Innovative payment mechanisms in Maryland Hospitals: An empirical analysisof readmissions under total patient revenue

Karoline Mortensen a,n, Chad Perman b, Jie Chen a

a Department of Health Services Administration 3310 School of Public Health Building University of Maryland College Park, MD 20742-2611, United Statesb Health Management Associates, 1350 Connecticut Ave. NW, Suite 605, Washington, DC 20036, United States

a r t i c l e i n f o

Article history:Received 28 February 2013Received in revised form13 September 2013Accepted 25 March 2014

Keywords:MarylandHealth care reformHospital readmissionsInnovative payment

a b s t r a c t

Background: The state of Maryland implemented innovative budgeting of outpatient and inpatientservices in eight rural hospitals under the Total Patient Revenue (TPR) system in July, 2010.Methods: This paper uses data on Maryland discharges from the 2009–2011 Healthcare Cost andUtilization Project (HCUP) State Inpatient Databases (SID). Individual inpatient discharges from eighttreatment hospitals and three rural control hospitals (n¼374,353) are analyzed. To get robust estimatesand control for trends in the state, we also compare treatment hospitals to all hospitals in Maryland thatreport readmissions (n¼1,997,164). Linear probability models using the difference-in-differencesapproach with hospital fixed effects are estimated to determine the effect of the innovative paymentmechanisms on hospital readmissions, controlling for patient demographics and characteristics.Results: Difference-in-differences estimates show that after implementation of TPR in the treatmenthospitals, there were no statistically significant changes in the predicted probability of readmissions.Conclusions: Early evidence from the TPR program shows that readmissions were not affected in the18 months after implementation.Implications: As the health care system innovates, it is important to evaluate the success of theseinnovations. One of the goals of TPR was to lower readmission rates, however these rates did not showconsistent downward trends after implementation. Our results suggest that payment innovations thatprovide financial incentives to ensure patients receive care in the most appropriate setting whilemaintaining quality of care may not have immediate effects on commonly used measures of hospitalperformance, particularly for rural hospitals that may lack coordinated care delivery infrastructure.

& 2014 Elsevier Inc. All rights reserved.

1. Introduction

The US health care system is undergoing rapid transformationin an effort to address high levels of health care expenditures, tocontrol growth in spending, and to reduce widespread inefficiency.Although hospitals account for over one-third of total health carespending,1 there are few incentives in our primarily fee-for-servicepayment system to encourage hospitals, physicians and otherhealth care providers to coordinate care.2 This results in duplica-tion of efforts, overuse of services, and extensive waste.2,3 There isa consensus that there is a need to move beyond traditional fee-for-service reimbursement strategies, and encourage study ofemerging models of provider-payment reform.2,4–9 Innovativepayment mechanisms that discourage volume of care and rewardcollaborative, efficient care show promise in slowing expendituregrowth, especially in the high-cost hospital setting.10

1.1. All-payer system in Maryland

The state of Maryland is well-suited to transform its health caredelivery system because it is the only state that sets hospital ratesfor all payers.3–6 Maryland implemented its system of full rate-setting authority for all payers and all general acute hospitals in1976.7 Rates are prospectively set, largely in line with Medicare'shospital prospective payment system (PPS), with no discounts orpreference to specific payers.7 The all-payer system includes pay-for-performance incentives. A value-based purchasing initiativeresults in redistribution of system revenue from lower-to-higherperforming hospitals, and an initiative to reduce hospital acquiredinfections provides hospitals incentives to reduce preventableconditions.

1.2. Total patient revenue

Maryland is on the forefront of health care reform, witha new system in place to realign providers' incentives throughsweeping payment reform. Maryland implemented the Total

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http://dx.doi.org/10.1016/j.hjdsi.2014.03.0022213-0764/& 2014 Elsevier Inc. All rights reserved.

n Corresponding author.E-mail address: karoline@umd.edu (K. Mortensen).

