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Priority dispatching strategies for EMS systems

Author

Listed:
  • Damitha Bandara

    (Clemson University, Clemson, SC, USA)

  • Maria E Mayorga

    (North Carolina State University, Raleigh, NC, USA)

  • Laura A McLay

    (University of Wisconsin-Madison, Madison, WI, USA)

Abstract

Emergency medical service (EMS) systems provide urgent medical care and transport. In this study we implement dispatching policies for EMS systems that incorporate the severity of the call in order to increase the survival probability of patients. A simulation model is developed to evaluate the performance of EMS systems. Performance is measured in terms of patients’ survival probability, since survival probability more directly mirrors patient outcomes. Different response strategies are evaluated utilizing several examples to study the nature of the optimal dispatching policy. The results show that dispatching the closest vehicle is not always optimal and dispatching vehicles considering priority of the call leads to an increase in the average survival probability of patients. A heuristic algorithm, that is easy to implement, is developed to dispatch ambulances for large-scale EMS systems. Computational examples show that the dispatching algorithm is valuable in increasing the patients’ survival probability.

Suggested Citation

  • Damitha Bandara & Maria E Mayorga & Laura A McLay, 2014. "Priority dispatching strategies for EMS systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(4), pages 572-587, April.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:4:p:572-587
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    Citations

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    Cited by:

    1. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    2. Phillip R. Jenkins & Matthew J. Robbins & Brian J. Lunday, 2021. "Approximate Dynamic Programming for Military Medical Evacuation Dispatching Policies," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 2-26, January.
    3. Akbari, Leilanaz & Kazemi, Ahmad & Salari, Majid, 2023. "Operational planning of vehicles for rescue and relief operations considering the unavailability of the relocated vehicles," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    4. Bélanger, V. & Lanzarone, E. & Nicoletta, V. & Ruiz, A. & Soriano, P., 2020. "A recursive simulation-optimization framework for the ambulance location and dispatching problem," European Journal of Operational Research, Elsevier, vol. 286(2), pages 713-725.
    5. Ibrahim Çapar & Sharif H Melouk & Burcu B Keskin, 2017. "Alternative metrics to measure EMS system performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 792-808, July.
    6. Wang, Wei & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2022. "EMS location-allocation problem under uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    7. Rettke, Aaron J. & Robbins, Matthew J. & Lunday, Brian J., 2016. "Approximate dynamic programming for the dispatch of military medical evacuation assets," European Journal of Operational Research, Elsevier, vol. 254(3), pages 824-839.
    8. Phillip R. Jenkins & Matthew J. Robbins & Brian J. Lunday, 2018. "Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system," Annals of Operations Research, Springer, vol. 271(2), pages 641-678, December.

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