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Original Investigation
June 26, 2019

Regression to the Mean in the Medicare Hospital Readmissions Reduction Program

Author Affiliations
  • 1Sol Price School of Public Policy, Department of Health Policy and Management, University of Southern California, Los Angeles
  • 2Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles
  • 3Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
  • 4Department of General Internal Medicine, University of California, Los Angeles
  • 5School of Public Health, Division of Health Policy and Management, University of Minnesota, Minneapolis
JAMA Intern Med. Published online June 26, 2019. doi:10.1001/jamainternmed.2019.1004
Key Points

Question  Could regression to the mean explain some of the decline in readmissions at hospitals initially classified as below-mean performers under the Medicare Hospital Readmissions Reduction Program (HRRP)?

Findings  In this analysis of change in hospital readmission rates for specific conditions among Medicare beneficiaries, strong evidence suggests that most declines in excess readmission experienced after implementation of the HRRP at poorly performing hospitals was explained by a statistical phenomenon called regression to the mean. Regression to the mean signifies that entities farther away from the mean in one period are likely to be recorded closer to the mean in subsequent periods, simply by chance.

Meaning  Random chance or luck rather than improvement in quality of care appears to be the primary driver of improvements in readmissions experienced at hospitals initially classified as below-mean performers under the HRRP.


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.

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    1 Comment for this article
    The Principal Cause of Early Readmission
    Mark Ketterer, PhD | Henry Ford Hospital
    Diagnosis must preceed treatment. If the core behavioral cause of readmissions is not recognized, it cannot be ameliorated.

    Cognitive impairment is highly prevalent in conditions with high readmission rates, is the strongest prospective predictor of early readmission (Agarwal et al., 2016; Ketterer et al., 2017; Ketterer et al., 2014a; 2014b; Patel et al., 2015) , causes nonadherence (Cameron et al., 2010) and psychoeducational counselling for patients/families regarding reduces early readmission by 30-50% (Jasinski et al., 2018; Ketterer et al., 2016; Ketterer et al., 2019). Furthermore, cognitive impairment is unrecognized in 70-90% of cases (Agarwal
    et aal., 2016; Ketterer et al., 2014a; Raymont et al., 2004; Valcour et al., 2000).

    Persistence of low readmission rates beyond 30 days requires more study. Can families maintain the support affecting low early rates?

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    Jasinski MJ, Lumley MA, Soman S, Yee J & Ketterer MW. (2018). Family consultation to reduce early hospital readmissions among patients with end stage kidney disease: a randomized, controlled trial. Clinical Journal of the American Sociaety of Nephrology 13:850-857. DOI: https://doi.org/10.2215/CJN.08450817.

    Ketterer MW, Chawa M & Paone G. (2017). Prospective correlates of early (30 day) readmissions on a cardiothoracic surgery service. Psychology, Health & Medicine 22(8):947-954. https://doi.org/10.1080.13548506.2017.1287408.

    Ketterer, M. W., Draus, C., McCord, J., Mossallam, U., & Hudson, M. (2014a). Behavioral factors and hospital admissions/readmissions in patients with CHF. Psychosomatics, 55, 45–50. PMID: 24016384.

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