Does Use of a Hospital-wide Readmission Measure Versus Condition-specific Readmission Measures Make a Difference for Hospital Profiling and Payment Penalties?

Effect of Medicare Advantage on Hospital Readmission and Mortality Rankings

BACKGROUND

Medicare links hospital performance on readmissions and mortality to payment solely on the basis of outcomes among fee-for-service (FFS) beneficiaries. Whether including Medicare Advantage (MA) beneficiaries, who account for nearly half of all Medicare beneficiaries, in the evaluation of hospital performance affects rankings is unknown.

OBJECTIVE

To determine if the inclusion of MA beneficiaries in readmission and mortality measures reclassifies hospital performance rankings compared with current measures.

DESIGN

Cross-sectional.

SETTING

Population-based.

PARTICIPANTS

Hospitals participating in the Hospital Readmissions Reduction Program or Hospital Value-Based Purchasing Program.

MEASUREMENTS

Using the 100% Medicare files for FFS and MA claims, the authors calculated 30-day risk-adjusted readmissions and mortality for acute myocardial infarction, heart failure, chronic obstructive pulmonary disease, and pneumonia on the basis of only FFS beneficiaries and then both FFS and MA beneficiaries. Hospitals were divided into quintiles of performance based on FFS beneficiaries only, and the proportion of hospitals that were reclassified to a different performance group with the inclusion of MA beneficiaries was calculated.

RESULTS

Of the hospitals in the top-performing quintile for readmissions and mortality based on FFS beneficiaries, between 21.6% and 30.2% were reclassified to a lower-performing quintile with the inclusion of MA beneficiaries. Similar proportions of hospitals were reclassified from the bottom performance quintile to a higher one across all measures and conditions. Hospitals with a higher proportion of MA beneficiaries were more likely to improve in performance rankings.

LIMITATION

Hospital performance measurement and risk adjustment differed slightly from those used by Medicare.

CONCLUSION

Approximately 1 in 4 top-performing hospitals is reclassified to a lower performance group when MA beneficiaries are included in the evaluation of hospital readmissions and mortality. These findings suggest that Medicare's current value-based programs provide an incomplete picture of hospital performance.

PRIMARY FUNDING SOURCE

Laura and John Arnold Foundation.

The Effects of Certificate-of-Need Laws on the Quality of Hospital Medical Services

Certificate-of-need (CON) laws restrict entry into health services by requiring healthcare providers to seek approval from state healthcare regulators before making any major capital expenditures. An important question is whether CON laws influence the quality of medical services in CON law states. For instance, if CON laws actually lower the quality of medical services, they fail to achieve their intended effect. This paper tests the hypothesis that hospitals in states with CON laws provide lower-quality services than hospitals in states without CON laws. Our overall results suggest that CON regulations lead to lower-quality care for some quality measures and have little or no effect on other quality standards. The results remain consistent across several robustness tests.

Association of Inclusion of Medicare Advantage Patients in Hospitals' Risk-Standardized Readmission Rates, Performance, and Penalty Status

Importance

The Hospital Readmissions Reduction Program publicly reports and financially penalizes hospitals according to 30-day risk-standardized readmission rates (RSRRs) exclusively among traditional Medicare (TM) beneficiaries but not persons with Medicare Advantage (MA) coverage. Exclusively reporting readmission rates for the TM population may not accurately reflect hospitals' readmission rates for older adults.

Objective

To examine how inclusion of MA patients in hospitals' performance is associated with readmission measures and eligibility for financial penalties.

Design, Setting, and Participants

This is a retrospective cohort study linking the Medicare Provider Analysis and Review file with the Healthcare Effectiveness Data and Information Set at 4070 US acute care hospitals admitting both TM and MA patients. Participants included patients admitted and discharged alive with a diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia between 2011 and 2015. Data analyses were conducted between April 1, 2018, and November 20, 2020.

Exposures

Admission to an acute care hospital.

Main Outcomes and Measures

The outcome was readmission for any reason occurring within 30 days after discharge. Each hospital's 30-day RSRR was computed on the basis of TM, MA, and all patients and estimated changes in hospitals' performance and eligibility for financial penalties after including MA beneficiaries for calculating 30-day RSRRs.

