Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration

Background Accurate estimation of operative case-time duration is critical for optimizing operating room utilization. Current estimates are inaccurate and prior models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective dataset to improve estimation of case-time duration relative to current standards. Study Design We developed models to predict case-time duration using linear regression and supervised machine learning (ML). For each of these models, we generated: an all-inclusive model, service-specific models, and surgeon-specific models. In the latter two approaches, individual models were created for each surgical service and surgeon, respectively. Our dataset included 46,986 scheduled surgeries performed at a large academic medical center from January 2014 to December 2017, with 80% used for training and 20% for model testing/validation. Predictions derived from each model were compared to our institutional standard of using average historical procedure times and surgeon estimates. Models were evaluated based on accuracy, overage (case duration > predicted + 10%), underage (case duration Results The ML algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracies, lower percentage of overage and underage, and higher percentage of cases within the 10% threshold. The ability to predict cases within 10% improved from 32% using our institutional standard to 39% with the ML surgeon-specific model. Conclusion Our study is a notable advancement towards statistical modeling of case-time duration across all surgical departments in a large tertiary medical center. Machine learning approaches may improve case duration estimations, enabling improved OR scheduling, efficiency, and reduced costs.