Exploring Ambient Artificial Intelligence to Enhance Learning and Feedback During Operating Room-to-Intensive Care Unit Handoffs: Co-Design and Simulation Study.

Background: Operating room (OR)-to-intensive care unit (ICU) handoffs are among the most complex and high-risk communication events in perioperative care. Despite the implementation of structured checklists, trainees often receive limited feedback on their communication skills, and simulation-based education rarely provides objective data on communication performance and checklist adherence. This study explores how an ambient artificial intelligence (AI) handoff assistant used during simulation-based training of OR-to-ICU handoff discussions can enhance clinical communication training and AI literacy by mapping spoken handoff discussions to handoff checklist items, providing immediate feedback on checklist item omissions, and generating a structured handoff note that functions as a feedback-rich learning artifact. Objective: This study aims to co-design and evaluate an ambient AI handoff assistant that transcribes spoken OR-to-ICU handoff communication, maps the discussion to handoff checklist items, generates a structured handoff note for educational review, and provides immediate feedback on handoff completeness during simulated OR-to-ICU handoff discussions in a low-fidelity educational setting. Methods: A 2-phase mixed-methods study was conducted within the University of California, Los Angeles, Department of Anesthesiology and Perioperative Care (July-October 2025). Phase 1 comprised co-design interviews with 4 clinician educators to identify limitations of current handoff training and inform AI feature development. Phase 2 involved an error analysis, as well as evaluations of usability, workload, and educational impact, conducted through ten 60-minute simulation sessions with pairs of medical students and first-year residents. Quantitative measures included the Physician Task Load Index, System Usability Scale, and a postsimulation survey; qualitative data from co-design sessions and simulation debrief interviews were thematically analyzed. Results: Educators highlighted inconsistent checklist use and the absence of objective feedback on learners' communication skills as key areas that could benefit from structured documentation of handoff discussions using AI. Error analysis of the ambient AI handoff assistant revealed a mean of 3.6 (SD 1.2) errors per note, with incorrect output being the most frequent error type. There was no statistically significant difference between the ambient AI handoff assistant and the paper checklist with respect to the Physician Task Load Index and System Usability Scale measures. Trainees valued real-time transcripts and structured handoff notes for reflection of communication practices, and exposure to AI documentation errors enhanced critical thinking and awareness of AI technology limitations. Conclusions: The ambient AI handoff assistant mapped simulated handoff discussions to checklist items and generated a structured handoff note, facilitating reflection on team-based communication skills in handoff education. Imperfections in the AI's output encouraged critical appraisal of its capabilities and prompted discussion about automation complacency, suggesting that AI-assisted simulations can foster both communication and digital literacy skills essential for future AI-enabled clinical practice.