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Towards Modeling Situational Awareness Through Visual Attention in Clinical Simulations

Haoting Gao, Kapotaksha Das, Mohamed Abouelenien, Michael Cole, James Cooke, Vitaliy Popov

TL;DR

Transition Network Analysis (TNA) is applied to model visual attention in multiperson VR-based cardiac arrest simulations and reveals that individual and team's visual attention is dynamically and adaptively redistributed across roles and scenario phases.

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.

Towards Modeling Situational Awareness Through Visual Attention in Clinical Simulations

TL;DR

Transition Network Analysis (TNA) is applied to model visual attention in multiperson VR-based cardiac arrest simulations and reveals that individual and team's visual attention is dynamically and adaptively redistributed across roles and scenario phases.

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.
Paper Structure (16 sections, 3 equations, 3 figures, 2 tables)

This paper contains 16 sections, 3 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Study Context and Data Collection
  4. Defining Areas of Interest (AOIs)
  5. Gaze Metrics and Transition Construction
  6. AOI Fixation Sequence Construction
  7. Transition Matrix Estimation
  8. Transition-Network Metrics
  9. Entropy
  10. Self-loop Rate
  11. Transition Network Analysis
  12. Results & Discussion
  13. Role-Based Patterns in Visual Attention
  14. Stage-Based Shifts in Attentional Structure
  15. Conclusion & Future Work
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Visual for AOIs as seen in VR Environment ("Other Team Members" AOI not present in this Figure).
  • Figure 2: Role-specific AOI transition networks for four resuscitation roles across seven AOIs, aggregated over all sessions. Node size reflects total fixations; edge thickness indicates transition probability; red edges denote self-loops.
  • Figure 3: Transition Network Diagrams for CPR and TeamLead Roles across two Stages of the Cardiac Arrest Simulation. Fig. \ref{['fig:cpr1']} and \ref{['fig:tl1']} from Stage 1, are at the start of the simulation, while Fig. \ref{['fig:cpr5']} and \ref{['fig:tl5']} are from Stage 5, later in the simulation.