Please cite this article as: Mortensen K, et al. Innovative payment mechanisms in Maryland Hospitals: An empirical analysis ofreadmissions under total patient revenue. Healthcare (2014), http://dx.doi.org/10.1016/j.hjdsi.2014.03.002i

Healthcare ∎ (∎∎∎∎) ∎∎∎–∎∎∎

Patient Revenue (TPR) program in eight rural hospitals on July 1,2010.11 TPR is a voluntary alternative hospital financing strategydeveloped by the Health Services Cost Review Commission(HSCRC) covering all inpatient and outpatient services for ruralhospitals.12–14 TPR revenue constraint systems were made avail-able to hospitals operating in regions of the state characterized byan absence of densely overlapping service areas.14 The programchanged incentives for hospitals by providing a global budget thatguarantees a specified annual revenue for each hospital regardlessof the number of patients treated and the amount of servicesprovided.14 This is a significant deviation from the system thatfinancially rewarded admissions and readmissions rather thanincluding strong financial settings to reduce them.

The primary goal of the TPR program is to provide the hospitalswith strong incentives to treat its community of patients in themost efficient and clinically effective way, improving the value ofthe care provided via lower cost and better clinical effectiveness/quality.14 TPR aligns with several “best practices” of alternativepayment systems that influence changes in utilization and qual-ity.15 Such best practices include quantifying measures that haveroom for improvement, coordinated program design, and incen-tives for quality attainment and improvement that are largeenough to motivate a behavioral response. Participating hospitalsnow have incentives to increase efficiency of health care delivery,contain costs, and to reduce avoidable admissions and readmis-sions.16 HSCRC staff monitor hospital performance on quality ofcare metrics, with the expectation that each hospital will, at aminimum, maintain its relative performance ranking on theHSCRC Quality-Based Reimbursement (QBR) and Maryland Hospi-tal Acquired Conditions rankings.14 There is concern that theinitiative may lead to hospitals directing patients to rival facilities,providing insufficient care or selecting healthier patients; HSCRCannounced it would closely monitor hospitals' practices.16

The budget for each hospital is based on the hospital's revenuefrom the prior fiscal year.13 Base year patient revenues areadjusted for price variance from approved rates, for volumevariances, and change in differential due to changes in payermix.14 The approved revenue is adjusted based on each hospital'srelative performance on specific quality measures.12 Annualadjustments to the budget also include adjustment for populationchanges and growth, reversal of any previous retroactive adjust-ments, and differential readjustments due to payer mix changesand bad debt.14 If participating hospitals lower spending byreducing admissions, readmissions or by other strategies, theykeep the resulting savings.12 However, if costs increase beyond thebudget allotment, the hospital bears financial risk, and can adjusttheir prices within a 5% corridor in the next year.13,14,17 Focusingefforts on reducing readmission rates to improve quality of careand reduce costs is a strategy that has proven to be effective.18,19

Readmission rates are increasingly used in assessment of healthcare system performance20; they have advantages and limitationsas measures of quality of health care.21,22 Readmissions may beappropriate under certain circumstances, but they occur often atsignificant additional financial and health expense.18,23 There hasbeen very little improvement in national average 30-day read-mission rates in recent years.23 The Affordable Care Act (ACA) of2010 authorizes penalties for hospitals with Medicare admissionsthat exceed a hospital's average, risk-adjusted 30 day readmissionrate for specific diagnoses.20,21,24 Hospitals have naturally shiftedfocus to reducing readmission rates in effort to improve quality ofcare and reduce costs.18,19 Readmissions are an important metricof quality of care, and hospitals did not have adequate financialincentives to reduce readmissions before the state of Marylandaltered their incentive structure in 2010 to “aggressively reducereadmissions” – the focus of this analysis.14,25 To our knowledge,Maryland is the first state to implement global budgeting of

hospital and outpatient services (Rochester, New York globallybudgeted only inpatient services in the 1980s26). The goal of thispaper is to analyze the early effects of implementation of the TPRprogram on hospital readmissions.

2. Material and methods

2.1. Data

This analysis uses patient-level discharge data for Marylandfrom the State Inpatient Database (SID) core data file for 2009,2010, and 2011 (the most recent year available).27 The SID data arefrom the Healthcare Cost and Utilization Project (HCUP) datasetsponsored by the Agency for Healthcare Research and Quality andthe Department of Health and Human Services. The HSCRCsupplies these state-level data on the universe of discharges inMaryland for HCUP. Authorized use of the SID data comes withlimitations. Researchers agree to report only aggregate levelstatistics, so this analysis does not tabulate any data at theindividual hospital level beyond data already publicly available.Researchers agree not to contact establishments included in thedata.6

We identify the eight rural treatment hospitals that beganparticipating in the TPR program in July, 2010. Two of theparticipating hospitals report their data together (DorchesterGeneral and The Memorial Hospital at Easton), so the treatmenthospitals include data reported from seven sources. TPR wasalready implemented in two rural hospitals, Edward. W. McCreadyMemorial Hospital and Garrett County Memorial Hospital, so thesetwo hospitals are excluded from the analysis.

