Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs

This paper proposes a real-time actionable approach for modeling surgical team dynamics using time-expanded interaction graphs. The model enables efficient inference with a static graph neural network, predicting procedural efficiency and supporting counterfactual analysis to identify changes in communication structure.