Predicting physician burnout using clinical activity logs: Model performance and lessons learned.

BACKGROUND:Burnout is a significant public health concern affecting more than half of the healthcare workforce; however, passive screening tools to detect burnout are lacking. We investigated the ability of machine learning (ML) techniques to identify burnout using passively collected electronic health record (EHR)-based audit log data. METHOD:Physician trainees participated in a longitudinal study where they completed monthly burnout surveys and provided access to their EHR-based audit logs. Using the monthly burnout scores as the target outcome, we trained ML models using combinations of features derived from audit log data-aggregate measures of clinical workload, time series-based temporal measures of EHR use, and the baseline burnout score. Five ML models were constructed to predict burnout as a continuous score: penalized linear regression, support vector machine, neural network, random forest, and gradient boosting machine. RESULTS:88 trainee physicians participated and completed 416 surveys; greater than10 million audit log actions were collected (Mean [Standard Deviation] = 25,691 [14,331] actions per month, per physician). The workload feature set predicted burnout score with a mean absolute error (MAE) of 0.602 (95% Confidence Interval (CI), 0.412-0.826), and was able to predict burnout status with an average AUROC of 0.595 (95% CI 0.355-0.808) and average accuracy 0.567 (95% CI 0.393-0.742). The temporal feature set had a similar performance, with MAE 0.596 (95% CI 0.391-0.826), and AUROC 0.581 (95% CI 0.343-0.790). The addition of the baseline burnout score to the workload features improved the model performance to a mean AUROC of 0.829 (95% CI 0.607-0.996) and mean accuracy of 0.781 (95% CI 0.587-0.936); however, this performance was not meaningfully different than using the baseline burnout score alone. CONCLUSIONS:Current findings illustrate the complexities of predicting burnout exclusively based on clinical work activities as captured in the EHR, highlighting its multi-factorial and individualized nature. Future prediction studies of burnout should account for individual factors (e.g., resilience, physiological measurements such as sleep) and associated system-level factors (e.g., leadership).