There are a total of 46 non-federal, short-term acute carehospitals in Maryland (http://www.ahd.com/states/hospital_MD.html). We use the remaining 36 that are not participating in TPRas the universe for our two control groups (Fig. 1). For the firstcontrol group, we identify the remaining seven hospitals that areclassified as rural hospitals.28 Two of these hospitals (PeninsulaRegional Medical Center and Atlantic General Hospital) did notreport readmissions data in the SID, so they are excluded from theanalysis. Of the remaining five rural hospitals, we select threehospitals that did not participate but were identified by HSCRC aspotential participants in TPR in the future to serve as controls.29

These three rural hospitals include Civista Medical Center, FrederickMemorial Hospital, and Upper Chesapeake Medical Center.

The second control group that includes the three rural controls,two additional rural hospitals, and the remaining 25 Marylandhospitals that report readmissions data (four additional hospitalsin the state did not have readmission data in the SID). The data sethas 1,997,164 observations representing patient admissions for the37 reporting hospital units included in this analysis.

2.2. Variables

The analysis controls for characteristics that proxy for riskadjustment. The demographic variables include patient's age,sex, race (white, black, other race), and ethnicity (Hispanic). Theprimary payer for the discharge is categorized as private insurance,Medicare, Medicaid, self-pay, and no payment. We include thecount of unique chronic diagnoses reported on the discharge as ameasure for risk-adjustment. Estimated median household incomemeasured at the patient's zip code of residence is included inquartiles, with classification cut-off values varying by year.30

Patients discharged from small hospitals have been found to havehigher readmission rates than those discharged from large hospi-tals.19 With the exception of one medium-sized hospital, all ofthe hospitals in the treatment and rural controls fall under the

K. Mortensen et al. / Healthcare ∎ (∎∎∎∎) ∎∎∎–∎∎∎2

Please cite this article as: Mortensen K, et al. Innovative payment mechanisms in Maryland Hospitals: An empirical analysis ofreadmissions under total patient revenue. Healthcare (2014), http://dx.doi.org/10.1016/j.hjdsi.2014.03.002i

category of rural large hospitals in the Northeastern region of theUS.31 All of the treatment and rural control hospitals in thisanalysis are short-term acute care, voluntary, nonprofit hospitals.

Three key explanatory variables are generated for the analysis.The first is a temporal measure that captures the pre- and post-periods. The indicator for post equals one for all months beginningwith the July 1, 2010 implementation date. The second binaryvariable is an indicator for treatment status, equaling one for all ofthe seven hospitals participating in the TPR program. The thirdindicator variable is the interaction term between post andtreatment status. This interaction variable is the key coefficientfor interpreting the effect of the TPR program on readmissions inparticipating hospitals.

The outcome variable is the binary readmission variable. TheMaryland SID includes a binary variable indicating readmission.The READMIT variable indicates that the patient was admittedwithin 31 days of this admission.30 The state of Maryland did nothave an accurate algorithm to consistently match patients acrosshospitals during the study period,32 so this readmission measure isstrictly an intra-hospital readmission. The Maryland data do not

have unique patient identifiers, so visit linking and timing vari-ables for alternative readmission indicators are not available.Although not a perfect comparison, data on preventable read-missions for Medicare beneficiaries in the state of Maryland in2008 MedPAR data suggest that inter-hospital and out-of-statereadmissions are not a major concern for these rural hospitals.33

The differences in 30 day readmission rates for intra-hospital onlycompared to rates for intra- and inter-hospital readmissions rangefrom a low of an increase of .06 percentage points (Chester RiverHospital Center) to a high of a 2.42 percentage point increase(9.72% to 12.14% at Calvert Memorial Hospital).33

2.3. Methods

This analysis employs a difference-in-differences specification,used widely in the health services literature.10,34,35 The firstdifference is a pre-post comparison of hospitals that implementTPR during the study period. The second difference is the experi-ence of rural hospitals that did not participate in the programover the same time period. These non-participating hospitals are

46 non-federal, short-term acute hospitals in

Maryland

10 rural hospitals implemented Total

Patient Revenue

36 remaining hospitals in Maryland

2 rural hospitals are excluded- they

implemented TPR before the other 8

6 hospitals are excluded because they

did not report readmissions data

Results in 8 treatment hospitals (2 report

together)

Results in

1) 3 rural controls 2) 30 total controls

Fig. 1. Maryland hospitals included as treatment and controls.

