Objective
Medicare's Hospital Readmissions Reduction Program (HRRP) does not account for social risk factors in risk adjustment, and this may lead the program to unfairly penalize safety‐net hospitals. Our objective was to determine the impact of adjusting for social risk factors on HRRP penalties.
Study Design
Retrospective cohort study.
Data Sources/Study Setting
Claims data for 2 952 605 fee‐for‐service Medicare beneficiaries with acute myocardial infarction (AMI), congestive heart failure (CHF) or pneumonia from December 2012 to November 2015.
Principal Findings
Poverty, disability, housing instability, residence in a disadvantaged neighborhood, and hospital population from a disadvantaged neighborhood were associated with higher readmission rates. Under current program specifications, safety‐net hospitals had higher readmission ratios (AMI, 1.020 vs 0.986 for the most affluent hospitals; pneumonia, 1.031 vs 0.984; and CHF, 1.037 vs 0.977). Adding social factors to risk adjustment cut these differences in half. Over half the safety‐net hospitals saw their penalty decline; 4‐7.5 percent went from having a penalty to having no penalty. These changes translated into a $17 million reduction in penalties to safety‐net hospitals.
Conclusions
Accounting for social risk can have a major financial impact on safety‐net hospitals. Adjustment for these factors could reduce negative unintended consequences of the HRRP.
To better understand the degree to which risk-standardized thirty-day readmission rates may be influenced by social factors, we compared results for hospitals in Missouri under two types of models. The first type of model is currently used by the Centers for Medicare and Medicaid Services for public reporting of condition-specific hospital readmission rates of Medicare patients. The second type of model is an “enriched” version of the first type of model with census tract-level socioeconomic data such as poverty rate, educational attainment, and housing vacancy rate. We found that the inclusion of these factors had a pronounced effect on calculated hospital readmission rates for patients admitted with acute myocardial infarction, heart failure, and pneumonia. Specifically, the models including socioeconomic data narrowed the range of observed variation in readmission rates for the above conditions, in percentage points, from 6.5 to 1.8, 14.0 to 7.4, and 7.4 to 3.7, respectively. Interestingly the average readmission rates for the three conditions did not change significantly between the two types of models. The results of our exploratory analysis suggest that further work to characterize and report the effects of socioeconomic factors on standardized readmission measures may assist efforts to improve care quality and deliver more equitable care on the part of hospitals, payers, and other stakeholders.
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