MedEasy: Designing AI Standardized Patients for Clinical Consultation Training
This paper addresses the need for realistic, repeatable clinical training tools for medical students, but the findings are incremental and based on a small user study.
MedEasy is a multi-agent system for clinical consultation training using AI standardized patients. In a user study with 12 medical students, learners valued repeatable practice and recorded review but questioned missing actions and feedback criteria.
AI standardized patients are becoming a setting for professional training in clinical consultation. This paper presents MedEasy, a multi-agent system that organizes virtual-patient practice through patient dialogue, clinical actions, decision submission, documentation, and feedback. We first conducted a formative study with 12 clinical-year medical students through interviews and three co-design workshops. The findings informed a staged workflow, structured case records, action-contingent findings, and trajectory-based review. We then conducted an evaluative user study with a separate cohort of 12 clinical-year medical students, with each participant completing two counterbalanced cases. Learners interpreted MedEasy as a connected consultation environment. They used patient responses, examination findings, available actions, and feedback together to judge whether the represented case remained coherent. They valued repeatable practice and recorded review, while questioning missing actions and feedback criteria. The paper contributes design implications for AI-supported professional training systems that use case-specific standards to connect situated practice.