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Optimal dispatching strategies for emergency vehicles to increase patient survivability

Author

Listed:
  • Damitha Bandara
  • Maria E. Mayorga
  • Laura A. McLay

Abstract

A major focus of emergency medical service (EMS) systems is to save lives and to minimise the effect of an emergency health incident. The objective of this research is to determine how to optimally dispatch paramedic units to emergency calls to maximise patients' survivability. We formulate the problem as Markov decision process to obtain the optimal dispatching policies. These dispatching policies are developed incorporating the degree of the urgency of the call. The optimal policy provides an ordered preference (priority) list of ambulances to dispatch. The performance of the proposed dispatching rules is evaluated in terms of patients' survivability rather than measuring the response time thresholds, as survival probability more directly mirrors patient outcomes. Computational examples show that dispatching the closest vehicle is not always optimal and that dispatching vehicles considering the priority of the call leads to an increase in the average survival probability of patients.

Suggested Citation

  • Damitha Bandara & Maria E. Mayorga & Laura A. McLay, 2012. "Optimal dispatching strategies for emergency vehicles to increase patient survivability," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 15(2), pages 195-214.
    • Handle: RePEc:ids:ijores:v:15:y:2012:i:2:p:195-214
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    Citations

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    Cited by:

    1. Lakmali Weerasena & Aniekan Ebiefung & Anthony Skjellum, 2022. "Design of a heuristic algorithm for the generalized multi-objective set covering problem," Computational Optimization and Applications, Springer, vol. 82(3), pages 717-751, July.
    2. 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.
    3. Amir Ardestani-Jaafari & Beste Kucukyazici, 2022. "Improving Patient Transfer Protocols for Regional Stroke Networks," Management Science, INFORMS, vol. 68(9), pages 6610-6633, September.
    4. 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.
    5. 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.
    6. 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.
    7. Bertsimas, Dimitris & Ng, Yeesian, 2019. "Robust and stochastic formulations for ambulance deployment and dispatch," European Journal of Operational Research, Elsevier, vol. 279(2), pages 557-571.
    8. C. J. Jagtenberg & S. Bhulai & R. D. Mei, 2017. "Dynamic ambulance dispatching: is the closest-idle policy always optimal?," Health Care Management Science, Springer, vol. 20(4), pages 517-531, December.
    9. 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.
    10. McCormack, Richard & Coates, Graham, 2015. "A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival," European Journal of Operational Research, Elsevier, vol. 247(1), pages 294-309.
    11. Li, Mengyu & Carter, Alix & Goldstein, Judah & Hawco, Terence & Jensen, Jan & Vanberkel, Peter, 2021. "Determining ambulance destinations when facing offload delays using a Markov decision process," Omega, Elsevier, vol. 101(C).

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