K. Mortensen et al. / Healthcare ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 3

Please cite this article as: Mortensen K, et al. Innovative payment mechanisms in Maryland Hospitals: An empirical analysis ofreadmissions under total patient revenue. Healthcare (2014), http://dx.doi.org/10.1016/j.hjdsi.2014.03.002i

similar in that they are also rural with distinct service areas, andserve as a control group to difference out factors unrelated to TPRthat may have influenced readmission trends over the studyperiod. As a sensitivity check, we re-estimate the analysis usingthe control group of rural and urban hospitals in Maryland tocontrol for general trends in readmissions throughout the state.

We employ a fixed effects framework accounting for unob-served, time-invariant hospital characteristics, since hospitals'environments such as the policy environment, local health caremarkets and membership in integrated systems contribute to theircapacity to reduce readmissions.36 The fixed effects regressions areestimated in Stata 11.2 using the xtreg command, which estimateslinear panel data models. Linear models may be preferred whenestimating fixed effects models,37 and have been used in thereadmissions literature.38 They provide a clear interpretation ofboth the magnitude and sign of the interaction coefficient. How-ever, to address concerns about the limitations of the ordinaryleast squares framework, we also estimate probit regressions thatinclude hospital dummy variables as a sensitivity check (resultsavailable from authors upon request). We cluster the analysis atthe hospital level and use the robust command in Stata to calculateappropriate standard errors.

3. Results

Table 1 presents publicly available data on the eight hospitalsthat participated in the TPR program, as well as the three ruralcontrols and the larger set of control hospitals in the state.Treatment hospitals have statistically significantly fewer staffedbeds than the controls. The average number of staffed beds for thetreatment hospitals is 181, compared to 199 for the rural controlhospitals and 320 for all the control hospitals. The average of totaldischarges for treatment (11,921) is lower than the average forrural control hospitals (14,201) and all controls (19,777). Grosspatient revenue figures reported for fiscal year ending 2011 arefrom Medicare Cost report data available online from www.ahd.com. Average gross patient revenue for the treatment hospitals($191,753) is similar to that of the rural control hospitals($247,577) and all controls ($464,566).

Descriptive statistics aggregated for the treatment and controlhospitals are shown in Table 2. Patient characteristics includingage, race/ethnicity, health insurance status, and the number ofchronic conditions all differ significantly each year among treat-ment and controls. Treatment hospitals on average serve older,white patients with primary payers of Medicare, while controlhospitals serve more minorities and more privately insuredpatients.

Although the treatment and controls hospitals differ on patientcharacteristics, the control hospitals are useful for identifyingtrends throughout the state that may affect readmissions. Thereadmission rate for treatment hospitals remained relatively con-stant over the study period, at 18.3% in 2009, 18.8% in 2010, and18.4% in 2011. The three rural control hospitals exhibit morevariation in readmission rates. The set of all control hospitals havelower readmission rates than the treatment hospitals, morestability than the rural controls, and end up slightly lower in2011 (12%) than in 2009 (12.8%).

Fig. 2 shows the aggregate trends in readmissions for treat-ment, rural controls and all control hospitals for 2009, 2010, and2011. The data points are coefficients from unadjusted linearprobability models. Readmission rates for treatment hospitals arerelatively constant before and after TPR implementation. Read-missions in rural control hospitals are more variable, while read-mission rates for all controls show little relatively little variabilityover the study period. The larger control group readmission rates

suggest there were no significant factors driving change in read-missions during this time period.

Results from the difference-in-differences analyses are pre-sented in Table 3. Coefficients and confidence intervals for the

Table 1Structural characteristics of treatment, rural control and all control hospitals – totalpatient revenue.

