Neel M Butala1, Michael K Hidrue2, Arthur J Swersey3, Jagmeet P Singh1, Jeffrey B Weilburg2, Timothy G Ferris4, Katrina A Armstrong5, Jason H Wasfy6. 1. Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States. 2. Massachusetts General Physicians Organization, Boston, MA, United States. 3. Yale School of Management, New Haven, CT, United States. 4. Massachusetts General Physicians Organization, Boston, MA, United States; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States. 5. Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States. 6. Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Massachusetts General Physicians Organization, Boston, MA, United States. Electronic address: jwasfy@partners.org.
Abstract
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 and 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 0.692), though this improved upon exclusion of outliers (Spearman 0.755). 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.
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 and 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 0.692), though this improved upon exclusion of outliers (Spearman 0.755). 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.
Authors: John E Brush; Eileen M Handberg; Cathleen Biga; Kim K Birtcher; Alfred A Bove; Paul N Casale; Michael G Clark; Arthur Garson; Jerome L Hines; Jane A Linderbaum; George P Rodgers; Robert A Shor; Vinod H Thourani; Janet F Wyman Journal: J Am Coll Cardiol Date: 2015-05-19 Impact factor: 24.094
Authors: David Litaker; Lorraine Mion; Loretta Planavsky; Christopher Kippes; Neil Mehta; Joseph Frolkis Journal: J Interprof Care Date: 2003-08 Impact factor: 2.338
Authors: Jason H Wasfy; Michael K Hidrue; Jacqueline Ngo; Varsha K Tanguturi; Elizabeth T Cafiero-Fonseca; Ryan W Thompson; Natalie Johnson; Susan T McDermott; Jagmeet P Singh; Marcela G Del Carmen; Timothy G Ferris Journal: Circ Cardiovasc Qual Outcomes Date: 2020-05-12