The objective of this research project is to use Operations Research tools, in particular stochastic dynamic programming, to develop a better understanding of patient triage and prioritization decisions in the aftermath of mass casualty incidents. Such incidents (e.g., a major accident, a terrorist bombing, or a natural disaster) may cause a significant number of injuries creating a sudden jump in demand for medical resources such as operating rooms, ambulances, X-ray machines, etc. As a result, these medical resources become seriously overwhelmed, and thus their effective use becomes extremely important for the success of the entire emergency response effort. One basic question is how these resources should be allocated to the patients in need of treatment so as to do the greatest good for the greatest number. In this research, we will build mathematical models to identify key characteristics of effective patient triage and prioritization decisions that will help develop better policies that can be used on the field. We will particularly focus on giving informed prioritization decisions that take into account various factors such as the number of patients in different classes with different health conditions and treatment requirements.

The project will consist of three main stages. In the first stage, we will study the most commonly applied triage procedure that classifies patients only once during the response effort. Using a dynamic programming formulation, an analytical characterization of the critical patient will be obtained and near-optimal prioritization policies that consider the patient mix will be developed. The second stage of the project will be on dynamic triage procedures under which patients are reevaluated periodically throughout the response effort. The potential benefits of dynamic triage will be investigated and good policies for its implementation will be proposed. Finally, in the third stage, an extensive simulation study will be carried out to test the basic principles and policies that are proposed in the first two stages of the project, in more realistic settings.

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Wake Forest University School of Medicine
United States
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