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Automating Rule-Compliant and Equitable Call Schedules for Orthopedic Surgery Residents With Artificial Intelligence

Prushoth Vivekanantha1, Marc Daniel Bouchard1, Jeffrey Kay1

  • 1Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.

Journal of Medical Education and Curricular Development
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

This study shows that generative pretrained Transformer 5.2 (GPT-5.2) can automate orthopedic surgery resident call schedule generation. The AI model produces complex, rule-compliant, and equitable schedules efficiently and affordably.

Keywords:
administrationartificial intelligenceautomationeducationgraduatehealth personnellarge language modelmedicalscheduling

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Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Health Informatics

Background:

  • Call schedule generation is a time-consuming administrative task for residency programs.
  • Manual methods are often inflexible and require extensive computation.
  • Large language models (LLMs) present an efficient and adaptable alternative.

Purpose of the Study:

  • To assess if generative pretrained Transformer 5.2 (GPT-5.2) combined with a Python rule-checker can automate complex, rule-compliant, and equitable call schedules for orthopedic surgery residents.

Main Methods:

  • Modeled ten month-long residency blocks, including nonbackup and backup months.
  • Utilized GPT-5.2 via API, prompted with 14 institutional scheduling rules.
  • Assessed schedule success rate, fairness (Jain and Gini indices), and efficiency (time, cost).

Main Results:

  • 100% of nonbackup schedules were rule-compliant; 86.7% of backup schedules were successful, with at least two valid schedules per block.
  • High fairness metrics (Jain index ~0.94, Gini index ~0.12).
  • Low computational cost (<$0.25/run) and rapid generation (under 8 minutes/run).

Conclusions:

  • GPT-5.2 effectively automates the generation of complex, rule-compliant, and equitable orthopedic surgery resident call schedules.
  • The process is efficient, completing within minutes at a low computational cost.
  • This AI-driven approach offers a viable solution to a persistent administrative challenge in medical residency programs.