discussion

This study examined relationships between FIM scores, socio-demographics, and site of discharge for patients with stroke after inpatient rehabilitation in a San Fran-cisco IRF. A significant relationship between race/ethnicity and site of discharge was found with whites or Hispanics or Latinos more likely to be discharged to a community than blacks/African Americans, Asians, and native Hawai-ians or other Pacific Islanders. Related literature shows that persons who self-identify as black with stroke are more likely to have diabetes and obesity24 and that these self-identifying blacks with stroke are not likely to have equally good functional outcomes as compared with whites after rehabilitation.25 However, comorbidity indices have not been shown to be good predictors of functional status for patients after stroke in acute care or IRFs,26,27 so it is important to look at other factors that may be associ-ated with race/ethnicity and discharge location. Economic disparities could be one such factor and could affect fami-lies’ abilities to take care of family members at home who are recovering from stroke.

In the San Francisco Bay Area, there is well-documented economic disparity between whites and people of color. In particular, there is a growing population of working poor made up disproportionally of people of color.28 Individu-als with full-time jobs might not have time to care for family members at home. Additionally, those near poverty level might not have the economic resources to afford in-home healthcare. This makes community discharge more difficult for poor individuals with stroke. Future research should focus on the relationship between race/ethnicity, economic resources, and family ability and readiness to care for loved ones discharged from IRFs.

Clinically, the focus of IRF discharge planning should go beyond increasing functional mobility scores and emphasize the preparation of families to care for patients after discharge home. Complications at home can result in readmission if caregivers are unprepared. As IRFs and acute care hospitals collaborate in bundled payment plans, the extra cost associated with re-admission will be a focus for improvement, and CMS has established 30-day re-admission as a quality indicator for IRFs.29 Development of a standardized assessment instrument for family readiness to provide for home discharge might help prepare caregivers and avoid unnecessary complica-tions after discharge that could result in readmission.9

In the current study, mean FIM discharge scores were higher for patients who were discharged to a non- com-munity setting as opposed to a community setting. This is in contrast to findings in earlier studies.5,9,15 Addition-ally, site-of-discharge outcomes did not conform to the accepted discharge FIM cut-off score for community dis-charge. It is important to consider that variation might be unique to a location or geographical setting. Investi-gation of local economics and socio-demographic trends is important for quality improvement. Rehabilitation professionals involved in discharge planning will need to look closely at family/caregiver readiness for discharge in all patients early in their IRF stay. Targeting patients with low FIM scores will not provide optimal results in a setting where site-of-discharge outcomes do not conform with accepted FIM cut-off scores.

Study Limitations

This study was limited by the retrospective nature of the data. The researcher did not have information related to organizational trends or challenges that may have af-fected the data. The generous alpha level (at .05) is also a limitation and might have allowed Type I errors in the statistical analyses. Another limitation is the use of a single IRF site for data collection. Generalizability is limited due to the potential for confounding of results by local economy, culture and practice patterns. How-ever, features of the local economy lead to questions about how economic disparities based on race effect equity in healthcare, laying the groundwork for future studies. Economic disparities unique to San Francisco may contribute to the counterintuitive discharge pattern seen in the IRF in this study. Families who are struggling financially may not have the time or resources to care for a person after stroke rehabilitation, resulting in non-community discharges for patients with FIM scores that normally indicate community discharge.

Conclusion

The results of this study show an association between race/ethnicity and community versus non-community discharge of patients after stroke rehabilitation in a San Francisco IRF. Future studies are needed to examine the relationship between race/ethnicity, economic disparities, family readiness, and site of discharge. Development of a standardized assessment instrument for family readiness to provide for home discharge would be useful for more effective rehabilitation and discharge planning.

References

1. National Institutes of Health, National Institute of Neurological Disorders and Stroke. (2014). Post-stroke rehabilitation fact sheet (NIH Publication No. 14-1846). Retrieved from http://www.ninds.nih.


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gov/disorders/stroke/poststrokerehab.htm. Ac-cessed August 1, 2015.

2. Feigin, V.L., Forouzanfar, M.H, Krishnamurthi, R., Mensah, G.A., Connor, M., Bennett, D.A., Mo-ran, A.E….Murray, C. (2014). Global and regional burden of stroke during 1990-2010: Findings from the Global Burden of Disease Study 2010 [PDF]. The Lancet, 383(9913), 245–255. http://dx.doi.org/10.1016/S0140-6736(13)61953-4.

3. Department of Health and Human Service (2015). Health-related quality of life and well- being. Healthy People 2020. Retrieved from https://www.healthypeople.gov/2020/topics-objectives/topic/health-related-quality-of-life-well-being. Accessed August 1, 2015.

4. Centers for Medicare & Medicaid Services. (2012). In-patient rehabilitation facilities. Retrieved from https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/CertificationandComplianc/Inpatien-tRehab.html. Accessed August 12, 2015.

5. Reistetter, T.A., Graham, J.E., Deutsch, A., Granger, C.V., Markello, S., & Ottenbacker, K.J. (2010). Utility of functional status for classifying community versus institutional discharges after inpatient reha-bilitation for stroke. Archives of Physical Medicine and Rehabilitation, 91, 345–350. http://dx.doi.org/10.1016/j.apmr.2009.11.010.

6. Gillen, R., Tennen, H., & McKee, T. (2007). The impact of the Inpatient Rehabilitation Facility Prospective Payment System on stroke program outcomes. American Journal of Physical Medicine & Rehabilitation, 86(5), 356–363. http://dx.doi.org/ 10.1097/PHM.0b013e31804a7e2f.

7. O’Brien, S.R., Xue, Y., Ingersoll, G., & Kelly, A. (2013). Shorter length of stay is associated with worse functional outcomes for Medicare beneficiaries with stroke. Physical Therapy, 93 (12), 1592–1602. http://dx.doi.org/10.2522/ptj.20120484.

8. Centers for Medicare & Medicaid Services. (2013). Inpatient rehabilitation facility PPS. Retrieved from https://www.cms.gov/medicare/medicare-fee-for-service-payment/inpatientrehabfacpps/index.html. Accessed August 20, 2016.

9. Lutz, B.J. (2004). Determinants of discharge destination for stroke patients. Rehabilita-tion Nursing, 29(5), 154–163. http://dx.doi.org/10.1002/j.2048-7940.2004.tb00338.x.

10. Myamoto, H., Hagihara, A, & Nobutomo, K. (2008). Predicting the discharge destination of reha-bilitation patients using a signal detection approach. Journal of Rehabilitation Medicine. 40, 261-268. http://dx.doi.org/ 10.2340/16501977-0161.

11. Nguyen, V.Q.C., PrvuBettger, J., Guerrier, T., Hirsch, M.A., Thomas, J.G., Pugh, T.M., & Rhoads III, C.G. (2015). Factors associated with discharge to home versus discharge to institutional care after inpatient stroke rehabilitation. Archives of Physi-cal Medicine and Rehabilitation, 96, 1297–1303. http://dx.doi.org/10.1016/j.apmr.2015.03.007.

12. Uniform Data System for Medical Rehabilitation. (2014a). The FIM instrument: Its background, structure, and usefulness. Retrieved from http://www.udsmr.org/Documents/The_FIM_Instru-ment_Background_Structure_and_Useful ness.pdf. Accessed July 30, 2015.

