Towards Actionable Surgical Team Dynamics: from Teamwork to Counterfactual Annotations

Abstract:Modeling team interactions in high-stakes environments such as operating rooms is critical for understanding how coordination, communication, and individual behaviors shape team performance and safety outcomes. Existing datasets in this domain are often fragmented across modalities, annotation schemes, and formats, limiting their ability to support integrated analyses of real-world collaborative processes. We address this limitation by introducing an extended multimodal dataset for surgical team interaction analysis, built from real operating room recordings. Starting from an existing corpus, we construct an analysis-ready version of the data by providing speaker diarization, transcripts, and multi-level annotations capturing team performance, interaction processes, and individual characteristics. Team performance is assessed using a standardized surgical teamwork evaluation protocol, while interaction quality and individual attributes are annotated through structured rating schemes covering collaboration, group dynamics, and non-technical skills. To further support the study of coordination breakdowns and performance variability, we introduce counterfactual annotations that describe plausible alternative team outcomes in the presence of observed interaction failures, enabling analysis of how specific behavioral patterns may relate to different trajectories of team performance. In addition, we provide structured temporal and relational representations designed to support computational modeling of teamwork processes and the design of AI-assisted collaborative systems. The dataset is designed to support the study of how individual actions, interaction patterns, and team-level processes jointly contribute to team outcomes in surgical settings, providing a unified resource for analyzing collaborative behavior in high-stakes domains.

Machine Learning (cs.LG)

arXiv:2608.23344 [cs.LG]

(or arXiv:2608.23344v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.23344 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3834580.3838751 Focus to learn more DOI(s) linking to related resources

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