An analysis of movement patterns in mass casualty incident simulations
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RESEARCH
Open Access
An analysis of movement patterns in mass casualty incident simulations Boris Tolg*†
and Juergen Lorenz†
Abstract Background: 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. 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. Methods: GPS 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. Results: The 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. Conclusions: Results 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 evaluations Trial registration: Not applicable. Keywords: Mass casualty incident, Simulation, Movement patterns, Group recognition
Background Mass casualty incidents (MCI) are situations in which the medical capacity of a region is insufficient to handle the volume and complexity of tasks [1]. MCI simulations are performed to optimize the preparedness of pre- and inhospital care teams for MCI. However, MCI simulations are costly and complex. They require a lot of equipment and personnel, including actors coached to act out the *Correspondence: [email protected] † Boris Tolg and Juergen Lorenz contributed equally to the manuscript. Hamburg University of Applied Sciences, Hamburg, Ulmenliet 20, 21033 Hamburg, Germany
behavior of victims and specially trained teams to provide them with make-up for realistic injury appearance. To guarantee the optimal benefit of MCI simulations, it is important to collect large amounts of information and data without interfering wit
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