13. Chung, D.M., Niewcsyk, P., DiVita, M., Markello, S., & Granger, C. (2012). Predictors of discharge to acute care after inpatient rehabilitation in severely affected stroke patients. American Journal of Physical Medicine & Rehabilitation, 91(38), 387–392. http://dx.doi.org/10.1097/PHM.0b013e3182aac27.

14. Faulk, C.E., Cooper, N.A., Staneata, J.R., Bunch, M.P., Galang, E., Fang, X., & Foster, A.J. (2013). Rate of return to acute care hospital based on day and time of rehabilitation admission. Physical Medi-cine and Rehabilitation. 5, 757-762. http://dx.doi.org/10.1016/j.pmrj.2013.06.002.

15. Koyama, T., Sako, Y., Konta, M., & Domen Ka-zuhisa. (2011). Poststroke discharge destination: Functional independence and sociodemographic factors in urban Japan. Journal of Stroke and Cere-brovascular Diseases.20(3), 202–207. http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2009.11.020.

16. Nguyen, T-A., Page, A., Aggarwal, A., & Henke, P. (2007). Social determinants of discharge destination for patients after stroke with low admission FIM instrument scores. Archives of Physical Medicine and Rehabilitation, 88, 740–744. http://dx.doi.org/10.1016/j.apmr.2007.03.011.

17. Pereira, S., Foley, N., Salter, K., McClure, J.A., Meyer, M., Brown, J., Speechley, M., & Teasell, R. (2014). Discharge destination of individuals with severe stroke undergoing rehabilitation: A predic-tive model. Disability and Rehabilitation, 36(9), 727–731. http://dx.doi.org/10.3109/09638288.2014.902510.

18. Uniform Data System for Medical Rehabilitation. (2014b). About UDSMR. Retrieved from http://www.udsmr.org/. Accessed July 26, 2015.

19. Hamilton, B.B., Laughline, J.A., Fiedler, R.C., & Granger, C.V. (1994). Interrater reliability of the 7-level functional independence measure (FIM). Scandinavian Journal of Rehabilitation Medicine, 26(3), 115–119.


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20. Uniform Data System for Medical Rehabilitation. (2015). UDSMR services. Retrieved from http://www.udsmr.org/WebModules/UDSMR/Com_Services.aspx. Accessed July 26, 2015.

21. Inouye M, Hashimoto H, Mio T, Sumino K. In-fluence of admission functional status on func-tional change after stroke rehabilitation. Am J Phys Med Rehabil. 2001;80(2):121–25. http://dx.doi.org/10.1097/00002060-200102000-00008.

22. Beninato, M., Gill-Body, K. M., Salles, S., Stark, P. C., Black-Schaffer, R. M., & Stein, J. (2006). Original article: Determination of the Minimal Clinically Important Difference in the FIM Instru-ment in Patients With Stroke. Archives Of Physical Medicine And Rehabilitation, 8732-39. http://doi.org/10.1016/j.apmr.2005.08.130.

23. Uniform Data System for Medical Rehabilitation (2014c). Retrieved from https://www.udsmr.org. Accessed July 26, 2015.

24. Horn, S.D., Deutscher, D., Smout, R.J., DeJong, G., & Putman, K. (2010). Black-white differences in patient characteristics, treatments, and outcomes in inpatient stroke rehabilitation. Archives of Physi-cal Medicine and Rehabilitation, 91(11):1712-21. http://doi.org/10.1016/j.apmr.2010.04.013.

25. Ellis, C., Hyacinth, H.I., Beckett, J., Feng, W., Chimowitz, M., Ovbiagele, B., Lackland, D., & Adams, R. (2014). Racial/Ethnic differences in poststroke rehabilitation outcomes. Stroke Re-

search and Treatment, 2014:950746. http://doi.org/10.1155/2014/950746. Epub 2014 Jun 15.

26. Kumar, A., Graham, J.E., Resnik, L., Karmarkar, A.M., Tan, A., Deutsch, A., & Ottenbacher, K.J. (2016). Comparing Comorbidity Indices to Pre-dict Post-Acute Rehabilitation Outcomes in Older Adults. American Journal of Physical Medicine and Rehabilitation. [Epub ahead of print]. Re-trieved from http://www.ncbi.nlm.nih.gov/pubmed/27149597. Accessed June 22, 2016.

27. Kumar, A., Graham, J.E., Resnik, L., Karmarkar, A.M., Deutsch, A., Tan, A., Snih, A., & Otten-bacher, K.J. (2016). Examining the Association Between Comorbidity Indexes and Functional Status in Hospitalized Medicare Fee-for-Service Benefi-ciaries. Physical Therapy, 96(2):232–40. http//doi.org/10.2522/ptj.20150039. Epub 2015 Nov 12.

28. USC Program for Environmental and Regional Equity. (2015) An equity profile of the San Francisco Bay Area Region. [PDF]. Retrieved from http://www.policylink.org/sites/default/files/documents/bay-area-profile/BayAreaProfile_21April2015_Fi-nal.pdf. Accessed June 22, 2016.

29. Centers for Medicare & Medicaid Services. (2015). IRF quality reporting measures information. Re-trieved from https://www.cms.gov/Medicare/Qual-ity-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html. Accessed August 1, 2015.

Table 2: Discharge Setting by Admit, Discharge, and Change in Functional Independence Measure (FIM) Scores

Discharge Setting

VariableCommunity

(n=136)n (%)

Non-Community(n=45)n (%)

Chi-square Results

Admit FIM

≤ 37 18 (85.7) 3 (14.3)

χ2=3.46 (df=3,181), p=.32638-57 52 (76.0) 16 (23.5)

58-77 58 (69.9) 25 (30.1)

≥ 78 8 (88.9) 1 (11.1)

Discharge FIM

< 78 59 (83.1) 12 (16.9)χ2 =3.96 (df=1,181), p=.033

≥ 78 77 (70.0) 33 (30.0)

FIM Change

<29 82 (78.8) 22 (21.2)χ2 =1.80 (df=1,181), p=.180

≥29 54 (70.1) 23 (29.9)


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Figure 1: Means and 95% Confidence Intervals for Admission FIM Scores, Discharge FIM Scores, and Change in FIM Scores Admission to Discharge by Discharge Setting

100 90 80 70 60 50 40 30 20 10 0

Admission FIM Discharge FIM FIM Change

■ Community □ Non-community


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Abstract

The purpose of this commentary is to bring attention to patient safety in physical therapy, especially to the system factors that contribute to error, and to advocate for improving methods of reducing patient harm by increas-ing focus on a systems approach. Healthcare delivery is a complex undertaking with multiple individuals working to provide the best care in technically challenging envi-ronments. This, combined with human fallibility, makes error inevitable. However, error does not have to result in patient harm. Historically, patient safety efforts in physical therapy have focused on the individual provider. Research shows that most patient harm occurs because of a recurrent set of circumstances and system conditions rather than solely the error of one provider.

