Ohio State University, School of Health and Rehabilitation Sciences, Columbus, OH, USA.
Unveil, LLC, Dayton, OH, USA.
Appl Ergon. 2024 Jul;118:104265. doi: 10.1016/j.apergo.2024.104265. Epub 2024 Mar 12.
Resilient system performance in high-stakes settings, which includes the ability to monitor, respond, anticipate, and learn, can be enhanced for trainees through simulation of realistic scenarios enhanced by augmented reality. Active learning strategies can enhance simulation-based training, particularly the mental model articulation principle where students are prompted to anticipate what will happen next and the reflection principle where students self-assess their performance compared to a gold standard expert model. In this paper, we compared simulation-based training for trauma care with and without active learning strategies during pauses in the simulated action for progressively deteriorating patients. The training was conducted online and real-time without a facilitator, with 42 medical students viewing training materials and then immediately taking an online quiz for three types of trauma cases: hemorrhage, airway obstruction, and tension pneumothorax. Participants were randomly assigned to either the experimental or control condition in a between-subjects design. We compared performance in the control and experimental conditions based on: A) the proportion of cues correctly recognized, B) the proportion of accurate diagnoses, C) the proportion of appropriate treatment interventions, and D) verbal briefing quality on a 1-5 scale. We found that the training intervention increased recognition of subtle cues critical for accurate diagnosis and appropriate treatment interventions; the training did not improve the accuracy of diagnoses or the quality of the verbal briefing. We conclude that incorporating active learning strategies in simulation-based training improved foundational capabilities in detecting subtle cues and intervening to rescue deteriorating patients that can increase the readiness for trainees to contribute to resilient system performance in the high-stakes setting of emergency care in hospitals.
在高风险环境中具有弹性的系统性能，包括监测、响应、预测和学习的能力，可以通过增强现实技术增强的现实场景模拟来提高学员的能力。主动学习策略可以增强基于模拟的培训，特别是在心理模型表达原则下，学生被提示预测接下来会发生什么，以及在自我评估原则下，学生将自己的表现与黄金标准专家模型进行比较。在本文中，我们比较了在模拟动作暂停期间，是否使用主动学习策略进行创伤护理的基于模拟的培训，对于病情逐渐恶化的患者。培训是在线实时进行的，没有 facilitator，42 名医学生观看培训材料，然后立即参加三种创伤病例的在线测验：出血、气道阻塞和张力性气胸。参与者在被试间设计中被随机分配到实验组或对照组。我们根据以下标准比较了对照组和实验组的表现：A）正确识别线索的比例，B）准确诊断的比例，C）适当治疗干预的比例，以及 D）1-5 分制的口头汇报质量。我们发现，培训干预提高了对准确诊断和适当治疗干预至关重要的细微线索的识别能力；培训并没有提高诊断的准确性或口头汇报的质量。我们得出结论，在基于模拟的培训中加入主动学习策略可以提高检测细微线索和介入以抢救病情恶化患者的基本能力，从而提高学员在医院急诊高风险环境中对弹性系统性能的准备程度。
