Towards Modeling Situational Awareness Through Visual Attention in Clinical Simulations
This work addresses the problem of understanding team cognition in time-critical clinical environments, but it is incremental as it applies an existing method to a new domain.
The study tackled the challenge of characterizing situational awareness in clinical simulations by applying Transition Network Analysis to eye-tracking data from 40 clinicians in VR-based cardiac arrest scenarios, revealing dynamic redistribution of visual attention across roles and phases.
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.