Introduction

Healthcare delivery is a complex undertaking with multiple individuals working to provide the best care in technically challenging environments. Individual health-care professions and facilities, with the support of private and public organizations, have made deliberate efforts to improve the quality of patient care within these complex settings. The National Academy of Medicine, formerly the Institute of Medicine (IOM) calls for quality im-provement in six areas of healthcare: safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity.1,2 The physical therapy profession, defined as physical therapists and physical therapist assistants at all levels, has put time and resources into improving some of these quality improvement areas but has paid less attention to other areas, especially the area of patient safety.

Patient safety is not solely about reducing error and in-dividually striving to provide safe and high-quality care. Error is inevitable because humans are fallible. Despite best intentions, every provider in every setting will make mistakes because they are human. While error is inevita-

ble, patient harm is not. Patient harm and injury usually results from a combination of underlying system condi-tions and individual human error. Therefore, patient safety should be about both individual responsibility to reduce error and about creating effective systems and safeguards to prevent inevitable error from reaching the patient and causing harm.3-5 The purpose of this com-mentary is to bring attention to patient safety in physical therapy, especially to the system factors that contribute to error, and to call individuals and organizations within the profession to improve methods of reducing patient harm by increasing focus on a systems approach.

Physical Therapy’s Progress in Quality Improvement

The physical therapy profession has made progress to-wards improving overall quality of patient care in several areas. For example, in 2013, the House of Delegates for the American Physical Therapy Association (APTA) ad-opted a new vision: “Transforming society by optimizing movement to improve the human experience,”6 which speaks to both patient-centered care and effectiveness. Physical therapists have embraced the participation-focused International Classification of Function (ICF) model put forth by the World Health Organization7 and provide patient-centered care by considering factors such as the patient’s values and interests, psychosocial sta-tus, and social support.8,9 The profession has also taken steps to become more effective by enhancing its body of knowledge with the goal of minimizing unwarranted variability in care. Advances include research quality10 as well as improved availability and dissemination of this research in more clinically usable forms (e.g. PTNow.org, Linking Evidence and Practice [LEAP] articles,11,12 Clinical Practice Guidelines,13-16) and increased use of filtered resources (e.g. www.UpToDate.com and www.ClinicalKey.com). As entry-level education has evolved, physical therapists are more prepared than ever to un-derstand evidence-based practice, to critically appraise

Advocating for a Systems Approach to Enhance Patient Safety in Physical Therapy Practice: A Clinical CommentaryTamara S. Struessel, PT, DPT, OCS, MTCa / Jennifer W. Rodriguez, PT, MHS1a / Chelsea R. Van Zytveld, PT, DPTa,b

Corresponding Author: Tamara S. Struessel, PT, DPT, OCS, MTC, University of Colorado, Anschutz Medical Campus, Physical Therapy Program, 13121 E. 17th Street, Aurora, CO 80045 | email: [email protected] a University of Colorado, Anschutz Medical Campus, Physical Therapy Program, Aurora, COb South Valley Physical Therapy, Denver, CO


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articles, and to question their own and others’ practice. More and more clinicians are seeking further training and specialization through residencies, fellowships, and certifications17 to improve their skills and provide more effective care to their patients.

The physical therapy profession is to be lauded for this commitment to improving the quality of patient care. Physical therapy as a profession, however, has lagged behind other healthcare professions in addressing patient safety. This is evidenced by the limited research pub-lished on safety in physical therapy practice compared to the literature available regarding safety in other health-care professions. Other healthcare providers and organi-zations have conducted extensive research in the area of patient safety, producing numerous systemic reviews and meta-analyses addressing error in specific areas of prac-tice.18-27 Not only have error reduction processes been proposed, these processes have been extensively studied and refined to support their effectiveness at reducing patient harm. For example, the profession of nursing has published studies and systematic reviews on processes to reduce patient harm related to medication admin-istration,18-21 shift changes,22,23 staffing,24,25 and facility design.26,27 Similarly, interdisciplinary teams have devel-oped surgical protocols and checklists to ensure correct person, procedure, and site.28-36

In contrast, the physical therapy profession has pro-duced comparatively limited research on patient safety. These publications are of relatively low levels of evidence and focus on general patient safety topics. In 1992, Deusinger37 described the severity of harm caused by individual mistakes and proposed three dimensions of clinical competency: action, error, and consequence. In 2009, Cochran and colleagues38 conducted focus groups with experienced physical therapists to identify com-mon errors and their causes. Two prospective reports39,40 called the profession to pay more attention to patient safety and physical therapy error, while error reduction strategies and methods have been suggested in other articles.41,42 Physical therapists have taken some posi-tive interprofessional steps by collaborating with other healthcare professionals to publish literature on patient safety.43-48 While there is clearly a paucity of literature on patient safety and error in physical therapy, it is unclear if the lack of published research reflects a lack of atten-tion to patient safety at the clinical level, or simply a lack of publications on the topic.

Error and Patient Safety in Physical Therapy

There may be limited awareness of what is considered an error in physical therapy practice and a misconcep-tion that physical therapy practice is lower risk because therapists’ actions appear less life-threatening than those of other medical professionals.38,39 While many defini-

tions of error exist,49 Reason50 defines error as “the failure of a planned action to be completed as intended (i.e. error of execution) or the use of a wrong plan to achieve an aim (i.e. error of planning).” The authors of this paper propose the following definition: “An error is any act of commission or omission that has the potential to cause patient injury” in both clinical and research contexts. This definition includes both action and inaction and patient harm and potential for patient harm, and in-cludes errors related to diagnosis, treatment, prevention, and others such as equipment failure.5,40

Serious and life-threatening error can and does occur in physical therapy settings.38 Errors result in patient safety events,5 which can be classified on a continuum ranging from a minor near-miss to a major event depending on the severity of harm that occurs. Definitions and exam-ples of points on this continuum are provided in Figure 1.51,52 While often overlooked, a near-miss incident is still considered a patient safety event. The lack of per-manent or serious harm may merely be because the error was caught before injury occurred, stopped by a system safeguard, or simply due to good luck.5,51-53

Comprehensive data on error and patient safety event rates in physical therapy practice do not exist. Physi-cal therapists are included in Agency for Healthcare Research and Quality (AHRQ) survey data on safety issues in healthcare settings and teams where some physical therapists work.54 This agency has compiled a list of multiple journal articles showing that error oc-curs regularly in these healthcare settings; however, the data lacks specificity and combines physical therapists with diverse “direct care providers,” such as podiatrists, dieticians, pharmacists, and chaplains.54 Additionally, many physical therapists practice in outpatient or other settings not represented or minimally represented in the AHRQ surveys. This further confounds use of this data in a meaningful way when looking broadly at error rates across the profession.55

Data on physical therapy malpractice rates is avail-able,56,57 and it is tempting to look at malpractice rates as a surrogate for rates of patient safety events. But malpractice and patient safety events are not synony-mous. Malpractice is influenced by who files malprac-tice claims, for what events, and the legal complexity of how they are resolved.58 Therefore, malpractice data are not an accurate reflection of incidence patient safety events.56,59 Physical therapists may also avoid discuss-ing and reporting errors because they fear malpractice.40 Additionally, it can be argued that solely focusing on malpractice inhibits further advancement in the field of patient safety because it emphasizes individual negli-gence and ignores the contribution of recurrent system factors to error.43


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Person and System Approaches to Analysis of Error and Patient Safety Issues

Error reduction efforts can use a “person approach” or a “systems approach.” A physical therapist using a “person approach”3-5,50 focuses on how they individually can re-duce their mistakes, develop their knowledge and skills, and improve patient care. Similarly, managers and facili-ties might attribute error to lack of knowledge or skills, inattention, disorganization, forgetfulness, laziness, negligence, or recklessness of individual providers.3 For example, when considering the cases provided in Figure 1 using a person approach, the physical therapist could be blamed or blame his or herself for the patient harm, or near patient harm, in each case. Using an example from Figure 1, a patient dropped during use of a Hoyer lift, an investigation using a person approach could solely attribute the event to the therapist not having the necessary skills to operate the Hoyer lift or not paying attention to the weight rating.

