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Optimizing Medical Student Clerkship Schedules Using a Novel Application of the Hungarian Algorithm.

Matthew T MacLean1, Jerzy R Lysikowski2, Robert V Rege3

  • 1M.T. MacLean is currently a preliminary resident in internal medicine, Cone Health, Greensboro, North Carolina. At the time of writing, he was a fourth-year medical student, University of Texas Southwestern Medical Center, Dallas, Texas; ORCID: https://orcid.org/0000-0002-0514-7218 .

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Summary

The Hungarian algorithm optimizes medical student rotation schedules, ensuring more students receive their top choices. This method improves assignment fairness and satisfaction in medical education scheduling.

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Area of Science:

  • Medical Education
  • Operations Research
  • Algorithm Optimization

Background:

  • Medical student clinical rotation scheduling presents challenges in balancing student preferences with fair and efficient assignment.
  • Optimizing these schedules is crucial for maximizing student satisfaction and educational experience.

Purpose of the Study:

  • To introduce and evaluate a novel application of the Hungarian algorithm for optimizing medical student rotation assignments.
  • To compare the Hungarian algorithm's effectiveness against traditional methods like rank and lottery algorithms.

Main Methods:

  • A cost matrix was created based on student-ranked pathway preferences.
  • The Hungarian algorithm was employed to minimize the total cost, thereby optimizing assignments.
  • Simulations and analysis of real-world student preference data (3 years) were used for evaluation.

Main Results:

  • The Hungarian algorithm consistently assigned more students to their top 3 preferred rotations compared to alternative algorithms.
  • Fewer students received none of their preferences when using the Hungarian algorithm.
  • This outcome was validated across computer simulations and actual student data.

Conclusions:

  • The Hungarian algorithm offers a superior method for optimizing medical student rotation schedules, enhancing student satisfaction.
  • This approach is broadly applicable to various scheduling challenges in medical education.
  • The algorithm allows for the incorporation of additional constraints to further refine scheduling.