Machine Learning to Predict-Then-Optimize Elective Orthopaedic Surgery Scheduling Improves Operating Room Utilization

ObjectiveTo determine the potential for improving elective surgery scheduling for total knee and hip arthroplasty (TKA and THA, respectively) by utilizing a two-stage approach that incorporates machine learning (ML) prediction of the duration of surgery (DOS) with scheduling optimization. Materials and MethodsTwo ML models (for TKA and THA) were trained to predict DOS using patient factors based on 302,490 and 196,942 examples, respectively, from a large international database. Three optimization formulations based on varying surgeon flexibility were compared: Any: surgeons could operate in any operating room at any time, Split: limitation of two surgeons per operating room per day and MSSP: limit of one surgeon per operating room per day. Two years of daily scheduling simulations were performed for each optimization problem using ML-prediction or mean DOS over a range of schedule parameters. Constraints and resources were based on a high-volume arthroplasty hospital in Canada. ResultsThe Any scheduling formulation performed significantly worse than the Split and MSSP formulations with respect to overtime and underutilization (p<0.001). The latter two problems performed similarly (p>0.05) over most schedule parameters. The ML-prediction schedules outperformed those generated using a mean DOS over all schedule parameters, with overtime reduced on average by 300 to 500 minutes per week. Using a 15-minute schedule granularity with a wait list pool of minimum 1 month generated the best schedules. ConclusionAssuming a full waiting list, optimizing an individual surgeons elective operating room time using an ML-assisted predict-then-optimize scheduling system improves overall operating room efficiency, significantly decreasing overtime. Highlights- A new approach for elective surgery scheduling was developed combining machine learning prediction of duration of surgery with integer linear programming optimization. - Models developed on six years of prospective multi-institution data and prediction results were used to generate two years of weekly simulated operating room schedules. - Three optimization models with varying levels of surgeon constraint were compared, and the model that optimized an individual surgeons wait list performed the best. - Using this approach reduced overtime by 300-500 minutes per week across five operating rooms.