Results

There were 748 033 TM patients (mean [SD] age, 76.8 [83] years; 360 692 [48.2%] women) and 295 928 MA patients (mean [SD] age, 77.5 [7.9] years; 137 422 [46.4%] women) hospitalized and discharged alive for AMI; 1 327 551 TM patients (mean [SD] age, 81 [8.3] years; 735 855 [55.4%] women) and 457 341 MA patients (mean [SD] age, 79.8 [8.1] years; 243 503 [53.2%] women) for CHF; and 2 017 020 TM patients (mean [SD] age, 80.7 [8.5] years; 1 097 151 [54.4%] women) and 610 790 MA patients (mean [SD] age, 79.6 [8.2] years; 321 350 [52.6%] women) for pneumonia. The 30-day RSRRs for TM and MA patients were correlated (correlation coefficients, 0.31 for AMI, 0.40 for CHF, and 0.41 for pneumonia) and the TM-based RSRR systematically underestimated the RSRR for all Medicare patients for each condition. Of the 2820 hospitals with 25 or more admissions for at least 1 of the outcomes of AMI, CHF, and pneumonia, 635 (23%) had a change in their penalty status for at least 1 of these conditions after including MA data. Changes in hospital performance and penalty status with the inclusion of MA patients were greater for hospitals in the highest quartile of MA admissions.

Conclusions and Relevance

In this cohort study, the inclusion of data from MA patients changed the penalty status of a substantial fraction of US hospitals for at least 1 of 3 reported conditions. This suggests that policy makers should consider including all hospital patients, regardless of insurance status, when assessing hospital quality measures.

The 30-day unplanned readmission rate and hospital volume: a national population-based study in South Korea

OBJECTIVE

To examine the association between hospital volume and the unplanned 30-day readmission rate as a quality measure.

DESIGN

A retrospective cross-sectional study.

SETTING

The Korea healthcare system is operated by a single payer under the National Health Insurance Service.

PARTICIPANTS

Using national health claims data of the Health Insurance Review and Assessment in South Korea, we examined 1 296 275 adult discharges (≥18 years old) from 90 hospitals (≥500 beds) in the 2013 calendar year.

MAIN OUTCOME MEASURES

We analysed the 30-day, unplanned, observed-to-expected standardized readmission rate for hospitals and for five specialty cohorts: medicine, surgery/gynaecology, cardiovascular, cardiorespiratory, and neurology. We assessed the association between hospital volume by tertiles and the 30-day standardized readmission rates with and without adjustment for hospital characteristics.

RESULTS

The rate for the lowest-volume hospitals was 6.10 compared with 6.20 for the highest-volume hospitals. We observed the standardized readmission rates did not differ significantly between the lowest- and highest-volume groups, except for the neurology cohort, which remained significant after adjusting for hospital characteristics.

CONCLUSIONS

The standardized readmission rates were not associated with hospital volume, except for the neurology cohort, in which the standardized readmission rate was significantly higher in the highest-volume hospitals than in lowest- and intermediate-volume hospitals, which was not consistent with the typical association of greater hospital volume with better outcomes. This association was independent of hospital characteristics. Therefore, the rate of readmissions should be used with caution when gauging the quality of hospital care according to hospital volume.

Racial Disparities in Readmission Rates among Patients Discharged to Skilled Nursing Facilities

OBJECTIVES

Prior studies have reported mixed findings about the existence of racial disparities in readmission rates among Medicare Advantage beneficiaries, but these studies used data from one state, focused on black-white disparities, and did not focus on patients discharged to skilled nursing facilities (SNFs). The objective of the study was to characterize racial and ethnic disparities in rates of 30-day rehospitalization directly from SNFs among fee-for-service and Medicare Advantage patients.

DESIGN

A cross-sectional study of admissions to SNFs in 2015 was conducted.

SETTING

SNFs across the United States.

PARTICIPANTS

The sample included 1 500 334 white, 213 848 African American, and 99 781 Hispanic Medicare patients who were admitted to 13 375 SNFs.

MEASUREMENTS

The main outcome of interest was readmission, identified as patients sent back to any hospital directly from the SNF within 30 days of admission, as indicated on the Minimum Data Set discharge assessment.

