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Wagner TH, Almenoff P, Francis J, Jacobs J, Pal Chee C. Assessment of the Medicare Advantage Risk Adjustment Model for Measuring Veterans Affairs Hospital Performance. JAMA Network Open. 2018 Dec 7; 1(8):e185993.
Importance: Policymakers and consumers are eager to compare hospitals on performance metrics, such as surgical complications or unplanned readmissions, measured from administrative data. Fair comparisons depend on risk adjustment algorithms that control for differences in case mix. Objective: To examine whether the Medicare Advantage risk adjustment system version 21 (V21) adequately risk adjusts performance metrics for Veterans Affairs (VA) hospitals. Design, Setting, and Participants: This cohort analysis of administrative data from all 5.5 million veterans who received VA care or VA-purchased care in 2012 was performed from September 8, 2015, to October 22, 2018. Data analysis was performed from January 22, 2016, to October 22, 2018. Exposures: A patient''s risk as measured by the V21 model. Main Outcomes and Measures: The main outcome was total cost, and the key independent variable was the V21 risk score. Results: Of the 5?472?629 VA patients (mean [SD] age, 63.0 [16.1] years; 5?118?908 [93.5%] male), the V21 model identified 694?706 as having a mental health or substance use condition. In contrast, a separate classification system for psychiatric comorbidities identified another 1?266?938 patients with a mental health condition. The V21 model missed depression not otherwise specified (396 062 [31.3%]), posttraumatic stress disorder (345 338 [27.3%]), and anxiety (129 808 [10.2%]). Overall, the V21 model underestimated the cost of care by $2314 (6.7%) for every person with a mental health diagnosis. Conclusions and Relevance: The findings suggest that current aspirations to engender competition by comparing hospital systems may not be appropriate or fair for safety-net hospitals, including the VA hospitals, which treat patients with complex psychiatric illness. Without better risk scores, which is technically possible, outcome comparisons may potentially mislead consumers and policymakers and possibly aggravate inequities in access for such vulnerable populations.