Assessing the accuracy of race-and-ethnicity data in the Outcome and Assessment Information Set.

ABSTRACT

BACKGROUND: Limitations in the quality of race-and-ethnicity information in Medicare's data systems constrain efforts to assess disparities in care among older Americans. Using demographic information from standardized patient assessments may be an efficient way to enhance the accuracy and completeness of race-and-ethnicity information in Medicare's data systems, but it is critical to first establish the accuracy of these data as they may be prone to inaccurate observer-reported or third-party-based information. This study evaluates the accuracy of patient-level race-and-ethnicity information included in the Outcome and Assessment Information Set (OASIS) submitted by home health agencies. METHODS: We compared 2017-2022 OASIS-D race-and-ethnicity data to gold-standard self-reported information from the Medicare Consumer Assessment of Healthcare Providers and Systems® survey in a matched sample of 304,804 people with Medicare coverage. We also compared OASIS data to indirect estimates of race-and-ethnicity generated using the Medicare Bayesian Improved Surname and Geocoding (MBISG) 2.1.1 method and to existing Centers for Medicare & Medicaid Services (CMS) administrative records. RESULTS: Compared with existing CMS administrative data, OASIS data are far more accurate for Hispanic, Asian American and Native Hawaiian or other Pacific Islander, and White race-and-ethnicity; slightly less accurate for American Indian or Alaska Native race-and-ethnicity; and similarly accurate for Black race-and-ethnicity. However, MBISG 2.1.1 accuracy exceeds that of both OASIS and CMS administrative data for every racial-and-ethnic category. Patterns of inconsistent reporting of racial-and-ethnic information among people for whom there were multiple observations in the OASIS and Consumer Assessment of Healthcare Providers and Systems (CAHPS) datasets suggest that some of the inaccuracies in OASIS data may result from observation-based reporting that lessens correspondence with self-reported data. CONCLUSIONS: When health record data on race-and-ethnicity includes observer-reported information, it can be less accurate than both true self-report and a high-performing imputation approach. Efforts are needed to encourage collection of true self-reported data and explicit record-level data on the source of race-and-ethnicity information.