RESULTS

Overall readmission rates for fee-for-service patients were 16.7% (95% confidence interval [CI] = 16.7%-16.8%) for whites, 18.8% (95% CI = 18.7%-19.0%) for African Americans, and 17.4% (95% CI = 17.1%-17.7%) for Hispanics. Readmission rates in Medicare Advantage were 14.7% (95% CI = 14.5%-14.8%) for whites, 16.8% (95% CI = 16.6%-17.1%) for African Americans, and 15.3% (95% CI = 14.9%-15.6%) for Hispanics. We also found that African Americans had about 1% higher readmission rates than whites, even when they received care within the same SNF. No statistically significant differences were found in the magnitude of within-SNF racial disparities in Medicare Advantage compared with Medicare fee-for-service.

CONCLUSION

We found racial disparities in readmission rates even within the same facility for both Medicare Advantage and fee-for-service beneficiaries. Intervention to reduce disparities in readmission rates, as well as more comprehensive quality measures that incorporate outcomes for Medicare Advantage enrollees, are needed. J Am Geriatr Soc 67:1672-1679, 2019.

Pay-for-Performance and Veteran Care in the VHA and the Community: a Systematic Review

BACKGROUND

Although pay-for-performance (P4P) strategies have been used by the Veterans Health Administration (VHA) for over a decade, the long-term benefits of P4P are unclear. The use of P4P is further complicated by the increased use of non-VHA healthcare providers as part of the Veterans Choice Program. We conducted a systematic review and key informant interviews to better understand the effectiveness and potential unintended consequences of P4P, as well as the implementation factors and design features important in both VHA and non-VHA/community settings.

METHODS

We searched PubMed, PsycINFO, and CINAHL through March 2017 and reviewed reference lists. We included trials and observational studies of P4P targeting Veteran health. Two investigators abstracted data and assessed study quality. We interviewed VHA stakeholders to gain further insight.

RESULTS

The literature search yielded 1031 titles and abstracts, of which 30 studies met pre-specified inclusion criteria. Twenty-five examined P4P in VHA settings and 5 in community settings. There was no strong evidence supporting the effectiveness of P4P in VHA settings. Interviews with 17 key informants were consistent with studies that identified the potential for overtreatment associated with performance metrics in the VHA. Key informants' views on P4P in community settings included the need to develop relationships with providers and health systems with records of strong performance, to improve coordination by targeting documentation and data sharing processes, and to troubleshoot the limited impact of P4P among practices where Veterans make up a small fraction of the patient population.

DISCUSSION

The evidence to support the effectiveness of P4P on Veteran health is limited. Key informants recognize the potential for unintended consequences, such as overtreatment in VHA settings, and suggest that implementation of P4P in the community focus on relationship building and target areas such as documentation and coordination of care.

Applicability of Publicly Reported Hospital Readmission Measures to Unreported Conditions and Other Patient Populations: A Cross-sectional All-Payer Study

BACKGROUND

Readmission rates after hospitalizations for heart failure (HF), acute myocardial infarction (AMI), and pneumonia among Medicare beneficiaries are used to assess quality and determine reimbursement. Whether these measures reflect readmission rates for other conditions or insurance groups is unknown.

OBJECTIVE

To investigate whether hospital-level 30-day readmission measures for publicly reported conditions (HF, AMI, and pneumonia) among Medicare patients reflect those for Medicare patients hospitalized for unreported conditions or non-Medicare patients hospitalized with HF, AMI, or pneumonia.

DESIGN

Cross-sectional.

SETTING

Population-based.

PARTICIPANTS

Hospitals in the all-payer Nationwide Readmissions Database in 2013 and 2014.

MEASUREMENTS

Hospital-level 30-day all-cause risk-standardized excess readmission ratios (ERRs) were compared for 3 groups of patients: Medicare beneficiaries admitted for HF, AMI, or pneumonia (Medicare reported group); Medicare beneficiaries admitted for other conditions (Medicare unreported group); and non-Medicare beneficiaries admitted for HF, AMI, or pneumonia (non-Medicare group).

RESULTS

Within-hospital differences in ERRs varied widely among groups. Medicare reported ratios differed from Medicare unreported ratios by more than 0.1 for 29% of hospitals and from non-Medicare ratios by more than 0.1 for 46% of hospitals. Among hospitals with higher readmission ratios, ERRs for the Medicare reported group tended to overestimate ERRs for the non-Medicare group but underestimate those for the Medicare unreported group.

