Butala Neel M, Hidrue Michael K, Swersey Arthur J, Singh Jagmeet P, Weilburg Jeffrey B, Ferris Timothy G, Armstrong Katrina A, Wasfy Jason H
Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Massachusetts General Physicians Organization, Boston, MA, United States.
Healthc (Amst). 2019 Dec;7(4). doi: 10.1016/j.hjdsi.2019.02.001. Epub 2019 Feb 8.
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.
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.
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).
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.
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.
随着医师团体的合并以及基于价值的支付方式取代传统的按服务收费系统，医师执业机构更需要在基于团队的系统中准确衡量个体医师的临床生产力。我们比较了在调整共享执业资源后衡量个体医师门诊临床生产力的方法。
对于我院2015年1月至2016年6月期间的心脏病专家，我们通过检查每周每个临床时段完成的患者就诊次数来评估生产力。使用混合效应模型，我们依次考虑了共享执业资源和潜在的基线特征。我们使用K折交叉验证比较了混合效应模型和广义估计方程（GEE）模型，并根据医师生产力排名比较了混合效应模型、GEE模型和数据包络分析（DEA）模型。
与未调整的模型相比，调整共享执业资源的混合效应模型使提供者之间的生产力差异降低了63%。混合效应生产力排名与GEE排名高度相关（斯皮尔曼相关系数为0.99），但在K折交叉验证中表现优于GEE（均方根误差分别为2.66和3.02；平均绝对误差分别为1.89和2.20）。混合效应模型排名与DEA模型排名具有中等相关性（斯皮尔曼相关系数为0.692），不过在排除异常值后相关性有所提高（斯皮尔曼相关系数为0.755）。
混合效应建模考虑了共享执业资源导致的生产力显著差异，在预测能力方面优于GEE，并且比DEA更不容易受到异常值的影响。
通过使用其他易于获取的管理数据进行混合效应回归分析，执业机构可以更公平地评估医师临床生产力，并在医师薪酬和资源分配方面做出更明智的管理决策。
