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Resource planning for ambulance services in mass casualty incidents: a DES-based policy model

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  • Marion Rauner
  • Michaela Schaffhauser-Linzatti
  • Helmut Niessner

Abstract

Due to an increasing number of mass casualty incidents, which are generally complex and unique in nature, we suggest that decision makers consider operations research-based policy models to help prepare emergency staff for improved planning and scheduling at the emergency site. We thus develop a discrete-event simulation policy model, which is currently being applied by disaster-responsive ambulance services in Austria. By evaluating realistic scenarios, our policy model is shown to enhance the scheduling and outcomes at operative and online levels. The proposed scenarios range from small, simple, and urban to rather large, complex, remote mass casualty emergencies. Furthermore, the organization of an advanced medical post can be improved on a strategic level to increase rescue quality, including enhanced survival of injured victims. In particular, we consider a realistic mass casualty incident at a brewery relative to other exemplary disasters. Based on a variety of such situations, we derive general policy implications at both the macro (e.g., strategic rescue policy) and micro (e.g., operative and online scheduling strategies at the emergency site) levels. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Marion Rauner & Michaela Schaffhauser-Linzatti & Helmut Niessner, 2012. "Resource planning for ambulance services in mass casualty incidents: a DES-based policy model," Health Care Management Science, Springer, vol. 15(3), pages 254-269, September.
    • Handle: RePEc:kap:hcarem:v:15:y:2012:i:3:p:254-269
    DOI: 10.1007/s10729-012-9198-7
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    2. 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.
    3. Hanif Malekpoor & Konstantinos Chalvatzis & Nishikant Mishra & Amar Ramudhin, 2019. "A hybrid approach of VIKOR and bi-objective integer linear programming for electrification planning in a disaster relief camp," Annals of Operations Research, Springer, vol. 283(1), pages 443-469, December.
    4. Cevin Zhang, 2023. "A Literature Study of Medical Simulations for Non-Technical Skills Training in Emergency Medicine: Twenty Years of Progress, an Integrated Research Framework, and Future Research Avenues," IJERPH, MDPI, vol. 20(5), pages 1-32, March.
    5. Argelio Santos & James Gurling & Marcel F Dvorak & Vanessa K Noonan & Michael G Fehlings & Anthony S Burns & Rachel Lewis & Lesley Soril & Nader Fallah & John T Street & Lise Bélanger & Andrea Townson, 2013. "Modeling the Patient Journey from Injury to Community Reintegration for Persons with Acute Traumatic Spinal Cord Injury in a Canadian Centre," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-10, August.
    6. Marion S. Rauner & Helmut Niessner & Ulrike Leopold-Wildburger & Natasa Peric & Teresa Herdlicka, 2016. "A policy management game for mass casualty incidents: an experimental study," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 336-365, June.

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