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Related Experiment Video

Updated: Jul 9, 2026

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Optimizing Daily Surgical Scheduling Improves Operative Time Consumption: A Retrospective Study.

Aazad Abbas1,2, Imran Saleh3,4, Paul Wong1,5

  • 1Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.

Arthroplasty Today
|July 2, 2026
PubMed
Summary

Bin-packing optimization (BPO) significantly improves operating room (OR) efficiency for arthroplasty by reducing required OR days by a median of 10%. This scheduling optimization enhances throughput and resource utilization in orthopaedic surgery.

Keywords:
Bin packingOptimizationOvertimeSchedulingThroughput

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Area of Science:

  • Orthopaedic Surgery
  • Health Systems Engineering
  • Operations Research

Background:

  • Arthroplasty procedures are high-volume and resource-intensive in orthopaedics.
  • Current cost containment focuses on limiting operating room (OR) time, not optimizing schedules.
  • Bin-packing optimization (BPO) offers a method for improving OR scheduling efficiency.

Purpose of the Study:

  • To compare the efficiency of manual scheduling versus BPO for orthopaedic surgeries.
  • To test the hypothesis that BPO scheduling leads to more efficient OR utilization.

Main Methods:

  • Retrospective data collection from a mid-sized hospital performing elective orthopaedic surgeries (hip and knee arthroplasty).
  • Comparison of historic manual OR schedules with simulated BPO schedules.
  • Primary outcome measured: total number of OR days required to complete a defined surgical volume.

Main Results:

  • BPO schedules reduced the required OR days by a median of 10% (183 days vs. 207 days).
  • OR configurations changed in 87% of cases with BPO, and the number of configurations per surgeon decreased.
  • No increase in operative time was observed with BPO scheduling.

Conclusions:

  • BPO enhances OR throughput by 10% for arthroplasty procedures.
  • Potential benefits include cost reduction, shorter patient wait times, and improved surgeon satisfaction.
  • Future research should integrate predictive modeling for personalized scheduling.