While it is important to consider and address these indi-vidual issues, a person approach alone is an insufficient method of reducing the incidence of future patient safety events. Research shows that most patient harm occurs because of a recurrent set of circumstances and system conditions rather than solely the error of one provid-er.3-5,52 Therefore, patient safety requires not only consid-eration of an individual provider and his/her actions, but also a broader approach with consideration of all system factors that contribute to patient safety events. Examples of these contributory factors are provided in Table 1. If system factors are not identified and corrected, the same conditions exist for the same error to occur again with other patients and/or providers.3,4

The IOM introduces a framework for evaluating con-tributing system factors on four levels of the American healthcare system as illustrated in Figure 1.1,52 Factors at Levels B-D can contribute to error at the level of the patient’s experience, Level A.1 In the case described in Figure 1 in which a patient dies from a myocardial infarction while on the stationary bike, examples of po-tential contributory factors could be identified in Levels A-D. For example, Level A: the patient does not reveal an underlying medical condition, the physical therapist does not screen for cardiovascular risk factors and mea-sure vital signs during the initial evaluation, the physical therapist does not consider cardiovascular history when deciding on treatment or improperly monitors vital signs during treatment. Levels B or C: lack or malfunction of necessary equipment, clinic flow, productivity expec-tations, or limited accessibility and integration of the complete electronic medical record that allows tracking of cardiovascular issues. Level D: regulatory burden such as increasing documentation required for reimburse-

ment. Using this type of multi-level analysis lessens the likelihood of a similar event ocurring because all po-tential causes have been systematically considered and addressed.

A systems approach to error analysis can also be viewed from the perspective of defensive layers or safeguards. Multiple defensive layers (e.g. alarms, checklists, clear procedures, heightened provider awareness across all team members) should be in place within an organi-zation to prevent inevitable error from reaching the patient.3 Ideally, if a provider makes an error and one defensive layer fails, remaining layers should catch and prevent patient injury. It is when multiple safeguards fail that error reaches the patient and causes harm. These defensive layers typically fail because of a combination of two factors: active failures and latent conditions.3 Active failures are actions of providers who are in direct contact with the patient such as procedural violations, carelessness, momentary lack of recall, clumsiness and other human error.”50 These actions may immediately affect the patient, or the consequences may be delayed or the patient may never be affected.3,5,50,52 Latent con-ditions are organizational factors not in direct contact with the patient.3,50,52 Latent conditions create environ-ments that propagate error (e.g. fatigue, lack of training, productivity expectations, poor communication meth-ods) and can also weaken safeguards against error.3,52 Latent conditions can lie dormant for long periods of time before combining with active failures and allowing adverse events to occur.3,5,50 For example, in the earlier case involving the Hoyer lift, an active failure could be the physical therapist not paying attention to the weight rating. Potential latent conditions could include lack of training and orientation to the lift, lack of proper label-ing or signage on the lift to indicate the weight limit, lack of available and appropriate equipment to transfer the patient, or time constraints. When a patient safety event occurs, organizations and facilities must examine how they can modify their operations to improve system safeguards to block inevitable error from reaching the patient. These concepts are well studied in other health-care professions and in non-healthcare professions such as aviation, business, and the military.3

Processes and Tools for a System Approach

A common system approach to avoiding errors is the Root Cause Analysis, which is “a systematic approach to understanding the causes of an adverse event and iden-tifying system flaws that can be corrected to prevent the error from happening again.”60 While a detailed descrip-tion of the tools for a Root Cause Analysis are beyond the scope of this paper, the following resources are recommended to help physical therapists, facilities, and organizations develop and organize their processes.


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• Institute for Healthcare Improvement: Open School. PS 104: Root Cause and System Analysis. 2014; http://www.ihi.org/education/IHIOpenSchool/Courses/Documents/Course%20Summaries.pdf60

• Emslie S. Root Cause Analysis- Application Guide-lines 2007. http://stuart4handouts.files.wordpress.com/2011/04/moh-rca-guidelines-july-2007_b.pdf61

Call to Action

Patient safety and reduction of patient harm begin with the individuals who interact daily with the patient. Physical therapists must recognize that even with an individual commitment to patient safety, humans make errors. Rather than blame themselves or others, or ignore or hide an issue, physical therapists should use a system-focused error analysis process to evaluate mistakes and launch an investigation at the facility or organizational level to prevent future error and patient harm.

By investigating errors in a timely manner and commit-ting resources to correct contributing system factors and improve system safeguards, facility leaders can create environments in which patient safety is a priority. Be-cause common physical therapy errors and patient safety events are prevalent throughout all practice settings,37-40 organizations with successful error reduction processes should share what they have learned. Others can then customize the reported processes to their own facility.

The profession of physical therapy and the APTA can make patient safety a priority by providing educational opportunities for physical therapists, funding patient safety and quality improvement research, and supporting the dissemination of new data, to improve professional awareness of best practices in patient safety.

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53. Aspden P, Corrigan J, Wolcott J, Erickson SM, eds. Pa-tient safety: Achieving a new standard of care. Washing-ton, D.C.: The National Academies Press 2004.

54. Surveys on patient safety culture research reference list. 2015; http://www.ahrq.gov/professionals/quality-patient-safety/patientsafetyculture/re-sources/index.html. Accessed June 13, 2016.

55. American Physical Therapy Association (APTA). Today's physical therapist: A comprehensive review of a 21st-century healthcare profession 2011. http://www.apta.org/uploadedFiles/APTAorg/Practice_and_Patient_Care/PR_and_Marketing/Market_to_Professionals/TodaysPhysicalTherapist.pdf.

56. Sandstrom R. Malpractice by physical therapists: descriptive analysis of reports in the National Practi-tioner Data Bank public use data file, 1991-2004. J. Allied Health. 2007;36(4):201-208.

57. Physical Therapy Professional Liability Exposure: 2016 Claim Report Update. http://image.exct.net/lib/fe6715707d6d017c7514/m/1/CNA_PT_CS_021116+SEC.pdf. Accessed November 11, 2016.

58. Bal BS. An introduction to medical malpractice in the United States. Clin Orthop Relat Res. Feb 2009;467(2):339-347.

59. Jena AB, Seabury S, Lakdawalla D, Chandra A. Malpractice risk according to physician specialty. N. Engl. J. Med. 2011;365(7):629-636.

