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Improving the performance of the healthcare sector requires an understanding of the effectiveness and efficiency of care delivered by providers. Although this topic is of great interest to policymakers, researchers, and hospital managers, rigorous methods of measuring effectiveness and efficiency of care delivery have proven elusive. Through Data Envelopment Analysis (DEA), we make use of evidence from care delivered by emergency physicians, and develop scores that gauge physicians' performance in terms of effectiveness and efficiency. In order to validate our DEA scores, we independently use various Machine Learning (ML) algorithms, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Random Forest (RF), a Generalized Linear Model (GLM), and Least Absolute Shrinkage and Selection Operator (LASSO). After validating our DEA scores via comparison with predictions made by these algorithms, we make use of them to identify the distinguishing behaviors of highly effective and efficient physicians. We find that highly effective physicians order less tests compared to their peers and maintain their effectiveness when working under high workloads. We also observe that highly efficient physicians order less tests on average and become even more efficient during high-volume shifts. Importantly, our results indicate a statistically significant positive relationship between a physician's effectiveness and efficiency scores suggesting that, contrary to conventional wisdom, effectiveness and efficiency in care delivery should be viewed as compliments not substitutes. In addition, we find that effectiveness is lower among physicians who have higher job tenure or average test order count. Efficiency, however, is lower among physicians with less experience (measured in number of years after graduation from medical school) or high average test order count. Furthermore, our results indicate an increase in a physician's average efficiency and a decrease in his/her average effectiveness when faced with high workloads. Finally, we find evidence of peer influence on a focal physician's effectiveness and efficiency, which suggests an opportunity to improve system performance by taking physician characteristics into account when determining the set of physicians that should be scheduled during the same shifts.

Who Is an Efficient and Effective Physician?: Evidence From Emergence Medicine