Abstract
Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment. However, chatbot development and evaluation often rely on cooperative, articulate, simulated patients. We analyzed 2,053 real patient-chatbot conversations and found that communication patterns and expression of emotions vary widely across users.
To better understand patient needs, we developed a patient simulator that separately models clinical content, emotional state, conversational strategy, and communication style. In a Turing-inspired evaluation of realism with 15 human graders, simulated conversations were nearly indistinguishable from real ones, with human graders achieving an accuracy of 55%.
We used five distinct patient personae, across 1,164 clinician-graded cases, to evaluate the performance of four LLMs in urgency assessment. We found that communication style can significantly alter triage outcomes. Patient-centered conversational artificial intelligence must accommodate communication diversity: systems designed for idealized, rather than realistic, interactions risk underperforming and amplifying health disparities when deployed in the real world.
Blogger's Review: This study highlights the complexities of communication between real patients and chatbots, emphasizing the importance of accommodating communication diversity in the development of AI healthcare systems. Idealized designs may overlook real needs, leading to issues of equity in healthcare services. More focus is needed on how technology can better serve patients from diverse backgrounds.