60. Institute for Healthcare Improvement Open School: On-line Course Summaries. http://www.ihi.org/education/IHIOpenSchool/Courses/Documents/Course%20Summaries.pdf. Accessed November 4, 2015.

61. Emslie S. Root cause analysis-application guidelines. 2007; https://stuart4handouts.files.wordpress.com/2011/04/moh-rca-guidelines-july-2007_b.pdf. Accessed November 4, 2015.


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Table 1: Contributory Factors Checklist60-61

PATIENT FACTORS STAFF FACTORS TEAM FACTORS

CLINICAL COMPETENCE VERBAL COMMUNICATION

Pre-existing co-morbidity Inadequate knowledge Between professions

Difficulty in diagnosis Inadequate skill/Experience Outside of micro-work environment

PERSONAL COMPLIANCE Inadequate delegation communication

Personality Failure to comply with policy WRITTEN COMMUNICATION

Cultural or religious beliefs Intentional or unintentional violation Incomplete/inadequate information

Social/family circumstances PERSONAL Discrepancies in notes

Stress Personality Incomplete documentation

Disclosure of health history Stress Illegible charting

INTERPERSONAL Fatigue Misinterpretation of information

Patient-staff relationship Distraction SUPERVISION AND SEEKING HELP

Mental impairment (e.g. illness, drugs, pain) Unwillingness to seek help

Specific illness (e.g. depression) Unavailability of staff to help

Domestic issues Responsiveness of staff to request for help

INTERPERSONAL Inadequate delegation communication

Staff-patient relationship CONGRUENCY

Staff-staff/team relationship Definition of tasks between professions

Staff-organization relationship Definition of tasks within profession

LEADERSHIP

Ineffective leadership

Unclear definitions of responsibility

STAFF/COLLEAGUES RESPONSE TO INCIDENTS

Inadequate support by peers after incident

Inadequate support by team members after incident

Adapted from the Institute for Healthcare Improvement (IHI)60 and from Contributory Factors Framework in Root Cause Analysis-Application Guidelines.61


PTJ-PAL - J29

Table 1: Contributory Factors Checklist60-61 (cont.)

WORK/CARE ENVIRONMENT

MANAGEMENT/ORGANIZATIONAL

FACTORS

TASK/TECHNOLOGY FACTORS EXTERNAL FACTORS

BUILDING/DESIGN LEADERSHIP/GOVERNANCE

AVAILABILITY OF HEALTH INFORMATION POLITICAL

Inadequate equipment maintenance Leadership presence Misinterpretation of

information Goals

Poor functionality (ergonomic design) Leadership style Availability/reliability of

information Perceptions

PHYSICAL ENVIRONMENT RESOURCES/CONSTRAINTS

Hard to find information in EHR ECONOMIC

Movement of patients between areas Human resources TASK DESIGN Laws/regulations

Storage Financial resources Ease of task execution Regulatory requirements

Inadequate space within rooms

SAFETY, CULTURE, PRIORITIES Design deficiency

PARTNERSHIP WORKING WITH EXTERNAL

ORGANIZATIONSWORK.CA

INFORMATION TECHNOLOGY Inadequate safety culture Contractual arrangements

Malfunction/reliability Wrong priorities Communication

Functionality of EHR ORGANIZATIONAL STRUCTURE

System design/access Hierarchical arrangement of staff

STAFFING Unclear roles/responsibilities

Unavailability Standardization of common processes

Allocation of staff Authority gradient

Recruitment Objectives, policies/standards

EDUCATION/TRAINING Operations

Orientation Human resources policies

Ongoing/refresher training Information policies

Workload/hours of work Risk management process

Inadequate regular breaks

Heavy workload

Long working hours


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Minor Event

Moderate Event

Events can cause varying degrees of harm from none to very severe

Figure 1(Adapted with permission from Ginsburg et al51 Healthcare Quarterly, LongwoodsTM Publishing Corp)

Minor NearMiss

Major NearMiss

Major Event

Near Misses have the potential to cause varying degrees of harm from none to very serious (near misses can be caught far from to very close to the patient)

Definition: An event that would have resulted in no harm or very minimal temporary harm to the patient but did not because it was caught or because of good luck. Examples: 1. PT documents in wrong patient chart, but corrects shortly thereafter with addendum.2. After transfer to new facility, inaccurate biographical and medical information about a patient is discovered and corrected.3. PT in outpatient orthopedic setting spends 5 minutes treating right ankle, when left ankle was involved. Realizes mistake and completes treatment on the left side.

Definition: An event involving no harm or very minimal temporary harm to the patient.

Examples: 1.PT uses a Hoyer lift that is not rated for the patient’s weight. Patient is lifted two inches off the bed and the lift tips and drops her back into bed. The patient and PT are both scared, but no physical harm occurs. 2. Patient arrives in outpatient clinic without a referral or precautions shortly after hip labral repair. The PT assumes the precautions are the same as previous patients after the same procedure, and proceeds accordingly. On the next visit, PT discovers that precautions were unique in this case, and that she has exceeded those restrictions. No harm to the patient is evident, and PT communicates with surgeon who is not concerned.

Definition: An event that causes discomfort sufficient to interfere with usual activity and requires additional specific therapeutic intervention but poses no significant or permanent risk of harm to the patient. Examples: 1. During Dry Needling treatment, patient sustains pneumothorax, requiring hospitalization and chest tube; full recovery with no lasting effects. 2. Serial Casting applied to teenager without ability to report pain/sensation due to neurological condition. Sustains decubitus ulcer requiring several weeks of wound care. Ulcer eventually fully heals.

Definition: An event that would have resulted in death or serious physical or psychological injury but did not because it was caught or because of good luck.

Examples: 1. Surgeons use femoral nerve block after Total Knee Arthroplasty, but rehab staff is not aware of implications of the block. PT stands with patient, but knee collapses and patient sustains major fall. No traumatic brain injury or fracture due solely to good luck. 2. During an inpatient rehabilitation stay after a brain tumor removal, a man shows several early signs of Deep Vein Thrombosis and Pulmonary Embolism which are missed by the PT and the entire team for several weeks. After he experiences chest pain, the PE diagnosis is made and he is treated without sequelae.

Definition: An event involving death or serious physical / psychological injury. These events should not be considered “stuff” that “just happens” nor should they be considered inevitable. Examples: 1. PT delegates a medically unstable patient to a PTA, who lacks the skills/knowledge to closely monitor for response to treatment. During treatment, the patient sustains a stroke requiring a prolonged hospital stay and extensive treatment. The patient survives. 2. A PT in an outpatient orthopedic setting puts a patient on a stationary bike to warm up, not considering cardiovascular history. The patient sustains a myocardial infarction while on the bike and does not survive.

