Load index model: An advanced tool to support decision making during mass-casualty incidents. Issue 3 (March 2015)

• Record Type: Journal Article
• Authors: Adini, Bruria; Aharonson-Daniel, Limor; Israeli, Avi
• Is Part Of: Journal of trauma and acute care surgery. Volume 78:Issue 3(2015:Mar.)
• Publication Date: 2015-03
• Subjects: Mass-casualty event -- hospital congestion -- patient evacuation -- decision making -- load index; Surgical intensive care -- Periodicals; Surgical emergencies -- Periodicals; Wounds and injuries -- Surgery -- Periodicals; 617.026
• DOI: 10.1097/TA.0000000000000535
• Languages: English
• ISSNs: 2163-0755

Abstract

Abstract : BACKGROUND: In 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. METHODS: A 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. RESULTS: Variability 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. CONCLUSION: The 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.