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Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability model

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  • Amber Kunkel
  • Laura McLay

Abstract

Emergency medical services (EMS) provide life-saving care and hospital transport to patients with severe trauma or medical conditions. Severe weather events, such as snow events, may lead to adverse patient outcomes by increasing call volumes and service times. Adequate staffing levels during such weather events are critical for ensuring that patients receive timely care. To determine staffing levels that depend on weather, we propose a model that uses a discrete event simulation of a reliability model to identify minimum staffing levels that provide timely patient care, with regression used to provide the input parameters. The system is said to be reliable if there is a high degree of confidence that ambulances can immediately respond to a given proportion of patients (e.g., 99 %). Four weather scenarios capture varying levels of snow falling and snow on the ground. An innovative feature of our approach is that we evaluate the mitigating effects of different extrinsic response policies and intrinsic system adaptation. The models use data from Hanover County, Virginia to quantify how snow reduces EMS system reliability and necessitates increasing staffing levels. The model and its analysis can assist in EMS preparedness by providing a methodology to adjust staffing levels during weather events. A key observation is that when it is snowing, intrinsic system adaptation has similar effects on system reliability as one additional ambulance. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Amber Kunkel & Laura McLay, 2013. "Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability model," Health Care Management Science, Springer, vol. 16(1), pages 14-26, March.
    • Handle: RePEc:kap:hcarem:v:16:y:2013:i:1:p:14-26
    DOI: 10.1007/s10729-012-9206-y
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    References listed on IDEAS

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    1. Ernst, A. T. & Jiang, H. & Krishnamoorthy, M. & Sier, D., 2004. "Staff scheduling and rostering: A review of applications, methods and models," European Journal of Operational Research, Elsevier, vol. 153(1), pages 3-27, February.
    2. Mateo Restrepo & Shane Henderson & Huseyin Topaloglu, 2009. "Erlang loss models for the static deployment of ambulances," Health Care Management Science, Springer, vol. 12(1), pages 67-79, March.
    3. Diwas S. Kc & Christian Terwiesch, 2009. "Impact of Workload on Service Time and Patient Safety: An Econometric Analysis of Hospital Operations," Management Science, INFORMS, vol. 55(9), pages 1486-1498, September.
    4. McLay, Laura A. & Boone, Edward L. & Brooks, J. Paul, 2012. "Analyzing the volume and nature of emergency medical calls during severe weather events using regression methodologies," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 55-66.
    5. 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.
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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.

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