Abstract

Objective To 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 Methods Two 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.

Results The 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.

Conclusion Assuming a full waiting list, optimizing an individual surgeon’s elective operating room time using an ML-assisted predict-then-optimize scheduling system improves overall operating room efficiency, significantly decreasing overtime.