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Effective Response to Hospital Congestion Scenarios: Simulation-Based Evaluation of Decongestion Interventions

Author

Listed:
  • Wanxin Hou

    (School of Information Science and Technology, Research Centre for Intelligent Information Technology, Nantong University, Nantong 226019, China)

  • Shaowen Qin

    (College of Science and Engineering, Flinders University, Adelaide 5042, Australia)

  • Campbell Henry Thompson

    (School of Medicine, University of Adelaide, Adelaide 5005, Australia)

Abstract

Hospital overcrowding is becoming a major concern in the modern era due to the increasing demand for hospital services. This study seeks to identify effective and efficient ways to resolve the serious problem of congestion in hospitals by testing a range of decongestion strategies with simulated scenarios. In order to determine more efficient solutions, interventions with smaller changes were consistently tested at the beginning through a simulation platform. In addition, the implementation patterns were investigated, which are important to hospital managers with respect to the decisions made to control hospital congestion. The results indicated that diverting a small number of ambulances seems to be more effective and efficient in congestion reduction compared to other approaches. Furthermore, instead of implementing an isolated approach continuously, combining one approach with other strategies is recommended as a method for dealing with hospital overcrowding.

Suggested Citation

  • Wanxin Hou & Shaowen Qin & Campbell Henry Thompson, 2022. "Effective Response to Hospital Congestion Scenarios: Simulation-Based Evaluation of Decongestion Interventions," IJERPH, MDPI, vol. 19(23), pages 1-11, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16348-:d:994998
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    References listed on IDEAS

    as
    1. Kaushal, Arjun & Zhao, Yuancheng & Peng, Qingjin & Strome, Trevor & Weldon, Erin & Zhang, Michael & Chochinov, Alecs, 2015. "Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department," Socio-Economic Planning Sciences, Elsevier, vol. 50(C), pages 18-31.
    2. Acuna, Jorge A. & Zayas-Castro, José L. & Charkhgard, Hadi, 2020. "Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
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