From Conversation to Agent: LLM-Driven Design of Structured Healthcare Agents

Abstract

Designing effective conversational agents for healthcare requires methods grounded in expert interaction that scale to deployable agents. We present a structured, large language model-driven approach that transforms authentic expert-user dialogues into modular, patient-centered agents. Our three-step pipeline (elicitation, structure extraction, modular implementation) yields interpretable interaction phases implemented with the lightweight agent framework. In a case study on hearing-loss support for young adults, a communication-vulnerable and underserved group, the resulting agents foster purposeful engagement and surface well-being information aligned with WHOQOL domains. Compared with a fat-prompt baseline, the structured agents produced shorter, more focused exchanges and broader coverage of well-being topics. We position this method within context-aware and personalized healthcare systems and argue it offers a reproducible path toward adaptive, transparent, and trustworthy conversational agents.

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