Decision models for distinguishing between clinically insignificant and significant tumors in prostate cancer biopsies: an application of Bayes' Theorem to reduce costs and improve outcomes.

Prostate cancer is the second leading cause of death from cancer, behind lung cancer, for men in the U. S, with nearly 30,000 deaths per year. A key problem is the difficulty in distinguishing, after biopsy, between significant cancers that should be treated immediately and clinically insignificant tumors that should be monitored by active surveillance. Prostate cancer has been over-treated; a recent European randomized screening trial shows overtreatment rates of 40%. Overtreatment of insignificant tumors reduces quality of life, while delayed treatment of significant cancers increases the incidence of metastatic disease and death. We develop a decision analysis approach based on simulation and probability modeling. For a given prostate volume and number of biopsy needles, our rule is to treat if total length of cancer in needle cores exceeds c, the cutoff value, with active surveillance otherwise, provided pathology is favorable. We determine the optimal cutoff value, c*. There are two misclassification costs: treating a minimal tumor and not treating a small or medium tumor (large tumors were never misclassified in our simulations). Bayes' Theorem is used to predict the probabilities of minimal, small, medium, and large cancers given the total length of cancer found in biopsy cores. A 20 needle biopsy in conjunction with our new decision analysis approach significantly reduces the expected loss associated with a patient in our target population about to undergo a biopsy. Longer needles reduce expected loss. Increasing the number of biopsy cores from the current norm of 10-12 to about 20, in conjunction with our new decision model, should substantially improve the ability to distinguish minimal from significant prostate cancer by minimizing the expected loss from over-treating minimal tumors and delaying treatment of significant cancers.

Measuring individual physician clinical productivity in an era of consolidated group practices.

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.