• Bruria Adini, Limor Aharonson-Daniel, and Avi Israeli.
• From the PREPARED Center for Emergency Response Research (B.A., L.A.-D.), and Department of Emergency Medicine (B.A., L.A.-D.), Leon and Mathilde Recanati School of Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva; Hadassah (A.I.), Hebrew University Medical Center; and Ministry of Health (A.I.), Jerusalem, Israel.
• J Trauma Acute Care Surg. 2015 Mar 1; 78 (3): 622-7.

BackgroundIn mass-casualty events, accessing information concerning hospital congestion levels is crucial to improving patient distribution and optimizing care. The study aimed to develop a decision support tool for distributing casualties to hospitals in an emergency scenario involving multiple casualties.MethodsA comprehensive literature review and structured interviews with 20 content experts produced a shortlist of relevant criteria for inclusion in the model. A "load index model" was prepared, incorporating results of a modified Delphi survey of 100 emergency response experts. The model was tested in three simulation exercises in which an emergency scenario was presented to six groups of senior emergency managers. Information was provided regarding capacities of 11 simulated admitting hospitals in the region, and evacuation destinations were requested for 600 simulated casualties. Of the three simulation rounds, two were performed without the model and one after its presentation. Following simulation experiments and implementation during a real-life security threat, the efficacy of the model was assessed.ResultsVariability between experts concerning casualties' evacuation destinations decreased significantly following the model's introduction. Most responders (92%) supported the need for standardized data, and 85% found that the model improved policy setting regarding casualty evacuation in an emergency situation. These findings were reaffirmed in a real-life emergency scenario.ConclusionThe proposed model improved capacity to ensure evacuation of patients to less congested medical facilities in emergency situations, thereby enhancing lifesaving medical services. The model supported decision-making processes in both simulation exercises and an actual emergency situation.

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