Towards Modeling Situational Awareness Through Visual Attention in Clinical Simulations

TL;DR

This study uses Transition Network Analysis on eye-tracking data to model and quantify how clinicians' visual attention dynamically shifts during simulated cardiac arrest scenarios, revealing adaptive attentional patterns.

Contribution

It introduces the application of TNA to model visual attention in multiperson clinical simulations, providing new insights into team cognition and situational awareness.

Findings

Clinicians' visual attention shifts adaptively across roles and scenario phases.

CPR role focuses on critical tasks; TeamLead monitors globally.

TNA effectively maps functional differentiation of team cognition.

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.