Collaborative Research: Distribution of Patients to Medical Facilities in Mass-Casualty Events

合作研究：大规模伤亡事件中患者到医疗机构的分配

• 批准号: 1634822
• 负责人: James Winslow
• 金额: $ 5.43万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1634822&HistoricalAwards=false
• 关键词: Collaborative; Research; Distribution; Patients; Medical

Mass-casualty events such as terrorist attacks and natural disasters can affect hundreds to thousands of people and place significant burdens on emergency response systems for unpredicted periods of time. During these events, the emergency response management faces several complex operational decisions under time pressure and sometimes security and safety concerns. One fundamental decision is how to distribute casualties from the affected areas to multiple medical facilities that differ in capacity, specialty, and distance. Currently, this decision is left to the emergency transport officer in civilian settings and to battlefield commanders during military operations. Using mathematical modeling and analysis in conjunction with medical expertise, this project will build knowledge and decision tools to make casualty distribution more efficiently and objective. This multi-disciplinary project bringing together operations researchers and emergency physicians, will benefit society directly by facilitating effective casualty distribution during disasters. It will also significantly contribute to the education of a diverse group of students from the operations research, public health, and medical fields.In its most general form, casualty-distribution problem is a stochastic sequential decision making problem that includes various parameters and variables such as the number of casualties at each location; the number of emergency vehicles; the capacity, capability, and congestion levels of each hospital; the travel time between locations and hospitals; and the condition of travel routes. The first phase of the project involves identifying the most fundamental tradeoffs underlying this complex decision-making problem and formulating separate models for each. These models will then be analyzed by means of exact methods such as sample-path analysis and Markov decision processes to obtain insights about the characteristics of optimal decision rules. In the second phase of the project, approximate approaches such as fluid models and Lagrangian relaxations will be used to develop heuristic policies. In the final phase, an extensive simulation study will be conducted to test the principles and decision rules in more realistic settings using data from literature and the 2010 National Hospital Ambulatory Medical Care Survey. The mathematical models developed for this project can equivalently be seen as queueing models with dynamic routing. Hence, this project also contributes to the operations research literature by introducing and studying a new class of queue-routing problems, where the travel to queues takes time and possibly requires a scarce resource.

恐怖袭击和自然灾害等大规模伤亡事件可能影响数百至数千人，并在不可预测的时间内给应急系统带来重大负担。在这些事件中，应急响应管理部门在时间压力下面临着若干复杂的业务决策，有时还面临安全和安保问题。一个基本的决定是如何将受影响地区的伤员分配到能力、专业和距离不同的多个医疗设施。目前，这一决定在民事环境中留给紧急运输官员，在军事行动中留给战场指挥官。利用数学建模和分析结合医学专业知识，该项目将建立知识和决策工具，使伤亡分配更有效和客观。这个多学科项目汇集了业务研究人员和急诊医生，将通过促进灾害期间有效的伤亡分配直接造福社会。它也将大大有助于教育的学生从运筹学，公共卫生和医学领域的多样化群体。在其最一般的形式，伤亡分布问题是一个随机序列决策问题，其中包括各种参数和变量，如在每个位置的伤亡人数，急救车辆的数量，容量，能力和拥挤程度的每个医院，地点和医院之间的旅行时间;以及旅行路线的状况。该项目的第一阶段涉及确定这一复杂决策问题背后最基本的权衡，并为每个权衡制定单独的模型。然后，这些模型将通过精确的方法，如样本路径分析和马尔可夫决策过程进行分析，以获得最优决策规则的特性的见解。在该项目的第二阶段，将使用流体模型和拉格朗日松弛法等近似方法来制定启发式政策。在最后阶段，将进行广泛的模拟研究，以测试的原则和决策规则，在更现实的设置使用的数据，从文献和2010年全国医院门诊医疗服务调查。 为该项目开发的数学模型可以等效地看作是具有动态路由的路由模型。因此，这个项目也有助于运筹学文献，通过引入和研究一类新的排队路由问题，其中的旅行队列需要时间，并可能需要稀缺的资源。