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Priority Assignment in Emergency Response

Author

Listed:
  • Evin Uzun Jacobson

    () (Imperial College Business School, London SW7 2AZ, United Kingdom)

  • Nilay Tanık Argon

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

  • Serhan Ziya

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

Abstract

In the aftermath of mass-casualty events, key resources (such as ambulances and operating rooms) can be overwhelmed by the sudden jump in patient demand. To ration these resources, patients are assigned different priority levels, a process that is called triage. According to triage protocols in place, each patient's priority level is determined based on that patient's injuries only. However, recent work from the emergency medicine literature suggests that when determining priorities, resource limitations and the scale of the event should also be taken into account in order to do the greatest good for the greatest number . This article investigates how this can be done and what the potential benefits would be. We formulate the problem as a priority assignment problem in a clearing system with multiple classes of impatient jobs. Jobs are classified based on their lifetime (i.e., their tolerance for wait), service time, and reward distributions. Our objective is to maximize the expected total reward, e.g., the expected total number of survivors. Using sample-path methods and stochastic dynamic programming, we identify conditions under which the state information is not needed for prioritization decisions. In the absence of these conditions, we partially characterize the optimal policy, which is possibly state dependent, and we propose a number of heuristic policies. By means of a numerical study, we demonstrate that simple state-dependent policies that prioritize less urgent jobs when the total number of jobs is large perform well, especially when jobs are time-critical.

Suggested Citation

  • Evin Uzun Jacobson & Nilay Tanık Argon & Serhan Ziya, 2012. "Priority Assignment in Emergency Response," Operations Research, INFORMS, vol. 60(4), pages 813-832, August.
  • Handle: RePEc:inm:oropre:v:60:y:2012:i:4:p:813-832
    DOI: 10.1287/opre.1120.1075
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    File URL: http://dx.doi.org/10.1287/opre.1120.1075
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    References listed on IDEAS

    as
    1. Emmons, Hamilton & Pinedo, Michael, 1990. "Scheduling stochastic jobs with due dates on parallel machines," European Journal of Operational Research, Elsevier, vol. 47(1), pages 49-55, July.
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    Cited by:

    1. Dong Li & Li Ding & Stephen Connor, 2020. "When to Switch? Index Policies for Resource Scheduling in Emergency Response," Production and Operations Management, Production and Operations Management Society, vol. 29(2), pages 241-262, February.
    2. 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.
    3. Galit B. Yom-Tov & Avishai Mandelbaum, 2014. "Erlang-R: A Time-Varying Queue with Reentrant Customers, in Support of Healthcare Staffing," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 283-299, May.
    4. 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.
    5. 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.
    6. Li Zhu & Yeming Gong & Yishui Xu & Jun Gu, 2019. "Emergency relief routing models for injured victims considering equity and priority," Annals of Operations Research, Springer, vol. 283(1), pages 1573-1606, December.
    7. Emmett J. Lodree & Nezih Altay & Robert A. Cook, 2019. "Staff assignment policies for a mass casualty event queuing network," Annals of Operations Research, Springer, vol. 283(1), pages 411-442, December.
    8. Saed Alizamir & Francis de Véricourt & Shouqiang Wang, 2020. "Warning Against Recurring Risks: An Information Design Approach," Management Science, INFORMS, vol. 66(10), pages 4612-4629, October.
    9. 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.
    10. 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.
    11. Zhongzhen Yang & Liquan Guo & Zaili Yang, 2019. "Emergency logistics for wildfire suppression based on forecasted disaster evolution," Annals of Operations Research, Springer, vol. 283(1), pages 917-937, December.
    12. Carlo Drago & Matteo Ruggeri, 2019. "Setting research priorities in the field of emergency management: which piece of information are you willing to pay more?," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 2103-2115, July.
    13. R. K. Jana & Chandra Prakash Chandra & Aviral Kumar Tiwari, 2019. "Humanitarian aid delivery decisions during the early recovery phase of disaster using a discrete choice multi-attribute value method," Annals of Operations Research, Springer, vol. 283(1), pages 1211-1225, December.
    14. 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.
    15. Soroush Saghafian & Wallace J. Hopp & Mark P. Van Oyen & Jeffrey S. Desmond & Steven L. Kronick, 2014. "Complexity-Augmented Triage: A Tool for Improving Patient Safety and Operational Efficiency," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 329-345, July.
    16. Retsef Levi & Thomas Magnanti & Yaron Shaposhnik, 2019. "Scheduling with Testing," Management Science, INFORMS, vol. 65(2), pages 776-793, February.
    17. 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.

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