{"title":"Modeling and Simulation of In-Hospital Disaster Medicine in a Mass Casualty Event for the Resilience Evaluation of BCPs","authors":"Mizuki Umemoto, Shunsuke Kadono, T. Kanno, Kazumi Kajiyama, Sachika Sharikura, Ryoko Ikari, Masashi Yoneyama, Sheuwen Chuang","doi":"10.20965/jdr.2023.p0104","DOIUrl":null,"url":null,"abstract":"In this study, we developed a simulation model of detailed in-hospital disaster response to a mass casualty incident based on the analysis of related documents and actual in-hospital disaster response training, aiming to assess the hospital’s response capacity under various disaster situations. This simulation model includes detailed models of patients, floor configurations, resources, and response tasks, which consider resource requirements for the treatment of different patients with various injuries and physical conditions. The model covers patients’ arrivals to hospitalization or discharge. We conducted simulations of the target hospital to test two resource allocation strategies under two patient scenarios. By comparing the results under different resource allocation strategies, we found that the X-ray photography examination capacity could become a fundamental bottleneck in responding to mass casualty incidents. Also, we found that the appropriate resource allocations and quick replenishment could alleviate the negative effect of resource shortages and maintain a higher performance. Furthermore, the results show that the length of stay can be heavily affected by the patients’ configuration. As a result, by monitoring and anticipating the situation, a resilient and responsive resource allocation strategy must be prepared to handle such uncertain disaster situations.","PeriodicalId":46831,"journal":{"name":"Journal of Disaster Research","volume":" ","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Disaster Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20965/jdr.2023.p0104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, we developed a simulation model of detailed in-hospital disaster response to a mass casualty incident based on the analysis of related documents and actual in-hospital disaster response training, aiming to assess the hospital’s response capacity under various disaster situations. This simulation model includes detailed models of patients, floor configurations, resources, and response tasks, which consider resource requirements for the treatment of different patients with various injuries and physical conditions. The model covers patients’ arrivals to hospitalization or discharge. We conducted simulations of the target hospital to test two resource allocation strategies under two patient scenarios. By comparing the results under different resource allocation strategies, we found that the X-ray photography examination capacity could become a fundamental bottleneck in responding to mass casualty incidents. Also, we found that the appropriate resource allocations and quick replenishment could alleviate the negative effect of resource shortages and maintain a higher performance. Furthermore, the results show that the length of stay can be heavily affected by the patients’ configuration. As a result, by monitoring and anticipating the situation, a resilient and responsive resource allocation strategy must be prepared to handle such uncertain disaster situations.