Towards Modeling Situational Awareness Through Visual Attention in Clinical Simulations

2026-03-11

AI Summary

This study addresses the challenge of quantifying the dynamic and distributed nature of situational awareness (SA) in high-stress clinical team settings. For the first time, transfer network analysis (TNA) is applied to multi-participant VR cardiac arrest simulations, leveraging eye-tracking data to construct visual attention transfer networks. By integrating metrics such as entropy and self-loop rates, the research characterizes the structure and flow of attention across roles and scenario phases. Findings reveal that CPR performers maintain focused attention on critical tasks, whereas Team Leads progressively enhance global monitoring as the clinical scenario evolves, demonstrating role-specific and phase-adaptive cognitive division of labor. This approach establishes a novel paradigm for modeling team SA and enhancing acute care training.

Technology Category

• Computer Vision: Scene Analysis & Understanding
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Teamwork

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Situational awareness (SA) is essential for effective team performance in time-critical clinical environments, yet its dynamic and distributed nature remains difficult to characterize. In this preliminary study, we apply Transition Network Analysis (TNA) to model visual attention in multiperson VR-based cardiac arrest simulations. Using eye-tracking data from 40 clinicians assigned to four standardized roles (Airway, CPR, Defib, TeamLead), we construct gaze transition networks between clinically meaningful areas of interest (AOIs) and extract metrics such as entropy and self-loop rate to quantify attentional structure and flow. Our findings reveal that individual and team's visual attention is dynamically and adaptively redistributed across roles and scenario phases, with those in CPR roles narrowing their focus to execution-critical tasks and those in the TeamLead role concentrating on global monitoring as clinical demands evolve. TNA thus provides a powerful lens for mapping functional differentiation of team cognition and may support the development of phase-sensitive analytics and targeted instructional interventions in acute care training.

Problem

• Situational Awareness
• Visual Attention
• Clinical Simulations
• Team Cognition
• Eye-tracking

Innovation

• Transition Network Analysis
• visual attention
• situational awareness
• eye-tracking
• clinical simulation