Sequential Behavioral Analysis: A Novel Approach to Help Understand Clinical Decision-Making Patterns in Extended Reality Simulated Scenarios

Abstract

Statement: 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.

From static web to metaverse: reinventing medical education in the post-pandemic era

“…This can accelerate skill acquisition and improve muscle memory before performing procedures on real patients. Novel Assessment and Evaluation Methods Coupled with Metaverse Applications: The collection of discrete, nuanced, and real-time data on trainee or team processes (such as behavior, visual attention, speech analysis, and emotional arousal) that unfold over time and that have previously been difficult or impossible to observe is now possible with the help of new high-fidelity sensor technology and computational models [ 43 ]. Real-time team behavioral data collection can uncover significant correlations, spot patterns, and give each member personalized, learner-specific feedback.…”