Decision Making for Emergency Medical Services

Hari K. Rajagopalan (), Cem Saydam (), Hubert Setzler () and Elisabeth Sharer ()
Additional contact information
Hari K. Rajagopalan: Francis Marion University
Cem Saydam: The University of North Carolina at Charlotte
Hubert Setzler: Francis Marion University
Elisabeth Sharer: Francis Marion University

Chapter Chapter 11 in Community-Based Operations Research, 2012, pp 275-296 from Springer

Abstract: Abstract When a medical emergency is recognized, citizens of communities across the USA call 9-1-1. As taxpayers, citizens assume that their local Emergency Medical Services (EMS) agency is always ready to respond quickly and provide necessary treatments to save lives, alleviate pain and suffering, or otherwise address citizens’ concerns. Designing and operating EMS emergency response systems is a major area of research which directly affects communities. One of the primary goals of this research is to provide EMS administrators with viable ambulance location models for improving response times. The first step toward improving response times is to improve forecasting methods. Accurate forecasting provides EMS administrators with pertinent information about when and where calls for ambulances are likely to originate. Given this information EMS administrators can then attempt to locate their ambulances to maximize coverage over a given geographical area. However, calls for ambulances vary both temporally and spatially, forcing EMS administrators to frequently relocate their ambulances as demand patterns change. Forecasting the changes in these demand patterns, as well as determining the different locations ambulances need to be placed at different times of the day, is an important aspect in the successful day-to-day operations for any EMS administrator. However, frequent relocations cause fatigue and loss of morale among ambulance personnel. Our research enables EMS administrators to address these concerns while delivering highest-quality service with limited resources. This chapter briefly reviews existing research in this area and synthesizes findings from three recent papers written by the authors to provide an overview of current approaches to decision modeling for EMS provision.

Keywords: Artificial Neural Network; Emergency Medical Service; Time Block; Demand Node; Coverage Requirement (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/978-1-4614-0806-2_11

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