Browsing by keyword "mass casualty incidents"

Now showing items 1-2 of 2

    • Comparing the Accuracy of Three Pediatric Disaster Triage Strategies: A Simulation-Based Investigation
      Cicero, Mark X.; Overly, Frank; Brown, Linda; Yarzebski, Jorge D.; Walsh, Barbara M.; Shabanova, Veronika; Auerbach, Marc; Riera, Antonio; Adelgais, Kathleen; Meckler, Garth; et al. (2016-04-01)
      BACKGROUND: It is unclear which pediatric disaster triage (PDT) strategy yields the best accuracy or best patient outcomes. METHODS: We conducted a cross-sectional analysis on a sample of emergency medical services providers from a prospective cohort study comparing the accuracy and triage outcomes for 2 PDT strategies (Smart and JumpSTART) and clinical decision-making (CDM) with no algorithm. Participants were divided into cohorts by triage strategy. We presented 10-victim, multi-modal disaster simulations. A Delphi method determined patients' expected triage levels. We compared triage accuracy overall and for each triage level (RED/Immediate, YELLOW/Delayed, GREEN/Ambulatory, BLACK/Deceased). RESULTS: There were 273 participants (71 JumpSTART, 122 Smart, and 81 CDM). There was no significant difference between Smart triage and CDM. When JumpSTART triage was used, there was greater accuracy than with either Smart (P < 0.001; OR [odds ratio]: 2.03; interquartile range [IQR]: 1.30, 3.17) or CDM (P=0.02; OR: 1.76; IQR: 1.10, 2.82). JumpSTART outperformed Smart for RED patients (P=0.05; OR: 1.48; IQR: 1.01,2.17), and outperformed both Smart (P < 0.001; OR: 3.22; IQR: 1.78,5.88) and CDM (P < 0.001; OR: 2.86; IQR: 1.53,5.26) for YELLOW patients. Furthermore, JumpSTART outperformed CDM for BLACK patients (P=0.01; OR: 5.55; IQR: 1.47, 20.0). CONCLUSION: Our simulation-based comparison suggested that JumpSTART triage outperforms both Smart and CDM. JumpSTART outperformed Smart for RED patients and CDM for BLACK patients. For YELLOW patients, JumpSTART yielded more accurate triage results than did Smart triage or CDM.

    • Use of Facial Recognition Software to Identify Disaster Victims With Facial Injuries
      Broach, John; Yong, Rothsovann; Manuell, Mary-Elise; Nichols, Constance (2017-04-10)
      OBJECTIVE: After large-scale disasters, victim identification frequently presents a challenge and a priority for responders attempting to reunite families and ensure proper identification of deceased persons. The purpose of this investigation was to determine whether currently commercially available facial recognition software can successfully identify disaster victims with facial injuries. METHODS: Photos of 106 people were taken before and after application of moulage designed to simulate traumatic facial injuries. These photos as well as photos from volunteers' personal photo collections were analyzed by using facial recognition software to determine whether this technology could accurately identify a person with facial injuries. RESULTS: The study results suggest that a responder could expect to get a correct match between submitted photos and photos of injured patients between 39% and 45% of the time and a much higher percentage of correct returns if submitted photos were of optimal quality with percentages correct exceeding 90% in most situations. CONCLUSIONS: The present results suggest that the use of this software would provide significant benefit to responders. Although a correct result was returned only 40% of the time, this would still likely represent a benefit for a responder trying to identify hundreds or thousands of victims.