Tolg Boris
University of Applied Sciences Hamburg, Ulmenliet 20, 21033, Hamburg, Germany.
Adv Simul (Lond). 2024 Dec 27;9(1):51. doi: 10.1186/s41077-024-00328-w.
Regular training for mass casualty incidents at physical simulation events is vital for emergency services. The preparation and execution of these simulations consume huge amounts of time, personnel, and money. It is therefore important to gather as much information as possible from each simulation while minimizing any influence on the participants, so as to keep the simulation as realistic as possible. In this paper, an analysis of GPS-based remote motion measurements of participants in a mass casualty incident simulation is presented. A combination of different evaluation methods is used to analyze the data. This could reduce the potential bias of the measurement methods.
Movement patterns of participants of mass casualty incident simulations, measured by GPS loggers, were analyzed. The timeline of the simulation was segmented into event sections, based on movement patterns of participants entering or leaving defined areas. Movement patterns of participants working closely together were correlated to analyze their cooperation. Written logs created by observers on the ground were used to reconstruct the events of the simulation, to provide a comparative reference to validate the motion analysis.
Recorded motion patterns of the participants were found to be qualitatively related to observer logs and triage allocations, allowing a partial reconstruction of the behavior of the participants during the simulation. By analyzing the times the simulation patients left the site of events some possible misjudgments in the triage decisions were indicated.
Analysis of movement patterns from GPS loggers and comparison with observations made on the ground showed that accurate information about the events during the simulation can be automatically delivered. Although the records of observers on the ground are vital to assess details, delegation of the automated analysis of individual and group motion could perhaps allow observers to concentrate on more specific tasks. The partially automated motion analysis methods presented should simplify the process of analyzing mass casualty incident simulations.
在物理模拟活动中针对大规模伤亡事件进行定期培训对紧急服务至关重要。这些模拟的准备和执行耗费大量时间、人力和资金。因此，在尽量减少对参与者影响的同时，从每次模拟中收集尽可能多的信息很重要，以便使模拟尽可能逼真。本文介绍了对大规模伤亡事件模拟中参与者基于全球定位系统（GPS）的远程运动测量进行的分析。使用不同评估方法的组合来分析数据。这可以减少测量方法的潜在偏差。
分析了通过GPS记录仪测量的大规模伤亡事件模拟参与者的运动模式。根据参与者进入或离开定义区域的运动模式，将模拟的时间线划分为事件部分。对密切合作的参与者的运动模式进行关联分析，以研究他们的协作情况。地面观察员创建的书面记录用于重建模拟事件，为验证运动分析提供比较参考。
发现参与者记录的运动模式在质量上与观察员日志和分诊分配相关，从而能够部分重建模拟期间参与者的行为。通过分析模拟患者离开事件现场的时间，指出了分诊决策中一些可能的误判。
对GPS记录仪记录的运动模式进行分析并与地面观察结果进行比较表明，可以自动提供模拟期间事件的准确信息。虽然地面观察员的记录对于评估细节至关重要，但对个人和群体运动的自动分析或许可以让观察员专注于更具体的任务。所提出的部分自动化运动分析方法应能简化大规模伤亡事件模拟的分析过程。