Hospital name Staffedbeds

Totaldischarges

Gross patientrevenue ($000)

TreatmentCalvert Memorial Hospital 116 9221 $124,295Carroll Hospital Center 197 17,311 $216,533Chester River Hospital Center 53 3522 $62,804Dorchester General Hospital inCambridgea

– – –

The Memorial Hospital atEaston

179 13,297 $251,802

Meritus Medical Center 288 18,107 $297,359Union Hospital 140 8002 $126,899Western Maryland RegionalMedical Center

295 13,985 $265,796

Average 181 11,921 $191,753

Rural ControlsCivista Medical Center 120 9151 $113,146Frederick Memorial Hospital 294 18,318 $369,029Upper Chesapeake MedicalCenter

182 15,787 $226,535

Average 199n 14,201n $247,577n

All ControlsAnne Arundel Medical Center 316 23,397 $448,401Baltimore Washington MedicalCenter

311 20,705 $359,036

Doctors Community Hospital 190 12,357 $196,846Fort Washington MedicalCenter

37 3078 $47,665

Franklin Square Hospital Center 393 30,210 $523,816Greater Baltimore MedicalCenter

355 20,042 $424,052

Harford Memorial Hospital 105 6751 $100,235Holy Cross Hospital 450 36,596 $447,741Howard County GeneralHospital

227 18,988 $244,838

Johns Hopkins BayviewMedical Center

489 21,058 $530,282

Kernan Hospital 132 3244 $101,563Maryland General Hospital 180 6416 $187,278Medstar Good SamaritanHospital

317 17,098 $419,443

MedStar Harbor Hospital 221 14,528 $233,491MedStar Saint Mary's Hospital 108 10,264 $133,939MedStar Southern MarylandHospital

265 18,660 $232,657

Mercy Medical Center 293 21,108 $396,874Northwest Hospital 230 13,294 $221,889Saint Agnes Hospital 346 21,679 $474,665Shady Grove Adventist Hospital 367 27,050 $369,694Sinai Hospital of Baltimore 434 28,094 $772,605Suburban Hospital 239 13,696 $258,614The Johns Hopkins Hospital 918 48,442 $1,727,733Union Memorial Hospital 295 19,525 $514,879University of Maryland MedicalCenter

771 38,675 $2,523,977

University of Maryland SaintJoseph Medical

365 20,948 $380,038

Washington Adventist Hospital 281 18,067 $271,023

Average 320n 19,777n $464,566n

All data are publicly available. Financial data from Medicare Cost report data forperiod ending 6/30/2011 (HCRIS 3909-E) and data on total discharges are fromwww.ahd.com.

n Indicates statistically significant difference from control hospitals,two-sample t-tests at 95% confidence.

a Reports with Memorial at Easton.

K. Mortensen et al. / Healthcare ∎ (∎∎∎∎) ∎∎∎–∎∎∎4

Please cite this article as: Mortensen K, et al. Innovative payment mechanisms in Maryland Hospitals: An empirical analysis ofreadmissions under total patient revenue. Healthcare (2014), http://dx.doi.org/10.1016/j.hjdsi.2014.03.002i

difference-in-differences estimates with rural controls from thelinear probability model estimated with hospital fixed effects andclustered on hospital ID are in the first column. The explanatoryvariable of interest is the interaction term between post andtreatment. The coefficient on this interaction variable is notstatistically significant. The coefficient on the interaction fromthe linear model with all control hospitals, in the second column,is not statistically significant.

The linear probability model results show readmissions vary bydemographics, socio-economic characteristics, and presence ofchronic conditions. In the first model with rural controls, AfricanAmericans (coeff¼ .0105, pr .05) are more likely and other races(coeff¼� .0071, pr .05) are less likely than whites to have areadmission. Age is positively associated with readmissions(coeff¼ .0004, pr .05). The probability of readmissions is higherfor patients whose stays are paid by Medicare (coeff¼ .0290,

pr .05) and Medicaid (coeff¼ .0479, pr .05) relative to privateinsurance. The number of chronic conditions indicated at dis-charge (coeff¼ .0143, pr .05) is positively associated with read-mission. The estimates from the model with all control hospitalshad the same sign and significance for these same variables,except the coefficient on other races is no longer statisticallysignificant.

4. Discussion

Our results show that in the 18 months following implementa-tion of the program, TPR is not associated with decreases in theprobability of a patient readmission. This is consistent with recentfindings from one hospital participating in the TPR program,Western Maryland Health System. The hospital found that their

Table 2Characteristics of the Study Population: Total Patient Revenue, 2009–2011.

