Random Chance, Not Better Care, May Explain Hospitals’ Improvement Under Medicare Readmission Reduction Program

An AHRQ-funded study in JAMA Internal Medicine found that a statistical phenomenon known as regression to the mean (RTM), not better quality of care, accounted for nearly three-fourths of readmissions improvements by hospitals previously identified by Medicare as poor performers. RTM occurs when an outcome is measured repeatedly, often by random chance. Shortly after Medicare’s Hospital Readmission Reduction Program (HRRP) took effect in 2011, excess readmissions rates for patients with heart failure, heart attacks and pneumonia began to fall. Researchers who examined data from more than 3,200 hospitals found about three-fourths (74 percent to 86 percent) of the improvements in readmissions at hospitals were explained by the RTM phenomenon. To reduce the role of random chance or luck in classifying hospital performance, Medicare’s HRRP should consider excluding small hospitals that have greater variations in readmissions or calculate readmissions across a greater number of conditions, researchers concluded. Access the abstract.

2019 Jun 26. doi: 10.1001/jamainternmed.2019.1004. [Epub ahead of print]

Regression to the Mean in the Medicare Hospital Readmissions Reduction Program.

Joshi S1,2, Nuckols T3, Escarce J4, Huckfeldt P5, Popescu I4, Sood N1,2.

Author information

1: Sol Price School of Public Policy, Department of Health Policy and Management, University of Southern California, Los Angeles.
2: Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles.
3: Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California.
4: Department of General Internal Medicine, University of California, Los Angeles.
5: School of Public Health, Division of Health Policy and Management, University of Minnesota, Minneapolis.

Abstract

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.