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Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida

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  • Acuna, Jorge A.
  • Zayas-Castro, José L.
  • Charkhgard, Hadi

Abstract

In the last decade, emergency department (ED) overcrowding has become a national crisis for the US healthcare system. Increasing mortality rates, decreasing quality of care, financial losses due to walkouts, and ambulance diversion are some of the consequences of ED overcrowding. Given the increasing demand in terms of ambulance utilization, being able to assign service requests to EDs efficiently, becomes a key function of the emergency medical services. This paper presents new ambulance allocation optimization models to reduce patients’ total time to treatment, waiting times; therefore, ED overcrowding. Disparities and fairness are considered in the development of the mixed integer programming models. Under a set of assumptions, we apply our strategies to allocate 75 ambulance emergencies to 11 EDs in a specific county in Florida. Heterogeneous types of patients, demand characteristics, and geographical/facility information are considered in the models. Based on numerical experiments and the situation in Florida, we show that the optimization techniques can be utilized for large problems and result in up to 31% improvement of the current decentralized model. Further analysis reveals the negative or positive impact that the strategies have on each patient, giving new insights for future policy modifications. Bi-objective, single objective, and game theory optimization models are implemented in this study.

Suggested Citation

  • Acuna, Jorge A. & Zayas-Castro, José L. & Charkhgard, Hadi, 2020. "Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    • Handle: RePEc:eee:soceps:v:71:y:2020:i:c:s0038012118304191
    DOI: 10.1016/j.seps.2019.100747
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    Cited by:

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    4. Miguel Angel Ortíz-Barrios & Dayana Milena Coba-Blanco & Juan-José Alfaro-Saíz & Daniela Stand-González, 2021. "Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review," IJERPH, MDPI, vol. 18(16), pages 1-31, August.
    5. Vahid Mahmoodian & Iman Dayarian & Payman Ghasemi Saghand & Yu Zhang & Hadi Charkhgard, 2022. "A Criterion Space Branch-and-Cut Algorithm for Mixed Integer Bilinear Maximum Multiplicative Programs," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1453-1470, May.
    6. Akoluk, Damla & Karsu, Özlem, 2022. "Ensuring multidimensional equality in public service," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    7. Wanxin Hou & Shaowen Qin & Campbell Henry Thompson, 2022. "Effective Response to Hospital Congestion Scenarios: Simulation-Based Evaluation of Decongestion Interventions," IJERPH, MDPI, vol. 19(23), pages 1-11, December.
    8. 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).
    9. Jing Liu & Ruilin Ouyang & Chun-An Chou & Jacqueline Griffin, 2023. "An Analytical Approach for Dispatch Operations of Emergency Medical Services: A Case Study of COVID-19," SN Operations Research Forum, Springer, vol. 4(2), pages 1-36, June.
    10. Chen, Sihua & Qiu, Han & Wen, Xiang & Wang, Bolin & He, Wei & Shao, Xiuyan, 2024. "Does information disclosure alleviate overcrowding? An empirical study based on large-scale COVID-19 nucleic acid test," Journal of Business Research, Elsevier, vol. 171(C).
    11. Acuna, Jorge A. & Cantarino, Daniela & Martinez, Rodrigo & Zayas-Castro, José L., 2024. "A two-stage stochastic game model for elective surgical capacity planning and investment," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).

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