ArticleAEM education and training2018
60 Seconds to Survival: A Multisite Study of a Screen-based Simulation to Improve Prehospital Providers Disaster Triage Skills.
Article in AEM education and training, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Learning components for mixed reality mass casualty incident training: a modified Delphi study.BMC medical education · 2026Article
- The effect of "typical case discussion and scenario simulation" on the critical thinking of midwifery students: Evidence from China.BMC medical education · 2024Article
- Disaster Triage Skills Training: An Introductory Virtual Simulation for Medical Students.Cureus · 2023Article
- Full-scale simulations to improve disaster preparedness in hospital pharmacies.BMC health services research · 2022Article
- Prehospital major incident management: how do training and real-life situations relate? A qualitative study.BMJ open · 2021Article
- Cost-effectiveness of a video game versus live simulation for disaster training.BMJ simulation & technology enhanced learning · 2020Article
- 60 Seconds to Survival: A Multisite Study of a Screen-based Simulation to Improve Prehospital Providers Disaster Triage Skills.AEM education and training · 2018Article
Corrections and comments
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Authors and funding
14 authors at 9 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesParamedics and emergency medical technicians (EMTs) perform triage at disaster sites. There is a need for disaster triage training. Live simulation training is costly and difficult to deliver. Screen-based simulations may overcome these training barriers. We hypothesized that a screen-based simulation, 60 Seconds to Survival (60S), would be associated with in-game improvements in triage accuracy.
methodsThis was a prospective cohort study of a screen-based simulation intervention, 60S. Participants included emergency medical services (EMS) personnel from 21 EMS agencies across 12 states. Participants performed assessments (e.g., check for pulse) and actions (e.g., reposition the airway) for 12 patients in each scenario and assigned color-coded triage levels (red, yellow, green, or black) to each patient. Participants received on-screen feedback about triage performance immediately after each scenario. A scoring system was designed to encourage accurate and timely triage decisions. Participants who played 60S included practicing EMTs, paramedics, and nurses as well as students studying to assume these roles. Participants played the game at least three times over 13 weeks.
resultsIn total, 2,234 participants began game play and 739 completed the study and were included in the analysis. Overall, the median number of plays of the game was just above the threshold inclusion criteria (three or more plays) with a median of four plays during the study period (interquartile range [IQR] = 3-7). There was a significant difference in triage accuracy from the first play of the game to the last play of the game. Median baseline triage accuracy in the game was 89.7% (IQR = 82.1%-94.9%), which then increased to a median of 100% at the last game play (IQR = 87.5%-100.0%; p < 0.001). There was some variability in median triage accuracy on fourth through 11th game plays, ranging from 95% to 100%, and on the 12th to 16th plays, the median accuracy was sustained at 100%. There was a significant decrease in the rate of undertriage: from 10.3% (IQR = 5.1%-18.0%) to 0 (IQR = 0%-12.5%; p < 0.001).
conclusion60 Seconds to Survival is associated with improved in-game triage accuracy. Further study of the correlation between in-game triage accuracy and improvements in live simulation or real-world triage decisions is warranted.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.