Grey areas representing Events between categories. Arrows reflect increasing severity of the events (orange) and near misses (blue)


PTJ-PAL - J31

Figure 2

Figure 2:

Level A:Patient

Experience

Level B:The functioning of small

units of healthcare delivery

("microsystems")

Level C:The functioning of the organizations

that house and support the microsystem

Level D:The environment of policy, payment, regulation, accreditation, litigation, professional education,

social policy, and others which shape the behavior, interests, and opportunities of the

organization at Level C


HPA Resource

President's Message ......................................... 32

Global Health SIG News .................................... 33

Congratulations Global Health Special Interest Group Chair, Dr. Celia Pechak .............................. 35

Member Services Update ............................. 36

Nominating Committee Election Report ......... 37

APTA Payment and Practice Forum Report ..... 38

Technology SIG Report ...................................... 39

This past November, we held a national election and chose a new president. More than 130 million Ameri-cans participated (58% of eligible voters), but more than 90 million people did NOT vote who could have. No matter which side you were on, that is a bit discourag-ing. As I write this and it comes to print, our president-elect has not yet started his term, so we are not exactly sure what the effect on health policy will be in the upcoming years. I do believe that the 2010 passage of the ACA led to some of the most significant changes in U.S. healthcare policy since the enactment of Medicare and Medicaid in 1965. Now we might see many policy changes that will affect our patients and our profession, either by a rollback of some of the ACA provisions, or new policies that may be more market-based. Either way, the people have spoken, and now we must prepare for the healthcare system that is presented to us, adapting to any changes and new policies that Congress or the next administration passes.

APTA will no doubt be front and center and will rep-resent us well in Washington, D.C., policy discussions.

This is also an opportunity for HPA The Catalyst to take lead and educate our members in the upcom-ing program revisions. Our members will be looking to us for information and re-sources through our listserv discussions, webinars, CSM and NEXT presentations, and scholarly articles in this publication. I have no doubt we have the expertise within our ranks to lead and fill the vacuum of informa-tion created by this monumental election.

Prior to the presidential election, our Board of Direc-tors – along with committee chairs and our executive staff – met for our annual fall retreat in beautiful Em-pire, Colorado. The unseasonably warm autumn gave us the opportunity to enjoy the beautiful scenery in this Colorado Easter Seals camp. We met in our lodge build-

President's Messageby Ira Gorman, PT, PhD, MSPH

"We in America do not have government by the majority. We have government by the majority who get involved.” – Thomas Jefferson, 1787

TABLE OF CONTENTS: HPA RESOURCE


HPA Resource - 33

ing, ate together in the camp dining hall, and had many opportunities afterhours to socialize, brainstorm, share ideas, and dream in this wonderful setting. Of course, serious work did occur, as we implemented the function and structure of our new streamlined board to develop our 2017 budget, reviewed our bylaws, and began a discussion about the value HPA The Catalyst offers to members. Recent member survey data set the stage for our strategic planning.

At our annual membership business meeting at CSM, we plan to share with members our accomplishments from 2016, including the newly redesigned Board of Di-rectors and committee structure. We will introduce our new executive management team, Concepts Manage-ment Group, and share the recommendations from our LAMP committee to expand our focus from just leader-ship to include all facets of our section under the suc-cessful LAMP acronym, including: administration and management, payment and practice, professionalism, social justice, ethics, marketing, and, of course, health-care policy. We hope this year to increase our non-dues revenue through more low-cost, high-value webinars available to members and non-members.

Finally, at CSM this year we have reintroduced our stu-dent mentor program, offering scholarships to DPT and

PTA students who will join board member volunteers in section activities, including attending the board meeting and business meeting, and working at the exhibit hall booth. We desire to expand this program in the future for our student members, and hope they will continue to be active HPA members after their graduation. They are the future of our section.

Which brings me back to my old friend, T.J. (Thomas Jefferson). We need to ensure that our members are in-volved in our section and that we develop programs and services to meet their needs. We need members involved in the section, members involved in the association, and members involved in our country and society. This elec-tion can be a call to all of us to get involved in health policy and not sit back quietly only to complain when rules and regulations are passed that can negatively affect our practice and profession. Otherwise, we are a frustrat-ed minority ruled by the majority who did get involved.

Ira Gorman is an Associate Professor in the School of Physi-cal Therapy at Regis University in Denver, Colorado, and President of HPA. He can be reached at [email protected].

Global Health SIG Newsby Celia Pechak, PT, PhD, MPH

Global Health Special Interest Group (GHSIG) leader-ship has been busy preparing for our many activities at the Combined Sections Meeting in February 2017.

First, we will have wonderful programming that addresses a variety of interest topics to GHSIG members, including:

• Working with Survivors of Torture and Trauma: Translating Evidence to Practice Thursday, February, 16, 2017 | 11:00 a.m.-1:00 p.m.

• Love Global Health? Then Care About Policy! Thursday, February 16, 2017 | 3:00 p.m.-5:00 p.m.

• I Don’t Understand What You Told Me: Working with Low Health Literacy Friday, February 17, 2017 | 11:00 a.m.-1:00 p.m.

• The Physical Therapists’ Role in Humanitarian Crises Saturday, February 18, 2017 | 8:00 a.m.-10:00 a.m.

• Physical Therapy Pro Bono Services: Not Just Free Therapy Saturday, February 18, 2017 | 3:00 p.m.-5:00 p.m.

We are also excited to introduce the Global Health Catalyst Talks (The Good, Bad, and Ugly of Global Health Engagement) on Thursday, February 16, 2017, from 8:00 a.m.-10:00 a.m. In this new programming format, we have invited individuals with a passion for global health engagement to give presentations in a format similar to the “TED Talk” series. Between each speaker, we will invite the audience to discuss their reac-tions to the speaker in small groups at round tables. Our


34 - HPA Resource

speakers include:

• Dr. Ronnie Leavitt – the GHSIG co-founder, and author of “Cultural Competence: A Lifelong Journey to Cultural Proficiency.”

• Dr. Efosa Guobadia – co-founder and co-director of Physical Therapy Day of Service.

• Dr. Whitney Ogle – a PhD candidate who works as a pediatric physical therapist. During her DPT edu-cation, she participated in an immersion to Ethiopia where she was introduced to a world beyond the comforts of her insulated upbringing in a politically conservative home. She now is a vocal advocate for reflective consideration and implementation of social justice constructs within one’s daily life.

• Dr. Shafik Dharamsi (Professor & Associate Dean, University of the Incarnate Word, School of Osteo-pathic Medicine) – a physician with extensive global health experience, and is one of the authors on the following rehabilitation-related article: Shields M, Quilty J, Dharamsi S, Drynan D. International fieldwork placements in low-income countries: Exploring community perspectives. Aust Occup Ther J. 2016. (http://www.ncbi.nlm.nih.gov/pubmed/27111028).

We will hold the GHSIG Business Meeting on Friday, February 17, 2017, from 7:00 a.m.-8:00 a.m. As always, this is an important place to have your voice heard – and a great opportunity to put faces to the names we see on the listserv and newsletters.

The Global Health Reception will be on Friday, Febru-ary 17, 2017, from 8:00 p.m.-10:00 p.m., immediately following HPA The Catalyst’s Business Meeting. For those of you who have not attended before, it is a wonderful venue to network and socialize with others who have global health interests. Attendees can circulate among the tables, where the presenters will be sharing details of their domestic, international, and/or research projects related to global health. This is a particularly great event to invite non-members to, especially students, so that they can learn more about what the GHSIG and Section offer. Ap-petizers and a cash bar will be available.

