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Using data from cardiovascular surgery patients with long and highly variablepost-surgical lengths of stay (LOS), we develop a modeling framework to reducerecovery unit congestion. We estimate the LOS and its probability distributionusing machine learning models, schedule procedures on a rolling basis using avariety of optimization models, and estimate performance with simulation. Themachine learning models achieved only modest LOS prediction accuracy, despiteaccess to a very rich set of patient characteristics. Compared to the currentpaper-based system used in the hospital, most optimization models failed toreduce congestion without increasing wait times for surgery. A conservativestochastic optimization with sufficient sampling to capture the long tail ofthe LOS distribution outperformed the current manual process and otherstochastic and robust optimization approaches. These results highlight theperils of using oversimplified distributional models of LOS for schedulingprocedures and the importance of using optimization methods well-suited todealing with long-tailed behavior.

Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data