Characteristic Treatment hospitals Rural control hospitals All control hospitals

2009 2010 2011 2009 2010 2011 2009 2010 2011

ReadmissionsReadmission rate 18.3 18.8 18.4 18.5 21.9n 15.6n 12.8n 13.4n 12.0n

Age (years) 52.5 52.6 52.7 50.2n 50.7n 51.6n 48.3n 48.5n 48.6n

Percent Female 58.0 57.9 57.8 59.8n 58.7n 58.9n 58.4n 58.1 58.2n

Race/ethnicityWhite 87.7 88.8 88.6 78.8n 77.6n 78.2n 54.4n 53.8n 53.8n

Black 9.4 8.4 8.6 14.9n 15.0n 14.9n 35.3n 35.8n 35.7n

Hispanic 1.3 1.1 1.0 3.1n 3.9n 2.9n 4.0n 4.3n 4.5n

All other races 1.7 1.6 1.8 3.2n 3.4n 3.9n 5.9n 5.7n 5.6n

Primary payerMedicare 44.3 45.6 46.7 39.1n 39.5n 41.2n 35.1n 35.5n 36.0n

Medicaid 16.9 18.0 18.2 13.8n 14.6n 14.2n 20.0n 20.5n 20.8n

Private Insurance 32.8 31.0 29.3 40.0n 38.9n 37.7n 37.7n 36.0n 35.4n

Self-pay 3.9 3.4 3.7 3.5n 3.5 4.0 4.9n 5.3n 5.0n

No payment 2.0 2.0 2.1 3.6n 3.4n 3.3n 2.5n 2.8n 2.8n

Health Status# Chronic conditions 5.6 5.6 5.6 5.2n 5.4n 5.7n 4.8n 4.9n 5.0n

Authors' analysis of 2009–2011 Maryland State Inpatient Databases (SID).n Indicates statistically significant difference from treatment hospitals in each year, two-sample t-tests at 95% confidence.

Fig. 2. Monthly readmission rates for treatment, rural controls, and all control hospitals: Total Patient Revenue, 2009–2011.

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Please cite this article as: Mortensen K, et al. Innovative payment mechanisms in Maryland Hospitals: An empirical analysis ofreadmissions under total patient revenue. Healthcare (2014), http://dx.doi.org/10.1016/j.hjdsi.2014.03.002i

readmissions rate remained high at 16% in 2011 but droppedsignificantly to 9% in 2012.17 The program has enabled hospitals toenhance outpatient services, such as opening primary care clinics,diabetes clinics, behavioral health clinics, wound care centers, andfollowing up with discharged patients.17

There is much to be learned about innovative paymentmechanisms, so policymakers, organizations providing healthcare, and state health departments can benefit from this analysisof Maryland's experience with the TPR program. TPR is unique;rather than national policy that penalizes hospitals with highreadmission rates that may cause hospitals to forgo quality,21 TPRworks to create positive financial incentives to reduce unnecessaryadmissions and readmission rates while monitoring quality. Theincentives for hospitals to treat patients under TPR are differentthan under a prospective payment system like Medicare. Thefinancial incentives in Medicare are such that hospitals willcontinue to admit patients but reduce length of stay, whereasincentives under TPR encourage hospitals to invest in outpatientinfrastructure in the community to keep patients out of thehospital altogether.

The difference-in-differences approach we use provides astrong framework for analyzing the TPR program. It attributes achange in readmissions to the TPR program only if the change isconcurrent with implementation of the program, and if thechanges in readmissions in participating hospitals differ fromhospitals that did not participate. The hospital fixed effect

framework provides the opportunity to focus on changes withineach hospital, avoiding bias due to differences in the unobservablecharacteristics of these rural hospitals.35

This analysis has several limitations. The TPR program demon-strations were targeted to rural hospitals because of their defined,non-overlapping service areas, so participation in TPR was notrandomly assigned, and is not intended for urban and suburbanhospitals. Maryland is a relatively small state geographically, with46 non-federal, short-term acute hospitals, which limits thesample of rural hospitals that can appropriately be used forcontrols. The HCUP does not contain readmission data for neigh-boring states; the closest state with the readmit variable in the SIDis New Jersey. There are also limitations to the difference-in-differences model,39 which we attempt to address by clusteringstandard errors at the hospital level.

