Smart scheduling of arthroplasty surgery with machine learning and optimisation improves operating room utilisation

OBJECTIVES: Total hip and knee arthroplasty (THA and TKA, respectively) are effective procedures but are costly and resource intensive. As such, scheduling can have a significant impact on hospitals. The aim of this study was to evaluate the efficiency of a surgical schedule generated using machine learning (ML) and mathematical optimisation to current gold-standard scheduling practices. METHODS: All primary and revision TKA and THA cases between April 2012 and February 2022 from a single institution were included (n=15 267). Data was split by year into training/validation and test sets. Procedure-specific models were trained for predicting operative time. Integer linear programming optimisation of operating room (OR) utilisation using these ML predictions was compared with historic scheduling (using surgeon-surgery-specific rolling average values). Weekly simulated schedules were generated and compared based on OR underutilisation, overtime and cases completed. RESULTS: The neural network models performed the best for all four procedures (median MSE: 594.6). This was a 7.1% improvement in 15 min buffer accuracy compared with rolling average times. The ML-predicted and optimised schedule reduced OR underutilisation (p