The complexities of patient-centred conversational artificial intelligence

Study finds LLM health chatbot triage outcomes vary significantly with patient communication style

Researchers analyzed 2,053 real patient-chatbot conversations and found wide variation in communication patterns and emotional expression, revealing that idealized patient simulations used in chatbot development are inadequate. They built a patient simulator modeling clinical content, emotional state, conversational strategy, and communication style, achieving near-indistinguishability from real conversations (human graders 55% accurate). Evaluating four LLMs across 1,164 clinician-graded cases with five patient personae, the study found that communication style significantly alters triage urgency assessments. The authors warn that systems optimized for cooperative, articulate users risk underperforming and amplifying health disparities in real-world deployment.