Background: Readmission rates following total hip arthroplasty (THA) and total knee arthroplasty (TKA) are increasingly used to measure hospital performance. Readmission rates that are not adjusted for race/ethnicity and socioeconomic status, patient risk factors beyond a hospital’s control, may not accurately reflect a hospital’s performance. In this study, we examined the extent to which risk-adjusting for race/ethnicity and socioeconomic status affected hospital performance in terms of readmission rates following THA and TKA.
Methods: We calculated 2 sets of risk-adjusted readmission rates by (1) using the Centers for Medicare & Medicaid Services standard risk-adjustment algorithm that incorporates patient age, sex, comorbidities, and hospital effects and (2) adding race/ethnicity and socioeconomic status to the model. Using data from the Healthcare Cost and Utilization Project, 2011 State Inpatient Databases, we compared the relative performances of 1,194 hospitals across the 2 methods.
Results: Addition of race/ethnicity and socioeconomic status to the risk-adjustment algorithm resulted in (1) little or no change in the risk-adjusted readmission rates at nearly all hospitals; (2) no change in the designation of the readmission rate as better, worse, or not different from the population mean at >99% of the hospitals; and (3) no change in the excess readmission ratio at >97% of the hospitals.
Conclusions: Inclusion of race/ethnicity and socioeconomic status in the risk-adjustment algorithm led to a relative-performance change in readmission rates following THA and TKA at <3% of the hospitals. We believe that policymakers and payers should consider this result when deciding whether to include race/ethnicity and socioeconomic status in risk-adjusted THA and TKA readmission rates used for hospital accountability, payment, and public reporting.
Level of Evidence: Prognostic Level III. See instructions for Authors for a complete description of levels of evidence.
1RAND Corporation, Pittsburgh, Pennsylvania
2M.L. Barrett, Inc., San Diego, California
3Truven Health Analytics, Inc., Santa Barbara, California
4Agency for Healthcare Research and Quality, Rockville, Maryland
5RAND Corporation, Boston, Massachusetts
6Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
7Department of Orthopaedic Surgery, UCLA Medical Center, Los Angeles, California
8Truven Health Analytics, Inc., Bethesda, Maryland
E-mail address for G.R. Martsolf: firstname.lastname@example.org
E-mail address for R. Kandrack: email@example.com