Actively Recruiting
Comparing In-Situ and Off-Site Simulation Training for Adult Non-Trauma Resuscitation Teams Using the A-C-L-S Model
Led by National Taiwan University Hospital · Updated on 2026-01-22
100
Participants Needed
1
Research Sites
52 weeks
Total Duration
AI-Summary
What this Trial Is About
Researchers are evaluating the effectiveness of two simulation training methods, In-situ Simulation ISS and Off-site Simulation OSS, using the A-C-L-S teamwork model to improve adult non-trauma cardiac arrest resuscitation teams. This study uses a two-group non-randomized pre-post design conducted at two different campuses of the National Taiwan University Hospital Yunlin Branch. The goal is to compare team performance and patient outcomes between teams trained in real clinical environments versus those trained in dedicated simulation centers. Participants in the ISS group receive high-fidelity simulation training in their actual emergency department, including a 20-minute briefing, 10-minute simulation exercise, and 30-minute debriefing focused on teamwork and non-technical skills. The OSS group undergoes similar training at a separate simulation center with the same timing and teamwork focus but outside the clinical setting. Both groups follow the A-C-L-S model for structuring team roles during adult non-trauma resuscitation. Throughout the one-year study period, team performances are assessed by blinded external experts using video recordings and the Team Emergency Assessment Measure TEAM scale to evaluate non-technical skills. Secondary outcomes include timing of resuscitation steps, quality of chest compressions, and patient return of spontaneous circulation rates. Data collection aims to support evidence-based education, improve resuscitation efficiency, and enhance institutional teaching and research capabilities.
CONDITIONS
Brief Title
Effectiveness of In-Situ Simulation Training in Adult Non-Trauma Resuscitation: A Comparison of ISS and OSS Team Performance Using the A-C-L-S Model
Research Team
L
Liang-wei Wang
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