Understanding the Role of Large Language Model Virtual Patients in Developing Communication and Clinical Skills in Undergraduate Medical Education

Access to practice opportunities for history-taking in undergraduate medical education can be resource-limited. Large language models are a potential avenue to address this. This study sought to characterize changes in learner self-reported confidence with history-taking before and after a simulation with an LLM-based patient and understand learner experience with and the acceptability of virtual LLM-based patients. This was a multi-method study conducted at McMaster University. Simulations were facilitated with the OSCEai tool. Data was collected through surveys with a Likert scale and open-ended questions and semi-structured interviews. A total of 24 participants generated 93 survey responses and 17 interviews. Overall, participants reported a 14.6% increase in comfort with history-taking. Strengths included its flexibility, accessibility, detailed feedback, and ability to provide a judgement-free space to practice. Limitations included its lower fidelity compared to standardized patients and at times repetitive and less clinically relevant feedback as compared to preceptors. It was overall viewed best as a supplement rather than a replacement for standardized patients. In conclusion, LLM-based virtual patients were feasible and valued as an adjunct tool. They can support scalable, personalized practice. Future work is needed to understand objective metrics of improvement and to design curricular strategies for integration.