Abstract

BackgroundMass casualty incidents (MCI) such as train or bus crashes, explosions, collapses of buildings, or terrorist attacks result in rescue teams facing many victims and in huge challenges for hospitals. Simulations are performed to optimize preparedness for MCI. To maximize the benefits of MCI simulations, it is important to collect large amounts of information. However, a clear concept and standardization of a data-driven post-exercise evaluation and debriefing are currently lacking.MethodsGPS data loggers were used to track the trajectories of patients, medics, and paramedics in two simulated MCI scenarios using real human actors. The distribution of patients over the treatment area and their time of arrival at the hospital were estimated to provide information on the quality of triage and for debriefing purposes.ResultsThe results show the order in which patients have been treated and the time for the individual arrivals as an indicator for the triage performance. The distribution of patients at the accident area suggested initial confusion and unclear orders for the placement of patients with different grades of injury that can be used for post-exercise debriefing. The dynamics of movement directions allowed to detect group behavior during different phases of the MCI.ConclusionsResults indicate that GPS data loggers can be used to collect precise information about the trajectories of patients and rescue teams at an MCI simulation without interfering with the realism of the simulation. The exact sequence of the deliverance of patients of different triage categories to their appropriate destinations can be used to evaluate team performance for post-exercise debriefing. Future MCI simulations are planned to validate the use of GPS loggers by providing “hot-debrief” immediately after the MCI simulation and to explore ways in which group detection can provide relevant information for post-exercise evaluationsTrial registrationNot applicable.

Highlights

  • Mass casualty incidents (MCI) such as train or bus crashes, explosions, collapses of buildings, or terrorist attacks result in rescue teams facing many victims and in huge challenges for hospitals

  • If the analysis of movement patterns provides information that may be used for a data-driven post-scenario debriefing of MCI simulations and develop a novel process of collecting the required data

  • The time measurement started with the emergency call at the beginning of the training

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Summary

Introduction

Mass casualty incidents (MCI) such as train or bus crashes, explosions, collapses of buildings, or terrorist attacks result in rescue teams facing many victims and in huge challenges for hospitals. They require a lot of equipment and personnel, including actors coached to act out the (2020) 5:27 using video cameras at the various operational sites [2]. It is important to maintain an optimal degree of immersion in the simulated scenario by a minimum of observers and visible measuring devices at the scene [3]. To analyze these complex situations, an objective measurement method is required [4]

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