LIMITATION

Medicare groups and risk adjustment differed slightly from those used by the Centers for Medicare & Medicaid Services.

CONCLUSION

Hospital ERRs, as estimated by Medicare to determine financial penalties, have poor agreement with corresponding measures for populations and conditions not tied to financial penalties. Current publicly reported measures may not be good surrogates for overall hospital quality related to 30-day readmissions.

PRIMARY FUNDING SOURCE

Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology.

Did case-based payment influence surgical readmission rates in France? A retrospective study

OBJECTIVES

To determine whether implementation of a case-based payment system changed all-cause readmission rates in the 30 days following discharge after surgery, we analysed all surgical procedures performed in all hospitals in France before (2002-2004), during (2005-2008) and after (2009-2012) its implementation.

SETTING

Our study is based on claims data for all surgical procedures performed in all acute care hospitals with >300 surgical admissions per year (740 hospitals) in France over 11 years (2002-2012; n=51.6 million admissions).

INTERVENTIONS

We analysed all-cause 30-day readmission rates after surgery using a logistic regression model and an interrupted time series analysis.

RESULTS

The overall 30-day all-cause readmission rate following discharge after surgery increased from 8.8% to 10.0% (P<0.001) for the public sector and from 5.9% to 8.6% (P<0.001) for the private sector. Interrupted time series models revealed a significant linear increase in readmission rates over the study period in all types of hospitals. However, the implementation of case-based payment was only associated with a significant increase in rehospitalisation rates for private hospitals (P<0.001).

CONCLUSION

In France, the increase in the readmission rate appears to be relatively steady in both the private and public sector but appears not to have been affected by the introduction of a case-based payment system after accounting for changes in care practices in the public sector.

A propensity score analysis of the impact of surgical intervention on unexpected 30-day readmission following admission for subdural hematoma

OBJECTIVE

Subdural hematoma (SDH) is a common disease that is increasingly being managed nonoperatively. The all-cause readmission rate for SDH has not previously been described. This study seeks to describe the incidence of unexpected 30-day readmission in a cohort of patients admitted to an academic neurosurgical center. Additionally, the relationship between operative management, clinical outcome, and unexpected readmission is explored.

METHODS

This is an observational study of 200 consecutive adult patients with SDH admitted to the neurosurgical ICU of an academic medical center. Demographic information, clinical characteristics, and treatment strategies were compared between readmitted and nonreadmitted patients. Multivariable logistic regression, weighted by the inverse probability of receiving surgery using propensity scores, was used to evaluate the association between operative management and unexpected readmission.

RESULTS

Of 200 total patients, 18 (9%) died during hospitalization and were not included in the analysis. Overall, 48 patients (26%) were unexpectedly readmitted within 30 days. Sixteen patients (33.3%) underwent SDH evacuation during their readmission. Factors significantly associated with unexpected readmission were nonoperative management (72.9% vs 54.5%, p = 0.03) and female sex (50.0% vs 32.1%, p = 0.03). In logistic regression analysis weighted by the inverse probability of treatment and including likely confounders, surgical management was not associated with likelihood of a good outcome at hospital discharge, but was associated with significantly reduced odds of unexpected readmission (OR 0.19, 95% CI 0.08-0.49).

CONCLUSIONS

Over 25% of SDH patients admitted to an academic neurosurgical ICU were unexpectedly readmitted within 30 days. Nonoperative management does not affect outcome at hospital discharge but is significantly associated with readmission, even when accounting for the probability of treatment by propensity score weighted logistic regression. Additional research is needed to validate these results and to further characterize the impact of nonoperative management on long-term costs and clinical outcomes.

What is the impact of a clinically related readmission measure on the assessment of hospital performance?

BACKGROUND

The Hospital Readmission Reduction Program (HRRP) penalizes hospitals for high all-cause unplanned readmission rates. Many have expressed concern that hospitals serving patient populations with more comorbidities, lower incomes, and worse self-reported health status may be disproportionately penalized by readmissions that are not clinically related to the index admission. The impact of including clinically unrelated readmissions on hospital performance is largely unknown. We sought to determine if a clinically related readmission measure would significantly alter the assessment of hospital performance.

