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Original Investigation
November 2018

Assessment of the Effect of Adjustment for Patient Characteristics on Hospital Readmission Rates: Implications for Pay for Performance

Author Affiliations
  • 1Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania
  • 2Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
  • 3Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
  • 4Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, Massachusetts
  • 5General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Boston, Massachusetts
JAMA Intern Med. 2018;178(11):1498-1507. doi:10.1001/jamainternmed.2018.4481
Key Points

Question  To what extent are differences in hospital readmission rates explained by measurable patient characteristics not used by Medicare to adjust for risk of readmissions in pay-for-performance programs?

Findings  This observational study of Medicare claims and US Census data found that adjustment for additional clinical and social variables reduced overall hospital variation in readmission rates by 9.6%, narrowed differences in rates between hospitals serving higher vs lower proportions of high-risk Medicare patients by 54%, and reduced expected penalties by 41% among the 10% of hospitals with the largest penalty reductions.

Meaning  Hospitals are penalized to some extent for serving sicker and poorer patients rather than for poorer quality of care, which highlights the need for improved risk adjustment in pay-for-performance programs.


Importance  In several pay-for-performance programs, Medicare ties payments to readmission rates but accounts only for a limited set of patient characteristics—and no measures of social risk—when assessing performance of health care providers (clinicians, practices, hospitals, or other organizations). Debate continues over whether accounting for social risk would mitigate inappropriate penalties or would establish lower standards of care for disadvantaged patients if they are served by lower-quality providers.

Objectives  To assess changes in hospital performance on readmission rates after adjusting for additional clinical and social patient characteristics by using methods that distinguish the association between patient characteristics and readmission from between-hospital differences in quality.

Design, Setting, and Participants  Using Medicare claims for admissions in 2013 through 2014 and linked US Census data, we assessed several clinical and social characteristics of patients that are not currently used for risk adjustment in the Hospital Readmission Reduction Program. We compared hospital readmission rates with and without adjustment for these additional characteristics, using only the average within-hospital associations between patient characteristics and readmission as the basis for adjustment, thereby appropriately excluding hospitals’ distinct contributions to readmission from the adjustment.

Main Outcomes and Measures  All-cause readmission within 30 days of discharge.

Results  The study sample consisted of 1 169 014 index admissions among 1 003 664 unique Medicare beneficiaries (41.5% men; mean [SD] age, 79.9 [8.3] years) in 2215 hospitals. Compared with adjustment for patient characteristics currently implemented by Medicare, adjustment for the additional characteristics reduced overall variation in hospital readmission rates by 9.6%, changed rates upward or downward by 0.37 to 0.72 percentage points for the 10% of hospitals most affected by the additional adjustments (±30.3% to ±58.9% of the hospital-level standard deviation), and would be expected to reduce penalties (in relative terms) by 52%, 46%, and 41% for hospitals with the largest 1%, 5%, and 10% of penalty reductions, respectively. The additional adjustments reduced the mean difference in readmission rates between hospitals in the top and bottom quintiles of high-risk patients by 0.53 percentage points (95% CI, 0.50-0.55; P < .001), or 54% of the difference estimated with CMS adjustments alone. Both clinical and social characteristics contributed to these reductions, and these reductions were considerably greater for conditions targeted by the Hospital Readmission Reduction Program. Adjustment for social characteristics resulted in greater changes in rates of readmission or death than in rates of readmission alone.

Conclusions and Relevance  Hospitals serving higher-risk patients may be penalized substantially because of the patients they serve rather than their quality of care. Adjusting solely for within-hospital associations may allow adjustment for additional patient characteristics to mitigate unintended consequences of pay for performance without holding hospitals to different standards because of the patients they serve.

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