A Simulation-Based Optimization approach for analyzing the ambulance diversion phenomenon in an Emergency-Department network
A Simulation-Based Optimization approach for analyzing the ambulance diversion phenomenon in an Emergency-Department network
复制标题
一种基于仿真的优化方法，用于分析急诊室网络中的救护车改道现象
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
M. Roma
中科院分区：
文献类型：
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作者：
Christian Piermarini;M. Roma
救护车转院（AD）是缓解全球急诊科（ED）拥挤现象的可能策略之一。当急诊科超负荷时可以实施该策略，它包括将乘坐救护车前来的患者转送至邻近的急诊科。如果实施得当，救护车转院应能减少患者治疗的延误，确保危及生命的患者的安全和救治。从运营角度来看，救护车转院相当于一个网络中急诊科之间的资源共享政策。在本文中，我们基于模拟优化（SBO）方法提出了一个研究救护车转院策略有效性的新模型。特别是，我们开发了一个离散事件模拟模型来重现急诊科网络的运行。然后，对于所考虑的每一种救护车转院政策，我们制定并解决一个最优资源分配问题，该问题由一个双目标模拟优化问题组成，其目标是使患者花费的无增值时间以及急诊科网络产生的总成本最小化。对于每一种政策，都能得到一组属于帕累托前沿的最优点。为了证明所提出方法的可靠性，我们考虑了一个由意大利拉齐奥地区的六个大型急诊科组成的实际案例研究，分析了采用不同救护车转院政策的效果。
Ambulance Diversion (AD) is one of the possible strategies for relieving the worldwide phenomenon of Emergency Department (ED) overcrowding. It can be carried out when an ED is overloaded and consists of redirecting incoming by ambulance patients to neighboring EDs. Properly implemented, AD should result in reducing delays of patient treatment, ensuring safety and rescue of life-threatening patients. From an operational point of view, AD corresponds to a resource pooling policy among EDs in a network. In this paper we propose a novel model for studying the effectiveness of AD strategies, based on the Simulation-Based Optimization (SBO) approach. In particular, we developed a discrete event simulation model for reproducing the ED network operation. Then, for each AD policy considered, we formulate and solve an optimal resources allocation problem consisting of a bi-objective SBO problem where the target is the minimization of the non-value added time spent by patients and the overall cost incurred by the ED network. A set of optimal points belonging to the Pareto frontier is obtained for each policy. To show the reliability of the proposed approach, a real case study consisting of six large EDs in the Lazio region of Italy is considered, analyzing the effects of adopting different AD policies.
影响因子：
3.6
作者：
Ahalt, Virginia;Argon, Nilay Tanik;Mehrotra, Abhi
通讯作者：
Mehrotra, Abhi