Alternative Risk-Adjustment Approaches to Assessing the Quality of Home Health Care: Final Report. ABSTRACT


Background and Purpose. The Outcome-Based Quality Improvement (OBQI) program provides reports to all Medicare-certified home health agencies so that they can identify potential quality problems and devise appropriate strategies to address them. There are 41 OBQI quality measures. A data-driven “stepwise” approach currently is used to risk adjust the OBQI indicators with a separate set of risk factors included in the risk-adjustment model for each outcome. The purpose of this project was to use a theory and evidence-based approach to develop and test alternative risk-adjustment models for the OBQI quality indicators within the frame of the existing Outcome and Assessment Information Set (OASIS) instrument.

Methods. The data analyzed in this project were obtained from the Centers for Medicare and Medicaid Services’ (CMS) contractor at the University of Colorado. They drew the data from the OASIS National Repository at CMS to create discrete episodes of home health care during calendar year 2001. In this project, alternative models were estimated sequentially after replicating the current risk-adjustment models. The first model was limited to the admission (or baseline) value of the outcome indicator and a core set of risk-adjusters. Subsequent models included a small number of outcome-specific risk-adjusters. Following development of a final set of alternative risk-adjustment models, an agency-level analysis was conducted to determine the impact on agencies’ quality ratings.

Results. The alternative models that include outcome-specific risk-adjusters typically have slightly lower explanatory power than the current models. This finding is not surprising since the “stepwise” approach used to develop current models is likely to result in models with close to the best explanatory power possible for the data set analyzed. The number of OASIS data items required to risk-adjust all outcomes, on the other hand, is considerably higher for the current compared with the alternative models. The agency-level analysis examined how the alternative approaches to risk-adjustment of the OBQI indicators affect an agency’s quality ratings as calculated by CMS for public reporting. For most agencies and most outcomes, the adjusted proportion of patients with an outcome and the agency’s ranking relative to other agencies is similar regardless of whether the current or alternative model is used to risk-adjust outcomes.

Conclusions. The results suggest that the relatively small reduction in explanatory power of most of the alternative risk-adjustment models for the OBQI indicators is unlikely to have a substantial effect on the quality ratings of the majority of agencies. A theory and evidence-based modeling approach, then, has the potential to simplify risk-adjustment and provide a consistent and stable basis for risk-adjustment relative to the current approach. This should make it more understandable to providers and encourage individual agencies to risk-adjust their own outcomes. The reliance on a smaller number of OASIS data elements, in addition, would contribute to the Department’s efforts to streamline the OASIS instrument and potentially facilitate the identification of a parsimonious set of clinical measures appropriate for data exchange in an electronic health record environment.

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