Using Markov Chains to Understand Clinical Decision-Making Process

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.