Importance: Excess 30-day readmissions have declined substantially in hospitals initially penalized for high readmission rates under the Medicare Hospital Readmissions Reduction Program (HRRP). Although a possible explanation is that the policy incentivized penalized hospitals to improve care processes, another is regression to the mean (RTM), a statistical phenomenon that predicts entities farther from the mean in one period are likely to fall closer to the mean in subsequent (or preceding) periods owing to random chance. Objective: To quantify the contribution of RTM to declining readmission rates at hospitals initially penalized under the HRRP. Design, Setting, and Participants: This study analyzed data from Medicare Provider and Analysis Review files to assess changes in readmissions going forward and backward in time at hospitals with high and low readmission rates during the measurement window for the first year of the HRRP (fiscal year [FY] 2013) and for a measurement window that predated the FY 2013 measurement window for the HRRP among hospitals participating in the HRRP. Hospital characteristics are based on the 2012 survey by the American Hospital Association. The analysis included fee-for-service Medicare beneficiaries 65 years or older with an index hospitalization for 1 of the 3 target conditions of heart failure, acute myocardial infarction, or pneumonia or chronic obstructive pulmonary disease and who were discharged alive from February 1, 2006, through June 30, 2014, with follow-up completed by July 30, 2014. Data were analyzed from January 23, 2018, through March 29, 2019. Exposures: Hospital Readmission Reduction Program penalties. Main Outcome and Measures: The excess readmission ratio (ERR), calculated as the ratio of a hospital's readmissions to the readmissions that would be expected based on an average hospital with similar patients. Hospitals with ERRs of greater than 1.0 were penalized. Results: A total of 3258 hospitals were included in the study. For the 3 target conditions, hospitals with ERRs of greater than 1.0 during the FY 2013 measurement window exhibited decreases in ERRs in the subsequent 3 years, whereas hospitals with ERRs of no greater than 1.0 exhibited increases. For example, for patients with heart failure, mean ERRs declined from 1.086 to 1.038 (-0.048; 95% CI, -0.053 to -0.043; P <.001) at hospitals with ERRs of greater than 1.0 and increased from 0.917 to 0.957 (0.040; 95% CI, 0.036-0.044; P <.001) at hospitals with ERRs of no greater than 1.0. The same results, with ERR changes of similar magnitude, were found when the analyses were repeated using an alternate measurement window that predated the HRRP and followed up hospitals for 3 years (for patients with heart failure, mean ERRs declined from 1.089 to 1.044 [-0.045; 95% CI, -0.050 to -0.040; P <.001] at hospitals with below-mean performance and increased from 0.915 to 0.948 [0.033; 95% CI, 0.029 to 0.037; P <.001] at hospitals with above-mean performance). By comparing actual changes in ERRs with expected changes due to RTM, 74.3% to 86.5% of the improvement in ERRs for penalized hospitals was explained by RTM. Conclusions and Relevance: Most of the decline in readmission rates in hospitals with high rates during the measurement window for the first year of the HRRP appeared to be due to RTM. These findings seem to call into question the notion of an HRRP policy effect on readmissions.
|Original language||English (US)|
|Number of pages||7|
|Journal||JAMA internal medicine|
|State||Published - Sep 2019|
Bibliographical noteFunding Information:
receiving grants from the Agency for Healthcare Research and Quality (AHRQ) during the conduct of the study. Dr Nuckols reported receiving grant R01 HS024284 from the AHRQ during the conduct of the study and grants from the National Institute on Drug Abuse, National Institute of Diabetes and Digestive and Kidney Diseases, Patient-Centered Outcomes Research Institute, National Institutes of Health (NIH)/National Center for Advancing Translational Sciences, National Heart, Lung and Blood Institute, AHRQ, NIH/National Institute on Aging, and American Association of Neuromuscular and Electrodiagnostic Medicine outside the submitted work. Dr Escarce reported receiving grant R01 HS024284 from the AHRQ during the conduct of the study. Dr Huckfeldt reported receiving grants from the AHRQ during the conduct of the study and funding outside of the submitted work from the Assistant Secretary of Planning and Evaluation (ASPE) in the Department of Health and Human Services, Centers for Medicare & Medicaid Services, the Florida Medical Malpractice Joint Underwriting Association Patient Safety Grant Program, and the NIH; serving as a paid consultant to the RAND Corporation on NIH-funded research and as a peer reviewer; and serving as a paid consultant to the Urban Institute on a project funded by ASPE. Dr Popescu reported receiving grants from the AHRQ during the conduct of the study. Dr. Sood reported receiving grants from the AHRQ during the conduct of the study; serving as a scientific advisor for Virta Health, a start-up aiming to reverse diabetes through medication management and lifestyle change; receiving compensation for serving as an expert for the American Medical Association advising on the competitive effects of the CVS-Aetna merger; receiving compensation for serving as an expert for a consortium of law firms representing cities and jurisdictions suing pharmaceutical firms and distributors for the opioid crisis; receiving compensation for serving as an expert for China Development Research Foundation (Government of China) and the Pharmaceutical Research and Manufacturers of America on improving policies for controlling drugs costs and improving access to prescription drugs in China; and receiving grants from NIH, National Institute for Health Care Management Foundation, and Patient-Centered Outcomes Research Institute outside the submitted work. No other disclosures were reported.
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