Collaborative Research: Patient Triage in the Aftermath of a Mass Casualty Event - A Dynamic Programming Approach

协作研究：大规模伤亡事件后的患者分类 - 动态规划方法

• 批准号: 0620418
• 负责人: James Winslow
• 金额: $ 3.75万
• 依托单位: Wake Forest University School of Medicine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0620418&HistoricalAwards=false
• 关键词: Collaborative; Research; Patient; Triage; Aftermath

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.

本研究项目的目标是使用运筹学工具，特别是随机动态规划，以更好地了解大规模伤亡事件发生后的病人分诊和优先级决策。此类事件（例如，重大事故、恐怖主义爆炸或自然灾害）可能导致大量的伤害，从而造成对诸如手术室、救护车、X光机等医疗资源的需求突然增加。结果，这些医疗资源变得严重不堪重负，因此它们的有效使用对于整个紧急响应工作的成功变得极其重要。其中一个基本问题是，这些资源应如何分配给需要治疗的病人，以便为最多的人带来最大的好处。在这项研究中，我们将建立数学模型，以确定有效的患者分诊和优先级决策的关键特征，这将有助于制定更好的政策，可以在现场使用。我们会特别注重在充分掌握资料的情况下，考虑不同类别、不同健康状况和治疗需要的病人人数等因素，决定优先次序。在第一阶段，我们将研究最常用的分类程序，在响应过程中只对患者进行一次分类。使用动态规划制定，分析表征的危重病人将获得和接近最佳的优先级政策，考虑病人的组合将被开发。该项目的第二阶段将是动态分类程序，根据该程序，在整个应对工作中定期对患者进行重新评估。将调查动态分诊的潜在好处，并提出实施的良好政策。最后，在第三阶段，将进行广泛的模拟研究，在更现实的环境中检验项目前两个阶段提出的基本原则和政策。