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Resource-Based Patient Prioritization in Mass-Casualty Incidents

Author

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  • Alex F. Mills

    (Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

  • Nilay Tanık Argon

    (Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

  • Serhan Ziya

    (Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

Abstract

The most widely used standard for mass-casualty triage, START, relies on a fixed-priority ordering among different classes of patients, and does not explicitly consider resource limitations or the changes in survival probabilities with respect to time. We construct a fluid model of patient triage in a mass-casualty incident that incorporates these factors and characterize its optimal policy. We use this characterization to obtain useful insights about the type of simple policies that have a good chance to perform well in practice, and we demonstrate how one could develop such a policy. Using a realistic simulation model and data from emergency medicine literature, we show that the policy we developed based on our fluid formulation outperforms START in all scenarios considered, sometimes substantially.

Suggested Citation

  • Alex F. Mills & Nilay Tanık Argon & Serhan Ziya, 2013. "Resource-Based Patient Prioritization in Mass-Casualty Incidents," Manufacturing & Service Operations Management, INFORMS, vol. 15(3), pages 361-377, July.
  • Handle: RePEc:inm:ormsom:v:15:y:2013:i:3:p:361-377
    DOI: 10.1287/msom.1120.0426
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    References listed on IDEAS

    as
    1. Evin Uzun Jacobson & Nilay Tanık Argon & Serhan Ziya, 2012. "Priority Assignment in Emergency Response," Operations Research, INFORMS, vol. 60(4), pages 813-832, August.
    2. Armann Ingolfsson & Susan Budge & Erhan Erkut, 2008. "Optimal ambulance location with random delays and travel times," Health Care Management Science, Springer, vol. 11(3), pages 262-274, September.
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    Cited by:

    1. Rezapour, Shabnam & Naderi, Nazanin & Morshedlou, Nazanin & Rezapourbehnagh, Shaghayegh, 2018. "Optimal deployment of emergency resources in sudden onset disasters," International Journal of Production Economics, Elsevier, vol. 204(C), pages 365-382.
    2. Hyun-Rok Lee & Taesik Lee, 2018. "Markov decision process model for patient admission decision at an emergency department under a surge demand," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 98-122, June.
    3. Kamali, Behrooz & Bish, Douglas & Glick, Roger, 2017. "Optimal service order for mass-casualty incident response," European Journal of Operational Research, Elsevier, vol. 261(1), pages 355-367.
    4. Noa Zychlinski, 2023. "Applications of fluid models in service operations management," Queueing Systems: Theory and Applications, Springer, vol. 103(1), pages 161-185, February.
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    6. Farahani, Reza Zanjirani & Lotfi, M.M. & Baghaian, Atefe & Ruiz, Rubén & Rezapour, Shabnam, 2020. "Mass casualty management in disaster scene: A systematic review of OR&MS research in humanitarian operations," European Journal of Operational Research, Elsevier, vol. 287(3), pages 787-819.
    7. Sun, Huiping & Li, Yuchen & Zhang, Jianghua, 2022. "Collaboration-based reliable optimal casualty evacuation network design for large-scale emergency preparedness," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    8. Zhankun Sun & Nilay Tan?k Argon & Serhan Ziya, 2018. "Patient Triage and Prioritization Under Austere Conditions," Management Science, INFORMS, vol. 64(10), pages 4471-4489, October.
    9. Lee, Hyun-Rok & Lee, Taesik, 2021. "Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response," European Journal of Operational Research, Elsevier, vol. 291(1), pages 296-308.
    10. Elmira Farrokhizadeh & Seyed Amin Seyfi-Shishavan & Sule Itir Satoglu, 2022. "Blood supply planning during natural disasters under uncertainty: a novel bi-objective model and an application for red crescent," Annals of Operations Research, Springer, vol. 319(1), pages 73-113, December.
    11. 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.
    12. Liu, Yang & Cui, Na & Zhang, Jianghua, 2019. "Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 1-16.
    13. R. K. Jha & B. S. Sahay & P. Charan, 2016. "Healthcare operations management: a structured literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 43(3), pages 259-279, September.
    14. Tinglong Dai & Sridhar Tayur, 2020. "OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 869-887, September.
    15. Atefe Baghaian & M. M. Lotfi & Shabnam Rezapour, 2022. "Integrated deployment of local urban relief teams in the first hours after mass casualty incidents," Operational Research, Springer, vol. 22(4), pages 4517-4555, September.
    16. Shuwan Zhu & Wenjuan Fan & Xueping Li & Shanlin Yang, 2023. "Ambulance dispatching and operating room scheduling considering reusable resources in mass-casualty incidents," Operational Research, Springer, vol. 23(2), pages 1-37, June.
    17. Mor Armony & Efrat Perel & Nir Perel & Uri Yechiali, 2019. "Exact analysis for multiserver queueing systems with cross selling," Annals of Operations Research, Springer, vol. 274(1), pages 75-100, March.
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    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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