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Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.

Yen-Yi Feng1, I-Chin Wu2, Tzu-Li Chen3

  • 1Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, 10449, Taiwan, Republic of China.

Health Care Management Science
|August 6, 2015
PubMed
Summary
This summary is machine-generated.

Hospital emergency departments face overcrowding due to rising cases. This study optimizes medical resource allocation using a novel simulation algorithm to reduce patient wait times and costs in Taiwan.

Keywords:
Emergency departmentMedical resource allocationMulti-objective computing budget allocationNon-dominated sorting genetic algorithmSimulation optimization

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Area of Science:

  • Operations Research
  • Healthcare Management
  • Applied Mathematics

Background:

  • Annual increases in emergency cases lead to hospital emergency department (ED) overcrowding.
  • Existing solutions for resource enhancement are often impractical in Taiwan.
  • Optimizing resource allocation is crucial for minimizing patient stay and waste.

Purpose of the Study:

  • To develop a multi-objective mathematical model for ED medical resource allocation.
  • To address the complexity and stochastic nature of ED resource allocation problems.
  • To minimize average patient length of stay and medical resource waste costs.

Main Methods:

  • Constructed a multi-objective mathematical model for ED resource allocation.
  • Developed a simulation optimization algorithm integrating Non-dominated Sorting Genetic Algorithm II (NSGA II) and Multi-Objective Computing Budget Allocation (MOCBA).
  • Utilized a discrete event simulation model of ED flow, based on a Taiwanese hospital case.

Main Results:

  • The integrated NSGA II and MOCBA method effectively identified Pareto optimal medical resource allocation solutions.
  • Computational experiments verified the algorithm's effectiveness and performance.
  • Derived non-dominated solutions for optimizing ED resource allocation.

Conclusions:

  • The proposed simulation optimization algorithm provides an effective approach for complex multi-objective ED resource allocation.
  • This method can help Taiwanese hospitals manage limited resources efficiently.
  • Optimized resource allocation leads to reduced patient wait times and operational costs.