GHSIG has been involved in other activities as well. The Nominating Committee slated an excellent group of candidates, and is pleased to announce the results from our recent election. The following GHSIG leadership will assume their positions at the end of CSM 2017:

• Chair: Jennifer Audette, PT, PhD

• Vice Chair: Sue Klappa, PT, PhD

• Nominating Committee Member: Cheryl Kerfeld, PT, PhD

Our student liaison Jennifer Gigliotti has been collaborat-ing with the 2016 Global Outreach Project Committee of the APTA Student Assembly. Resulting from this collabo-ration, the student committee has made the Internation-al Service Manual for Students: A Guide for Students Considering International Service Trips available on the GHSIG homepage to members and non-members. Please check it out and share your feedback with the student authors. Thank you to the student leaders for sharing this resource and to Jen for facilitating this partnership with the Global Outreach Project Committee!

Finally, we have been planning our first ever global health-related webinar in 2017. Thank you to those who completed our survey to help us determine key topics of interest. More details to come!

Celia Pechak is the HPA Director of Social Responsibil-ity and Global Health and the President of the Global Health Special Interest Group (SIG). She can be reached at [email protected].

Global Health SIG Happenings at CSM 2017

Global Health Catalyst Talks (The Good, Bad, and Ugly of Global Health

Engagement) Thursday, Feb. 16, 2017

8:00 a.m.-10:00 a.m.

GHSIG Business Meeting Friday, Feb. 17, 2017 7:00 a.m.-8:00 a.m.

Global Health Reception Friday, Feb.17, 2017 8:00 p.m.-10:00 p.m.


HPA Resource - 35

Congratulations Global Health Special Interest Group Chair, Dr. Celia Pechak

by Jenny Audette , PT, PhD

The Doctor of Physical Therapy (DPT) Program at the Univer-sity of Texas at El Paso (UTEP) was recently awarded a $2.6 million grant from the Health Resources and Services Admin-istration (HRSA). The goal of HRSA’s Scholarships for Disad-vantaged Students is to increase the diversity of the healthcare workforce. UTEP is a Hispanic-majority university located on the international border that runs between El Paso, Texas, and Ciudad Juarez, Mexico. The university has provided access to higher education for many historically underserved students. The UTEP DPT program aims to develop leaders

with the cultural and linguistic competence to optimally serve its under-resourced binational community.

Celia Pechak, PT, PhD, MPH – who co-wrote the grant and co-manages the project with Loretta Dillon, PT, DPT – explained, “Having the support of this grant over the next four years will allow our students to graduate with significantly less debt. So, we hope that they will be in a much better financial position to pursue residencies,

fellowships, and other advanced training upon gradu-ation. Our community is in great need of more DPTs with advanced training. And our profession certainly needs more diversity!” As part of this project, the award-ees will receive financial training from the university and mentoring from high-achieving physical therapists from diverse backgrounds.

Pechak noted “Our vision for this grant is that it helps our students to reach their visions for their professional selves. Now that financial barriers have been reduced, and with intentional mentoring, we hope that our students will reach for even bigger dreams than they might otherwise have felt were possible…including the pursuit of academic doctorates. And of course, we hope that they will use their advanced training to serve under-served populations, whether it be here or elsewhere in the US. Hopefully they will pay this opportunity forward.” The grant recipients will also provide outreach to high school and college students across the region to educate others from disad-vantaged backgrounds about physical therapy and the availability of the grant at UTEP.

This is a wonderful example of what the Global Health SIG stands for, and the good work that our members are doing in the world! Congratulations on all of your hard work, Celia!

Jenny Audette is Vice Chair of the Global Health SIG. She can be reached at [email protected].

Celia Pechak, PT, PhD, MPH, played integral role

in recent $2.6 million grant at the University of Texas at

El Paso.

Join Us for the Global Health Reception at CSM 2017 in San Antonio

The Global Health SIG will host its Global Health Reception on Friday, Feb. 17, 2017, from 8:00 p.m. to 10:00 p.m. at the Hilton Palacio del Rio in room Salon del Rey B in San Antonio, TX. Join us for light hors d'oeuvres and cash bar while mingling with fellow physical therapy professionals who are interested in global health initiatives. And thank you to the following individuals and institutions for sponsoring the reception! (As of Dec. 19, 2016)

University of Washington Dept of Rehab Division of

Physical TherapyThe University of Texas

at El Paso

Arcadia UniversityRonnie Leavitt

Health Volunteers Overseas

Doctor of Physical Therapy University of Wisconsin-

MadisonNorthwestern University

Bellarmine University

Texas Woman's University, School of Physical Therapy

University of KentuckyThe Pro Bono Network


36 - HPA Resource

Member Services Update: Catalyzing Community Engagementby Sue Klappa PT, PhD

At the end of October, the HPA Board of Directors met in Empire, Colorado, for its fall leadership retreat. Jer-emy Bittel represented the Member Services Committee while I attended National Student Conclave (NSC) in Miami. While at NSC, I had the opportunity to net-work with many students who were excited about being members of HPA The Catalyst! Other students heard about HPA The Catalyst for the first time. NSC was a great place to share who we are as a Section. HPA The Catalyst offers a place for community engagement, no matter how you wish to define your community. From advocacy, health policy, and leadership, to LAMP and Technology and Global Health SIGs or Catalyst groups, there is something for everyone.

One exciting opportunity that has resurfaced is our student scholarship/mentorship program for CSM 2017. We will be offering registration scholarships to three students – two DPT students and one PTA student to attend CSM 2017. These students will be paired with a

mentor from our leader-ship team. Our committee just finished scoring the 23 student applicants for this CSM 2017 student scholar-ship/mentorship program. The Scholarship winners were Haley Monson, SPTA, at Northland Community & Technical College; Emily Power, SPT, at Regis Uni-versity; and Christina Lee, DPT, at Boston University. Mentors from our leadership include Kerry Wood, Matt Mesibov, and Karen Peterson. Thank you to our mentors for their generosity to connect with our newest members and future student leaders!

CSM 2017 in San Antonio will soon be here. When you get to CSM, take time to stop by the HPA booth and chat with us about what is happening in your practice. Come and learn more about LAMP and our SIGs. Don’t forget to register at the booth for another exciting drawing for a wearable technology prize! See you all in San Antonio!

Membership Committee members include: Sue Klappa, Jeremy Bittel, Andy Wagner, and Lauren Bounds.

Sue Klappa serves as the Chair of the Member Services Committee. She can be reached at [email protected].


HPA Resource - 37

Nominating Committee Election Reportby Jill Black, PT, DPT, EdD

The Nominating Committee was pleased to present a robust slate of candidates to membership for HPA the Catalyst’s annual election. This year marked the intro-duction of the new Board of Director positions with the addition of Director of Operations, Director of Scholar-ship, and Director of LAMP. We also elected a Vice-Pres-ident, Secretary, and Nominating Committee member.

The GHSIG elected a Chair, Vice Chair, and GHSIG Nominating Committee member. The GHSIG Chair will also serve as the Director of Social Responsibility and Global Health on the HPA Board of Directors. The Tech SIG elected a Vice Chair and a Tech SIG Nomi-nating Committee member. Two additional Tech SIG positions were filled by appointment at the most recent

HPA Board of Director Retreat in accordance with the Bylaws.

