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Clinical reasoning is a critical yet complex cognitive process of diagnostic and therapeutic decision-making in medical practice that has long challenged precise understanding and assessment. Sequential analysis can be used to uncover patterns and trends in clinical practices, contributing to improved training and ultimately leading to better patient care outcomes. In this study, 21 board-certified anesthesiologists participated in a simulated-based learning scenario requiring them to promptly recognize patient’s condition and initiate appropriate treatment. They were assigned into either the low-performing or high-performing group based on their performance. We utilized Markov Chain Transition Matrix, a robust statistical model for sequential data, to analyze participants’ practices using team reflection behavioral observation system and identified statistically significant differences between their transition matrices. The high-performing group had a much higher transition probability from evaluating information to implementation and from planning to planning. The implications are then discussed. 

Accounting neglect of intervention reasoning in CSCL research, we propose a new process model for team-based diagnostic and intervention reasoning in acute care, focusing on interactions among diagnostic activities (DAs), intervention activities (IAs), and collaborative activities (CAs) such as joint information processing, coordination, and communication. Using epistemic network analysis, we analyzed data from a VR-based cardiac arrest simulation to validate this model by comparing expert- and novice-led teams. As expected, expert-led teams demonstrated faster, more cohesive transitions between DAs and IAs, with a streamlined, linear CA pattern, while novice-led teams exhibited slower, fragmented transitions with cyclical CA patterns. These findings support the model’s potential to capture expertise-driven coordination and efficiency in high-stakes settings. Future research may expand this model across diverse team compositions and problem contexts. By refining understanding of acute care team dynamics, this model paves the way for instructional strategies enhancing coordination and performance in collaborative problem solving.