Dynamic Distribution of Patients to Medical Facilities in the Aftermath of a Disaster
Dynamic Distribution of Patients to Medical Facilities in the Aftermath of a Disaster
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DOI：
10.1287/opre.2017.1695
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发表时间：
2018-05-01
影响因子：
2.7
通讯作者：
Ziya, Serhan
中科院分区：
文献类型：
--
作者：
Mills, Alex F.;Argon, Nilay Tanik;Ziya, Serhan
灾难发生后，急救人员必须利用有限的运输资源(如救护车)将大量病人运送到医疗机构。关于将病人送往哪里的决定通常是由现场的响应人员以临时方式做出的。使用马尔可夫决策过程公式，我们开发了两种启发式策略，这两种策略使用有限的信息，如平均旅行时间和拥堵程度来确定(A)如何将救护车分配到患者所在的位置，以及(B)这些救护车应该到达哪个医疗机构。在一项模拟研究中，我们纳入了不同类型创伤的患者存活率和服务时间，并表明，与通常的将患者转移到最近设施的做法相比，所提出的启发式方法可以在预期幸存者数量方面提供实质性改善，即使决策者只限制了关于系统状态的最新信息。特别是，短视的方法只考虑下一个病人被运送最好的方式，在几乎所有考虑的情况下都会增加预期的幸存者数量。当事件涉及病情没有迅速恶化的患者时，尤其是当交通不是瓶颈且伤亡分散在多个地点时，使用更复杂的一步政策改进方法可进一步改善情况。我们在一个假想地震的案例研究中展示了所提出的启发式算法的有效性，在该案例中，伤亡数据是使用美国政府开发的计算机软件生成的。
In the aftermath of a disaster, emergency responders must transport a large number of patients to medical facilities, using limited transportation resources (such as ambulances). Decisions about where to send the patients are typically made in an ad hoc manner by responders on the scene. Using a Markov decision process formulation, we develop two heuristic policies that use limited information such as mean travel times and congestion levels to determine (a) how to allocate ambulances to patient locations and (b) which medical facility should be the destination for those ambulances. In a simulation study, we incorporate patient survival rates and service times for different types of traumatic injuries, and show that the proposed heuristics can provide substantial improvement in the expected number of survivors compared to the common practice of transporting to the nearest facility, even when the decision maker has only limited up-todate information about the system state. In particular, a myopic approach that considers only what is best for the next patient to be transported increases the expected number of survivors in almost all scenarios considered. Using a more sophisticated one-step policy improvement approach provides further improvement when the event involves patients who do not deteriorate rapidly, especially when the transportation is not the bottleneck and the casualties are spread over many locations. We demonstrate the effectiveness of the proposed heuristics on a case study of a hypothetical earthquake, where casualty data is generated using computer software developed by the U.S. government.