Simulation analysis of critical factors of casualty transportation for disaster response: A case study of Istanbul earthquake

IF 0.9 Q4 ENVIRONMENTAL STUDIES
Nadide Çağlayan, S. I. Satoglu
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引用次数: 5

Abstract

Purpose The purpose of this paper is to statistically assess the effects of the design factors including usage of data-driven decision support tool (DST), classification of patients (triage), prioritization based on vital scores of patients, number of ambulances and hospital selection rules, on the casualty transportation system’s performance in large-scale disasters. Besides, a data-driven DST for casualty transportation is proposed to enhance the casualty survival and ambulance transportation times during the disaster response stage. Design/methodology/approach In this study, the authors applied simulation and statistical analysis to evaluate the effects of usage of data-driven DST, classification of patients (triage), prioritization of the patients based on vital scores, number of ambulances and hospital selection rules, on the patient survival and transportation time of the casualty transportation system. An experimental design was made, and 16 scenarios were formulated. Simulation models were developed for all scenarios. The number of unrecoverable casualties and time-spent by the casualties until arriving at the hospital was observed. Then, a statistical analysis was applied to the simulation results, and significant factors were determined. Findings Utilization of the proposed DST was found to improve the casualty transportation and coordination performance. All main effects of the design factors were found statistically significant for the number of unrecoverable casualties. Besides, for the Time spent Until Arrival of T1-Type Casualty at the Hospital, all of the main factors are significant except the number of ambulances. Respiratory rate, pulse rate, motor response score priority and hospital selection rule based on available hospital capacities must be considered to reduce the number of unrecoverable casualties and time spent until arrival of the casualties at the hospitals. Originality/value In this study, the factors that significantly affect the performance of the casualty transportation system were revealed, by simulation and statistical analysis, based on an expected earthquake case, in a metropolitan city. Besides, it was shown that using a data-driven DST that tracks victims and intends to support disaster coordination centers and medical staff performing casualty transportation significantly improves survival rate of the victims and time to deliver the casualties. This research considers the whole systems’ components, contributes to developing the response stage operations by filling gaps between using the data-driven DST and casualty transportation processes.
灾害响应中伤亡运输关键因素的模拟分析——以伊斯坦布尔地震为例
目的统计评估数据驱动决策支持工具(DST)的使用、患者分类(分诊)、基于患者生命体征的优先级、救护车数量和医院选择规则等设计因素对大规模灾难中伤亡运输系统性能的影响。此外,还提出了一种用于伤员运输的数据驱动DST,以提高灾害响应阶段的伤员存活率和救护车运输时间。设计/方法/方法在本研究中,作者应用模拟和统计分析来评估数据驱动的DST的使用、患者分类(分诊)、基于生命体征评分的患者优先级、救护车数量和医院选择规则对患者存活率和伤员运输系统的运输时间的影响。进行了实验设计,并制定了16个场景。为所有场景开发了模拟模型。观察了无法恢复的伤亡人数以及伤亡人员到达医院所花费的时间。然后,对模拟结果进行统计分析,确定了重要因素。发现利用拟议的DST可以提高伤亡人员的运输和协调性能。设计因素的所有主要影响对不可恢复的伤亡人数都具有统计学意义。此外,对于T1型伤员到达医院的时间,除了救护车的数量外,所有主要因素都是重要的。必须考虑呼吸频率、脉搏率、运动反应评分优先级和基于可用医院容量的医院选择规则,以减少无法恢复的伤亡人数和伤亡人员到达医院所花费的时间。原创性/价值在这项研究中,通过模拟和统计分析,揭示了在大都市预期地震案例的基础上,显著影响伤亡交通系统性能的因素。此外,研究表明,使用数据驱动的DST来跟踪受害者,并打算支持灾难协调中心和医务人员运送伤员,这大大提高了受害者的存活率和运送伤员的时间。这项研究考虑了整个系统的组成部分,通过填补使用数据驱动的DST和伤亡人员运输流程之间的空白,有助于开发响应阶段的操作。
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来源期刊
CiteScore
3.40
自引率
6.20%
发文量
49
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