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Updated: Jan 18, 2026

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Using Machine Learning to Predict-Then-Optimize Elective Orthopedic Surgery Scheduling to Improve Operating Room

Johnathan R Lex1,2,3, Aazad Abbas1,2,3, Jacob Mosseri1,4

  • 1Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, 2075 Bayview Avenue, Suite S620, Toronto, ON, M4N 3M5, Canada.

JMIR Medical Informatics
|September 10, 2025
PubMed
Summary

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Machine learning improves surgical scheduling for total knee and hip arthroplasty (TKA and THA) by predicting surgery duration, significantly reducing overtime and enhancing operating room efficiency for healthcare systems.

Area of Science:

  • Orthopedic Surgery
  • Health Informatics
  • Operations Research

Background:

  • Total knee and hip arthroplasty (TKA and THA) are high-volume elective procedures with increasing demand.
  • Current scheduling relies on average durations, failing to account for patient-specific variability and leading to longer wait times.
  • Rising demand and resource intensity strain healthcare systems, exacerbating wait times despite investments.

Purpose of the Study:

  • To evaluate a machine learning (ML) based two-stage approach for optimizing elective TKA and THA scheduling.
  • To predict individual surgery duration (DOS) using ML and integrate predictions into scheduling optimization.
  • To assess the potential for improving operating room efficiency and reducing wait times.

Main Methods:

Keywords:
elective surgeryhip and knee arthroplastymachine learningoptimizationorthopedic surgeryscheduling

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  • Developed ML models to predict DOS for TKA and THA using large international patient datasets.
  • Compared three scheduling optimization formulations: Any, Split, and Multiple Subset Sum Problem (MSSP).
  • Conducted two-year simulations comparing ML-predicted DOS schedules against mean DOS schedules under various parameters.

    • Main Results:

    • ML models achieved 78.1% (TKA) and 75.4% (THA) accuracy (with a 30-min buffer).
    • ML-predicted schedules significantly reduced weekly overtime by 300-500 minutes compared to mean DOS schedules.
    • While ML schedules increased operating room underutilization (70-192 min), they outperformed mean schedules 97.1% of the time with optimal parameters.

    Conclusions:

    • ML-assisted scheduling optimizes elective operating room time, significantly decreasing overtime and improving efficiency.
    • This predict-then-optimize approach offers substantial benefits for healthcare systems facing cost pressures and long surgical waitlists.
    • Optimized scheduling using ML has the potential to alleviate strains on healthcare resources and improve patient access to TKA and THA procedures.