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Decision support algorithms for optimizing surgery start times considering the performance variation.

Shing Chih Tsai1, Wu Hung Lin1, Chia Cheng Wu2

  • 1Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan.

Health Care Management Science
|October 11, 2021
PubMed
Summary

This study introduces a novel algorithm for optimizing surgical scheduling in a single operating room. The method efficiently determines optimal surgery start times, balancing utility and performance variation.

Keywords:
Multiple attribute utility theoryOR in healthcareOperations researchRanking and selectionSimulation-based optimizationSurgery planned start timeSurgery scheduling under uncertainty

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Area of Science:

  • Operations Research
  • Healthcare Management
  • Applied Mathematics

Background:

  • Surgical scheduling is complex, involving multiple elective surgeries within a single operating room.
  • Traditional optimization methods are insufficient due to the stochastic nature and analytical intractability of the problem.

Purpose of the Study:

  • To develop a decision support algorithm for optimizing elective surgical start times.
  • To maximize surgically related utility while adhering to performance variation thresholds.

Main Methods:

  • A two-phase sequential decision support algorithm was developed.
  • Phase 1: Variance screening to handle stochastic elements.
  • Phase 2: Multiple attribute utility theory for solution selection.

Main Results:

  • The proposed algorithm effectively addresses the analytically intractable optimization problem.
  • Numerical experiments demonstrate the algorithm's ability to find promising solutions efficiently.
  • The approach balances maximizing surgical utility with managing performance variation.

Conclusions:

  • The developed algorithm provides a viable solution for complex surgical scheduling problems.
  • This method offers an efficient way to optimize operating room utilization.
  • The findings support improved decision-making in healthcare operations management.