Search for: All records

Background Team leadership during medical emergencies like cardiac arrest resuscitation is cognitively demanding, especially for trainees. These cognitive processes remain poorly characterized due to measurement challenges. Using virtual reality simulation, this study aimed to elucidate and compare communication and cognitive processes-such as decision-making, cognitive load, perceived pitfalls, and strategies-between expert and novice code team leaders to inform strategies for accelerating proficiency development. Methods A simulation-based mixed methods approach was utilized within a single large academic medical center, involving twelve standardized virtual reality cardiac arrest simulations. These 10- to 15-minutes simulation sessions were performed by seven experts and five novices. Following the simulations, a cognitive task analysis was conducted using a cued-recall protocol to identify the challenges, decision-making processes, and cognitive load experienced across the seven stages of each simulation. Results The analysis revealed 250 unique cognitive processes. In terms of reasoning patterns, experts used inductive reasoning, while novices tended to use deductive reasoning, considering treatments before assessments. Experts also demonstrated earlier consideration of potential reversible causes of cardiac arrest. Regarding team communication, experts reported more critical communications, with no shared subthemes between groups. Experts identified more teamwork pitfalls, and suggested more strategies compared to novices. For cognitive load, experts reported lower median cognitive load (53) compared to novices (80) across all stages, with the exception of the initial presentation phase. Conclusions The identified patterns of expert performance — superior teamwork skills, inductive clinical reasoning, and distributed cognitive strategiesn — can inform training programs aimed at accelerating expertise development. 

This study introduces AutoCLC, an AI-powered system designed to assess and provide feedback on closed-loop communication (CLC) in professional learning environments. CLC, where a sender’s Call-Out statement is acknowledged by the receiver’s Check-Back statement, is a critical safety protocol in high-reliability domains, including emergency medicine resuscitation teams. Existing methods for evaluating CLC lack quantifiable metrics and depend heavily on human observation. AutoCLC addresses these limitations by leveraging natural language processing and large language models to analyze audio recordings from Advanced Cardiovascular Life Support (ACLS) simulation training. The system identifies CLC instances, measures their frequency and rate per minute, and categorizes communications as effective, incomplete, or missed. Technical evaluations demonstrate AutoCLC achieves 78.9% precision for identifying Call-Outs and 74.3% for Check-Backs, with a performance gap of only 5% compared to human annotations. A user study involving 11 cardiac arrest instructors across three training sites supported the need for automated CLC assessment. Instructors found AutoCLC reports valuable for quantifying CLC frequency and quality, as well as for providing actionable, example-based feedback. Participants rated AutoCLC highly, with a System Usability Scale score of 76.4%, reflecting above-average usability. This work represents a significant step toward developing scalable, data-driven feedback systems that enhance individual skills and team performance in high-reliability settings. 

Simulation-based learning has become a cornerstone of healthcare education, fostering essential skills like communication, teamwork or decision-making in safe, controlled environments. However, participants’ reflection on simulations often rely on subjective recollections, limiting their effectiveness in promoting learning. This symposium explores how multimodal analytics and AI can enhance simulation-based education by automating teamwork analysis data, providing structured feedback, and supporting reflective practices. The papers examine real-time analytics for closed-loop communication in cardiac arrest simulations, multimodal data use to refine feedback in ICU nursing simulations, generative AI-powered chatbots facilitating nursing students' interpretation of multimodal learning analytics dashboards, and culturally sensitive, AI-based scenarios for Breaking Bad News in an Indian context. Collectively, these contributions highlight the transformative potential of using data and AI-enhanced solutions, emphasizing personalization, cultural sensitivity, and human-centered design, and invite dialogue on the pedagogical, technological and ethical implications of introducing data-based practices and AI-based tools in medical education. 

This NSF-funded study aims to develop and evaluate a novel debriefing system that aims to capture and visualize multimodal data streams from multi-user VR environment that evaluate learners’ cognitive (clinical decision-making) and behavioral (situational awareness, communication) processes to provide data-informed feedback focused on improving team-based care of patients who suffer sudden medical emergencies. Through this new multimodal debriefing system, instructors will be able to provide personalized feedback to clinicians during post-simulation debriefing sessions.