From the Department of Anesthesiology (L.R.R., E.M.P.), Department of Learning Health Sciences (L.R.R., V.P.), University of Michigan, Ann Arbor, MI; and School of Teaching and Learning (H.D.), College of Education, University of Florida, Gainesville, FL.
Simul Healthc. 2023 Oct 1;18(5):321-325. doi: 10.1097/SIH.0000000000000686. Epub 2022 Sep 7.
Extended reality (XR)-based simulation training offers unique features that facilitate collection of dynamic behavioral data and increased immersion/realism while providing opportunities for training health care professionals on critical events that are difficult to recreate in real life. Sequential analysis can be used to summarize learning behaviors by discovering hidden learning patterns in terms of common learning or clinical decision-making sequences. This project describes the use of sequential analysis to examine differential patterns of clinical decision-making behaviors in observed XR scenarios, allowing for new insights when using XR as a method to train for critical events and to trace clinical decision making.
基于扩展现实(XR)的模拟训练提供了独特的功能,可方便地收集动态行为数据,并提高沉浸感/真实性,同时为医疗保健专业人员提供在现实生活中难以重现的关键事件进行培训的机会。序列分析可用于通过发现常见学习或临床决策序列中的隐藏学习模式,从而总结学习行为。本项目描述了使用序列分析来检查观察到的 XR 场景中临床决策行为的差异模式,当使用 XR 作为培训关键事件和跟踪临床决策的方法时,这可以提供新的见解。