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Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model

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  • Dean, Matthew D.
  • Nair, Suresh K.

Abstract

During a mass casualty incident (MCI), to which one of several area hospitals should each victim be sent? These decisions depend on resource availability (both transport and care) and the survival probabilities of patients. This paper focuses on the critical time period immediately following the onset of an MCI and is concerned with how to effectively evacuate victims to the different area hospitals in order to provide the greatest good to the greatest number of patients while not overwhelming any single hospital. This resource-constrained triage problem is formulated as a mixed-integer program, which we call the Severity-Adjusted Victim Evacuation (SAVE) model. It is compared with a model in the extant literature and also against several current policies commonly used by the so-called incident commander. The experiments indicate that the SAVE model provides a marked improvement over the commonly used ad-hoc policies and an existing model. Two possible implementation strategies are discussed along with managerial conclusions.

Suggested Citation

  • Dean, Matthew D. & Nair, Suresh K., 2014. "Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model," European Journal of Operational Research, Elsevier, vol. 238(1), pages 363-373.
    • Handle: RePEc:eee:ejores:v:238:y:2014:i:1:p:363-373
    DOI: 10.1016/j.ejor.2014.03.028
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    10. Alan Kirschenbaum, 2021. "Reducing patient surge: community based social networks as first responders," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 163-175, August.
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    15. Alex F. Mills & Jonathan E. Helm & Yu Wang, 2021. "Surge Capacity Deployment in Hospitals: Effectiveness of Response and Mitigation Strategies," Manufacturing & Service Operations Management, INFORMS, vol. 23(2), pages 367-387, March.
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    17. Repoussis, Panagiotis P. & Paraskevopoulos, Dimitris C. & Vazacopoulos, Alkiviadis & Hupert, Nathaniel, 2016. "Optimizing emergency preparedness and resource utilization in mass-casualty incidents," European Journal of Operational Research, Elsevier, vol. 255(2), pages 531-544.
    18. Glasgow, Simon M. & Perkins, Zane B. & Tai, Nigel R.M. & Brohi, Karim & Vasilakis, Christos, 2018. "Development of a discrete event simulation model for evaluating strategies of red blood cell provision following mass casualty events," European Journal of Operational Research, Elsevier, vol. 270(1), pages 362-374.
    19. Mills, Alex F., 2016. "A simple yet effective decision support policy for mass-casualty triage," European Journal of Operational Research, Elsevier, vol. 253(3), pages 734-745.

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