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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.