Measuring individual physician clinical productivity in an era of consolidated group practices - Butala, Neel M. (Healthc (Amst), 2019)
BACKGROUND: As physician groups consolidate and value-based payment replaces traditional fee-for-service systems, physician practices have greater need to accurately measure individual physician clinical productivity within team-based systems. We compared methodologies to measure individual physician outpatient clinical productivity after adjustment for shared practice resources. METHODS: For cardiologists at our hospital between January 2015-June 2016, we assessed productivity by examining completed patient visits per clinical session per week. Using mixed-effects models, we sequentially accounted for shared practice resources and underlying baseline characteristics. We compared mixed-effects and Generalized Estimating Equations (GEE) models using K-fold cross validation, and compared mixed-effect, GEE, and Data Envelopment Analysis (DEA) models based on ranking of physicians by productivity. RESULTS: A mixed-effects model adjusting for shared practice resources reduced variation in productivity among providers by 63% compared to an unadjusted model. Mixed-effects productivity rankings correlated strongly with GEE rankings (Spearman 0.99), but outperformed GEE on K-fold cross validation (root mean squared error 2.66 vs 3.02; mean absolute error 1.89 vs 2.20, respectively). Mixed-effects model rankings had moderate correlation with DEA model rankings (Spearman 0.543), though this improved upon exclusion of outliers (Spearman 0.809). CONCLUSIONS: Mixed-effects modeling accounts for significant variation in productivity secondary to shared practice resources, outperforms GEE in predictive power, and is less vulnerable to outliers than DEA. IMPLICATIONS: With mixed-effects regression analysis using otherwise easily accessible administrative data, practices can evaluate physician clinical productivity more fairly and make more informed management decisions on physician compensation and resource allocation.