Readmissions data for the state of Maryland are not alwaysreadily accessible.23 Maryland uses its own penalty system forreadmissions32 so it is exempted from the national HospitalReadmissions Reduction Program and penalties instituted by theCenters for Medicare & Medicaid Services and mandated by theAffordable Care Act. Data from the Dartmouth Atlas do not reportaggregate readmission data for Maryland.23 Thus, the tabulationswe calculate from the HCUP data cannot be cross-verified. Weview this as a potential limitation because we had to remove twohospitals from our initially selected rural control group sinceneither hospital reported any readmissions for either one of thecalendar years or the entire three year sample. Despite theselimitations, the results have important implications for hospitalsparticipating in the TPR program, hospitals considering participa-tion, and local health systems across the country.

5. Conclusions

This paper provides early evidence of the effects of the TPRprogram on rural hospital readmissions. The program continuesthrough at least 2013, so this is not a comprehensive evaluation ofthe program. The results show that participating hospitals did notreduce their readmissions relative to control hospitals in the first18 months of the program. The program targeted rural hospitals,perhaps because of their clearly defined service areas, but alsoperhaps because rural hospitals do not have the same integratedstructure and capacity as urban hospitals, and global budgetingprovides them the incentive and financial stability to undertakesystematically these efforts. Streamlining outpatient and inpatientservices and other steps towards integration take time, so it isreasonable to expect that the TPR program did not instantaneouslyreduce hospital readmission rates.

Acknowledgements

The authors would like to thank Melinda Beeuwkes Buntin,anonymous reviewers, and seminar participants at University ofMinnesota Medical Industry Leadership Institute, George MasonUniversity, AcademyHealth, and the International Health EconomicsAssociation in Sydney, Australia for valuable feedback. We gratefullyacknowledge support from the Eunice Kennedy Shriver NationalCenter for Child Health and Human Development Grant R24-HD041041, Maryland Population Research Center. We thank JackMeyer for guidance and support.

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Table 3Difference-in-differences regression estimates of the effect of total patient revenueon hospital readmissions.

Characteristic Linear probabilitymodel with fixedeffects rural controlsn¼374,353(10 hospitals)

Linear probabilitymodel with fixedeffects all controlsn¼1,997,164(38 hospitals)

Post � .0261 � .0079n

(� .0680, .0159) (� .0157, � .0002)Post*treatment .0260 .0080

(� .0173, .0693) (� .0039, .0199)Female .0012 � .0002

(� .0159, .0183) (� .0090, .0037)Black .0105n .0084n

(.0017, .0193) (.0023, .0145)Hispanic � .0048 .0076

(� .0305, .0210) (� .0078, .0231)Other races � .0071n � .0046

(� .0136, � .0006) (.0115, .0022)Age .0004n .0003n

(.0001, .0008) (.0001, .0005)Income quartile 1 � .0012 � .0114

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(� .0257, .0152) (� .0145, .0020)Income quartile 3 � .0071 � .0037

(� .0246, .0104) (� .0084, .0009)Self-pay .0127 � .0017

(� .0017, .0272) (� .0212, .0177)No payment .0031 � .0075

(� .0113, .0176) (� .0333, .0184)Medicaid .0479n .0325n

(.0304, .0654) (.0235, .0415)Medicare .0290n .0234n

(.0132, .0448) (.0154, .0316)# Chronic conditions .0143n .0123n

(.0102, .0185) (.0100, .0147)Constant .0693n .0501n

(.0329, .1056) (.0331, .0672)

Author's calculations using the 2009–2011 Maryland State Inpatient Databases(SID). Robust standard errors are clustered by hospital.

n p-Valuer .05.

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Please cite this article as: Mortensen K, et al. Innovative payment mechanisms in Maryland Hospitals: An empirical analysis ofreadmissions under total patient revenue. Healthcare (2014), http://dx.doi.org/10.1016/j.hjdsi.2014.03.002i

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Please cite this article as: Mortensen K, et al. Innovative payment mechanisms in Maryland Hospitals: An empirical analysis ofreadmissions under total patient revenue. Healthcare (2014), http://dx.doi.org/10.1016/j.hjdsi.2014.03.002i