Objective To develop a new model to quantify information management dynamically and to identify factors that lead to information gaps. Background Information management is a core task for emergency medical service (EMS) team leaders during the prehospital phase of a mass-casualty incident (MCI). Lessons learned from past MCIs indicate that poor information management can lead to increased mortality. Various instruments are used to evaluate information management during MCI training simulations, but the challenge of measuring and improving team leaders’ abilities to manage information remains. Method The Dynamic Communication Quantification (DCQ) model was developed based on the knowledge representation typology. Using multi point-of-view synchronized video, the model quantifies and visualizes information management. It was applied to six MCI simulations between 2014 and 2019, to identify factors that led to information gaps, and compared with other evaluation methods. Results Out of the three methods applied, only the DCQ model revealed two factors that led to information gaps: first, consolidation of numerous casualties from different areas, and second, tracking of casualty arrivals to the medical treatment area and departures from the MCI site. Conclusion The DCQ model allows information management to be objectively quantified. Thus, it reveals a new layer of knowledge, presenting information gaps during an MCI. Because the model is applicable to all MCI team leaders, it can make MCI simulations more effective. Application This DCQ model quantifies information management dynamically during MCI training simulations.

BACKGROUND: Video communications during cardiopulmonary resuscitation (CPR) can improve the quality of information exchange between a bystander performing CPR and an emergency medical dispatcher (EMD). OBJECTIVE: To improve chest compression effectiveness, a filming protocol instructing video camera placements around a patient was developed. This study measured whether the filming protocol increased chest compressions' effectiveness. METHODS: A simulation study was conducted comparing CPR effectiveness under three conditions: telephone-instructed, video-instructed, and video-instructed with the filming protocol. Twenty-five emergency medical technicians acted as EMDsin the three conditions. A mannequin measured five factors that determined the effectiveness of the chest compressions. RESULTS: Compared with telephone-instructed CPR, the filming protocol improved the proportion of time in which the bystander's hands were in the correct position during chest compressions. Compared with video-instructed CPR, the filming protocol improved both the proportion of time in which the chest was fully released after each compression and the proportion of time in which the compressions were conducted with an appropriate rhythm. The depth and rate of compressions did not improve in the filming protocol condition. CONCLUSIONS: Video-instructed CPR with the filming protocol improves CPR effectiveness compared to telephoneand video-instructed CPR. Detailed implementation can improve new technology introduction.

Introduction: Information management (also known as “communication” or “gathering and analyzing information”) is one of the core tasks of MCI (Mass-Casualty Incident) commanders during the chaotic prehospital phase.1 To train EMS (emergency medical service) personnel on how to manage an MCI efficiently, simulation drills are conducted in which participant performance is evaluated. As such, varied instruments have been developed to measure information management quality during MCI simulations. Since every instrument could lead to different results, our goal at the current study was to evaluate what are the advantages of applying each one of the instruments. Methods: Three instruments were chosen for the current study: (1) The Self-Assessment Teamwork Tool for Students (SATTS), (2) a set of performance indicators aimed to be scored by an experienced observer, and (3) Dynamic Information Management Quantification Instrument (DIMQI) which allows quantifying information items. All instruments were applied to the same MCI simulation. Results and Conclusions: Applying each of the instruments yielded a different layer of information. Thus, researchers and instructors consider using all three instruments as they are complementary to each other.

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