Last year, 10.0% of the membership participated in the election. This year, 10.5% participated. Please join us in congratulating the election winners, and extend-ing thanks to the willing candidates. We look forward to having the non-elected candidates continue as active members of HPA, and those elected to begin their terms of service immediately following the Combined Sections Meeting in February 2017.

Jill Black is HPA’s Nominating Committee Chair. She can be reached at [email protected].

Incoming LeadershipKaren Hughes, PT, MS, LSS, BB, was elected to a three-year term as Vice President.

Jim Eng, PT, DPT, MS, GCS, was re-elected to a three-year term as Secretary.

David Norris, PT, MS, was elected to a three-year term on the HPA Nominating Committee.

Kate Brewer, PT, MBA, RAC-CT, was elected to a one-year term as the Director of Operations.

Dawn Magnusson, PT, PhD, was elected to a two-year term as the Director of Scholarship.

Emily Becker, PT, was elected to a three-year term as the Director of LAMP.

Jennifer Audette, PT, PhD, was elected to a three-year term as GHSIG Chair and the Director of Social Responsibility and Global Health.

Sue Klappa, PT, PhD, was elected to a three-year term as GHSIG Vice Chair.

Cheryl Kerfeld, PT, PhD, was elected to a three-year term on the GHSIG Nominating Committee.

Alan Lee, PhD, DPT, CWS, GCS, was elected to a three-year term as the Tech SIG Vice Chair.

Gregory Adams, PT, MS, CCS, CEP, was elected to a three-year term on the Tech SIG Nominating Committee.

Vazira Kanga, PT, MHA, was appointed to a two-year term on the Tech SIG Nominating Committee.

Sean Bagbey, PTA, MHA, ATC, CIRS was appointed to a three-year term as Tech SIG Secretary.


38 - HPA Resource

APTA Payment and Practice Forum Reportby Lori Pearlmutter, PT, MPH

Anne Biala and I were fortunate to be sent to the APTA Annual Payment and Practice Forum as members of HPA’s Practice and Payment Committee that took place in Pittsburgh, PA,. The forum was filled with information.

Wanda Evans and Elise Latawiec from State Affairs gave an update of activities throughout the states. Posi-tive developments include direct access now in Florida and Louisiana, PTs being included in youth concus-sion evaluation (NM, HI), and ordering radiographs (WI). Negatives were issues of non-payment for manual therapy (97140) and our inability to reduce impacts from POPTS. There are also continuing insurance is-sues, such as denials without reason, inability to acquire consistent responses from utilization review companies, and decreasing workers' compensation reimbursements throughout the country.

New modes of payment were discussed by Roshunda Drummond-Dye and Heather Smith during the Fed-eral Regulatory Affairs Update. Look for more Value Based Models and Fee For Service reimbursement tied more heavily to outcomes. The IMPACT act – a quality measurement and payment system for post-acute care – began Oct. 1, 2014. The OIG has a targeted medical review for outpatient PT services with high utilization. TriCare is decreasing payments by 30% to 50%, and not allowing PTAs to provide reimbursable treatment. PTs are encouraged to participate in the Merit-Based Incen-tive Payment System (MIPS).

Stephen Hellier and Angela Shuman from State Af-fairs discussed infringement issues by chiropractors and ATCs, and the continuing issue of dry needling. Richard Katz gave a presentation of his state’s success in working with the State of California’s Insurance Commissioner in order to control the third-party administrator.

The forum also addressed population health concerns. In addition to information about Value Based Models including a SWOT analysis by Heather Smith, Michael Eisenhart talk-ed about his success in work-ing directly with companies to help their employees. Kathleen Picard helped us understand the new evaluation codes and included some excellent cases so we could put our knowledge into practice.

During the panel discussion about the Physical Therapy Licensure Compact, we learned that we need six more states to join AZ, OR, TN and MO in order to reach the minimum level of ten states for a commission. This will allow therapists to treat in nearby states without seeking reciprocity, thereby helping military families and con-tract therapists and reducing some of the states’ financial burdens. The final presentation by Christopher Peterson gave us insights into telehealth and how PTs can become more involved.

This was the first Payment and Practice Forum for myself and Anne Biala. It was extremely informative, and has al-ready helped us in our roles on the Payment and Practice committee, and in our positions. We thank HPA for the opportunity.

Lori Pearlmutter is a member of HPA’s Payment and Prac-tice Committee. She can be reached at [email protected].


HPA Resource - 39

Technology SIG Reportby Robert Latz, PT, DPT, CHCIO

Elections were held recently for our Tech SIG. Thanks to the Nominating Committee, we had a full slate of candidates, which is always good. After elections, the our Technology SIG leadership will be as follows effective with the upcoming Combined Sections Meeting:

Chair: Robert “Bob” Latz, PT, DPT, CHCIO

Vice Chair: Alan Chong Lee, PT, DPT, PhD, CWS, GCS

Secretary: Sean Bagbey, PTA, MHA, ATC, CIRS

Nominating Committee Chair: Tim Richardson

Two-Year Nominating Committee Member: Vazira Kanga, PT, MHA

Three-Year Nominating Committee Member: Greg

Adams, PT, MS, CCS

Outgoing: Dr. Beth Ennis has been the Vice Chair for the past six years. She is moving into a new position as President of the Kentucky Physical Therapy Association. Even so, she is available as an advisor as needed to help the Tech SIG continue to grow. Thank you, Beth, for all you do!

Also, Alan Lee was in the position as Secretary for the past year. He was elected into the Vice Chair position.

Robert “Bob” Latz, PT, DPT, CHCIO is the Chair of the Technology Special Interest Group of the HPA The Catalyst Section of the APTA. Currently, Bob is the Chief Informa-tion Officer for Trinity Rehabilitation Services. He can be reached at [email protected].

CSM 2017: Register for an HPA The CatalystPre-Conference Course

HPA The Catalyst is excited to host three pre-conference sessions at this year's Combined Sections Meeting in San Antonio, Texas. All three courses will be two-day work-shops to increase your skills in physical therapy leader-ship or physical therapy administration skills, and they will take place Tuesday, Feb. 14, 2017, and Wednesday, Feb. 15, 2017. Register for LAMP Institute for Leader-ship in Physical Therapy Leadership 101: Personal Leadership Development to kick off your LAMP jour-ney and to sharpen your leadership skills. If you already have completed LAMP Leadership 101, register for LAMP Institute for Leadership in Physical Therapy Leadership 201: Advanced Leadership Development, where you will further advance your leadership skills

HPA The Catalyst is also excited to debut its Adminis-trative and Management Skills for PT Practice work-shop at CSM 2017! This inaugural course will focus on performance management, human resources, legal considerations, regulatory aspects, compliance issues,

communication, project/program management, and finance and budgeting. This is a valuable offering for all PT professionals, regardless of their administrative or managerial role.

Expand your role in physical therapy by enhancing your leadership and administrative skills through HPA The Catalyst. Visit www.APTA.org/CSM to add on of these terrific pre-conference courses today, and we look for-ward to seeing you in San Antonio!


Official Publication of the Section on Health Policy & Administration of the American Physical Therapy Association

HPA The CatalystAmerican Physical Therapy Association2400 Ardmore BlvdSte 302Pittsburgh, PA 15221