METHODS

We analyzed Medicare claims for beneficiaries in Michigan admitted for pneumonia and joint replacement from 2011 to 2013. We compared each hospital's 30-day readmission rate using specifications from the HRRP's all-cause unplanned readmission measure to values calculated using a clinically related readmission measure.

RESULTS

We found that the mean 30-day readmission rates were lower when calculated using the clinically related readmission measure (joint replacement: all-cause 5.8%, clinically related 4.9%, p < 0.001; pneumonia: all cause 12.5%, clinically related 11.3%, p < 0.001)). The correlation of hospital ranks using both methods was strong (joint replacement: 0.95 (p < 0.001), pneumonia: 0.90 (p < 0.001)).

CONCLUSIONS

Our findings suggest that, while greater specificity may be achieved with a clinically related measure, clinically unrelated readmissions may not impact hospital performance in the HRRP.

Effect of a Hospital-wide Measure on the Readmissions Reduction Program

BACKGROUND

The Hospital Readmissions Reduction Program penalizes hospitals that have high 30-day readmission rates across specific conditions. There is support for changing to a hospital-wide readmission measure to broaden hospital eligibility and provide incentives for improvement across more conditions.

METHODS

We used Medicare claims from 2011 through 2013 to evaluate the number of hospitals that were eligible for penalties, in that they met a volume threshold of 25 admissions over a 3-year period for a specific condition or 25 admissions over a 1-year period for the cohorts included in the hospital-wide measure. We estimated the expected effects that changing from the condition-specific readmission measures to a hospital-wide measure would have on average penalties for safety-net hospitals (i.e., hospitals that treat a large proportion of low-income patients) and other hospitals.

RESULTS

Our sample included 6,807,899 admissions for the hospital-wide measure and 4,392,658 admissions for the condition-specific measures. Of 3443 hospitals, 688 were considered to be safety-net hospitals. Changing to the hospital-wide measure would result in 76 more hospitals being eligible to receive penalties. The hospital-wide measure would increase penalties (mean [±SE] Medicare payment reductions across all hospitals) from 0.42±0.01% to 0.89±0.01% of Medicare base diagnosis-related-group payments. It would also increase the disparity in penalties between safety-net hospitals and other hospitals from -0.03±0.02 to 0.41±0.06 percentage points.

CONCLUSIONS

A transition to a hospital-wide readmission measure would only modestly increase the number of hospitals eligible for penalties and would substantially increase the penalties for safety-net hospitals.

Thirty-Day Hospital Revisit Rates and Factors Associated With Revisits in Patients Undergoing Septorhinoplasty

Importance

Estimates of the 30-day hospital revisit rate following septorhinoplasty and the risk factors associated with revisits are unknown in the current literature. Surgical 30-day readmission rates are important to establish, as they are increasingly used as a quality care metric and can incur future financial penalties from third-party payers and government agencies.

Objective

To determine the rate of 30-day hospital revisits following septorhinoplasty and the risk factors associated with revisits.

Design, Setting, and Participants

A retrospective cohort analysis was conducted of 175 842 patients undergoing septorhinoplasty between January 1, 2005, and December 31, 2009, using data from the Healthcare Cost and Utilization Project state inpatient database, state ambulatory surgery database, and state emergency department database from California, Florida, and New York. Information on revisits for these patients was collected from the 3 databases between January 1, 2005, and December 31, 2012. Data analysis was conducted from September 1, 2014, to May 1, 2015.

Main Outcomes and Measures

Hospital revisits within 30 days after an index septorhinoplasty and the primary diagnosis at the time of the revisit were the main outcome measures. The revisit rate was calculated within subgroups of patients based on different demographic and clinical characteristics. A multivariable model was then used to determine independent risk factors for the occurrence of a hospital revisit within 30 days of the septorhinoplasty procedure.

Results

In total, 11 456 of 175 842 patients (6.5%) who underwent septorhinoplasty procedures revisited the hospital within 30 days of the procedure. Most of these revisits (6353 [55.5%]) were to the emergency department. The most common primary diagnosis was bleeding or epistaxis, occurring in 2150 patients (1.2%). Multivariable logistic regression showed that patients aged 41 to 65 years (adjusted odds ratio [aOR], 1.09; 99% CI, 1.02-1.16) or older than 65 years (aOR, 1.23; 99% CI, 1.06-1.43) had an increased revisit rate, as did black patients (aOR, 1.39; 99% CI, 1.16-1.66); those with Medicare (aOR, 1.55; 99% CI, 1.32-1.81) and Medicaid (aOR, 1.63; 99% CI, 1.33-2.01); those with diagnoses of autoimmune disorders or immunodeficiency (aOR, 2.69; 99% CI, 1.20-6.03), coagulopathy (aOR, 2.06; 99% CI, 1.33-3.20), anxiety (aOR, 1.79; 99% CI, 1.55-2.07), and alcohol use (aOR, 1.70; 99% CI, 1.35-2.14); and those who had a conchal cartilage graft (aOR, 2.01; 99% CI, 1.29-3.14).

