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Simulation of single start station for Edmonton EMS

Author

Listed:
  • A Ingolfsson

    (University of Alberta)

  • E Erkut

    (University of Alberta)

  • S Budge

    (University of Alberta)

Abstract

The City of Edmonton's Emergency Medical Services (EMS) department proposed to move to a ‘single start station system’ (SS system) in which all ambulances would begin and end their shifts at the same location. We developed a discrete event simulation model to estimate the impact of this change and subsequently used this model to explore other changes to Edmonton EMS operations, including the addition of stations, the addition of ambulances, different shifts, and a different redeployment system. We found that a SS system increased average unit availability and the fraction of calls reached within the department's response time standard, particularly during the current shift changeover periods. The paper describes the development and validation of the simulation model and summarizes the results of its application.

Suggested Citation

  • A Ingolfsson & E Erkut & S Budge, 2003. "Simulation of single start station for Edmonton EMS," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(7), pages 736-746, July.
    • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:7:d:10.1057_palgrave.jors.2601574
    DOI: 10.1057/palgrave.jors.2601574
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    References listed on IDEAS

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    Cited by:

    1. F. Javier Otamendi & David García-Heredia, 2015. "Isochrones as Indicators of the Influence of Traffic in Public Health: A Visual Simulation Application in Ávila, Spain," IJERPH, MDPI, vol. 12(10), pages 1-21, October.
    2. Nabil Channouf & Pierre L’Ecuyer & Armann Ingolfsson & Athanassios Avramidis, 2007. "The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta," Health Care Management Science, Springer, vol. 10(1), pages 25-45, February.
    3. Abo-Hamad, Waleed & Arisha, Amr, 2013. "Simulation-based framework to improve patient experience in an emergency department," European Journal of Operational Research, Elsevier, vol. 224(1), pages 154-166.
    4. Matthew S. Maxwell & Mateo Restrepo & Shane G. Henderson & Huseyin Topaloglu, 2010. "Approximate Dynamic Programming for Ambulance Redeployment," INFORMS Journal on Computing, INFORMS, vol. 22(2), pages 266-281, May.
    5. Roberto Aringhieri & Giuliana Carello & Daniela Morale, 2016. "Supporting decision making to improve the performance of an Italian Emergency Medical Service," Annals of Operations Research, Springer, vol. 236(1), pages 131-148, January.
    6. Ridler, Samuel & Mason, Andrew J. & Raith, Andrea, 2022. "A simulation and optimisation package for emergency medical services," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1101-1113.
    7. Karatas, Mumtaz & Yakıcı, Ertan, 2019. "An analysis of p-median location problem: Effects of backup service level and demand assignment policy," European Journal of Operational Research, Elsevier, vol. 272(1), pages 207-218.
    8. Rania Boujemaa & Aida Jebali & Sondes Hammami & Angel Ruiz & Hanen Bouchriha, 2018. "A stochastic approach for designing two-tiered emergency medical service systems," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 123-152, June.
    9. Susan Budge & Armann Ingolfsson & Dawit Zerom, 2010. "Empirical Analysis of Ambulance Travel Times: The Case of Calgary Emergency Medical Services," Management Science, INFORMS, vol. 56(4), pages 716-723, April.
    10. N C Simpson & P G Hancock, 2009. "Fifty years of operational research and emergency response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 126-139, May.
    11. Roberto Aringhieri & Giuliana Carello & Daniela Morale, 2016. "Supporting decision making to improve the performance of an Italian Emergency Medical Service," Annals of Operations Research, Springer, vol. 236(1), pages 131-148, January.

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