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Penalties for Readmission Rates or Patient Characteristics?
Study Questions:
Do patient characteristics (that are not included in Medicare’s current risk-adjustment methods) account for differences in hospital readmission rates?
Methods:
The authors analyzed data from the 2000-2010 biennial waves of the Health and Retirement Study (HRS), a nationally representative longitudinal survey of adults living in the United States, and linked data to Medicare claims from 2000-2012. The authors assessed 29 patient characteristics from survey data and claims as potential predictors of 30-day readmission when added to standard Medicare adjustments of hospital readmission rates. Readmissions for all hospitalizations were examined and not restricted to the condition-specific measures used in the Medicare Hospital Readmissions Reduction Program (i.e., congestive heart failure, myocardial infarction, and pneumonia). For comparisons of participants admitted to hospitals with high versus low readmission rates, the authors categorized index admissions into quintiles according to the admitting hospital’s publicly reported hospital-wide readmission rate from 2011-2012.
Results:
The proportion of admissions followed by readmission significantly differed across categories for 27 of the 29 patient characteristics not included in Centers for Medicare and Medicaid Services (CMS) adjustments. Twenty-two remained significantly predictive of readmission after standard CMS adjustments (p ≤ 0.04) for age, sex, discharge diagnosis, and specific diagnoses present in claims during the 12 months prior to admission; 17 of these 22 characteristics were distributed differently between the hospitals in the highest and lowest quintiles of readmission rates. Participants admitted to hospitals in the highest quintile had higher Hierarchical Condition Category (HCC) scores, more chronic conditions, less education, fewer assets, worse self-reported health status, more depressive symptoms, worse cognition, worse physical functioning, and more difficulties with activities of daily living.
Conclusions:
Several patient characteristics not currently included in risk adjustment of hospital readmission rates were significantly predictive of readmission and frequently distributed differently between hospitals in the highest and lowest quintiles of readmission rates.
Perspective:
This is an important contribution from a nationally representative study of readmission in the Medicare population. Medicare penalizes hospitals with higher than expected readmission rates up to 3% of annual inpatient payments. Such a process may be misguided. As the authors demonstrate, ‘The higher prevalence of clinical and social predictors of readmission among patients admitted to hospitals with higher readmission rates is likely driven by factors largely outside a hospital’s influence.’ Future legislation should consider adjustment for readmission rates based on patient and health-related variables.
Keywords: Activities of Daily Living, Centers for Medicare and Medicaid Services, U.S., Cognition, Depression, Geriatrics, Heart Failure, Myocardial Infarction, Patient Readmission, Pneumonia, Risk Adjustment, Secondary Prevention
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