Conclusions and Relevance

The study results suggest that patients with more medical comorbidities and lower socioeconomic status most commonly returned to the emergency department for surgical complications, such as bleeding or epistaxis, in the 30-day period after the procedure. These data provide valuable preoperative counseling information for patients and physicians. In addition, this study provides data to third-party payers or government agencies in which postprocedure readmissions in the 30-day period are used as a quality care metric affecting reimbursements and financial penalties.

Level of Evidence

3.

Hospital Readmissions after Surgery: How Important Are Hospital and Specialty Factors?

BACKGROUND

Hospital readmission rates after surgery can represent an overall hospital effect or a combination of specialty and patient effects. We hypothesized that hospital readmission rates for procedures within specialties were more strongly correlated than rates across specialties within the same hospital.

STUDY DESIGN

For general, orthopaedic, and vascular specialties at Veterans Affairs hospitals during 2008 to 2014, 30-day risk-adjusted readmission rates were estimated for 6 high-volume procedures and each specialty. Relationships were assessed using the Pearson correlation coefficient.

RESULTS

At 84 hospitals, 64,724 orthopaedic, 24,963 general, and 10,399 vascular inpatient procedures were performed; mean readmission rates were 6.3%, 13.6%, and 16.4%, respectively. There was no correlation between specialty-specific adjusted hospital readmission rates: general and orthopaedic (r = 0.21; p = 0.06), general and vascular (r = 0.15; p = 0.19), and vascular and orthopaedic surgery (r = 0.07; p = 0.55). Within specialties, we found modest correlations between knee and hip arthroplasty readmission rates (r = 0.39; p < 0.01) and colectomy and ventral hernia repair (r = 0.24; p = 0.03), but not between lower-extremity bypass and endovascular aortic repair (r = 0.13; p = 0.26). Overall, controlling for patient-level factors, 1.9% of the variation in readmissions was attributable to specialty-level factors; only 0.6% was attributable to hospital-level factors.

CONCLUSIONS

Hospital readmission rates for orthopaedic, vascular, and general surgery were not correlated between specialties; within each of the 3 specialties, modest correlations were found between 2 procedures within 2 of these specialties. These findings suggest that hospital surgical readmission rates are primarily explained by patient- and procedure-specific factors and less by broader specialty and/or hospital effects.

Reliability of 30-Day Readmission Measures Used in the Hospital Readmission Reduction Program

OBJECTIVE

To assess the reliability of risk-standardized readmission rates (RSRRs) for medical conditions and surgical procedures used in the Hospital Readmission Reduction Program (HRRP).

DATA SOURCES

State Inpatient Databases for six states from 2011 to 2013 were used to identify patient cohorts for the six conditions used in the HRRP, which was augmented with hospital characteristic and HRRP penalty data.

STUDY DESIGN

Hierarchical logistic regression models estimated hospital-level RSRRs for each condition, the reliability of each RSRR, and the extent to which socioeconomic and hospital factors further explain RSRR variation. We used publicly available data to estimate payments for excess readmissions in hospitals with reliable and unreliable RSRRs.

PRINCIPAL FINDINGS

Only RSRRs for surgical procedures exceeded the reliability benchmark for most hospitals, whereas RSRRs for medical conditions were typically below the benchmark. Additional adjustment for socioeconomic and hospital factors modestly explained variation in RSRRs. Approximately 25 percent of payments for excess readmissions were tied to unreliable RSRRs.

CONCLUSIONS

Many of the RSRRs employed by the HRRP are unreliable, and one quarter of payments for excess readmissions are associated with unreliable RSRRs. Unreliable measures blur the connection between hospital performance and incentives, and threaten the success of the HRRP.