Chatbot To Help Patients Understand Their Health

ACL ARR 2025 May Submission5064 Authors

Abstract: Patients must possess the knowledge necessary to actively participate in their care. To this end, we developed NoteAid-Chatbot, a conversational AI designed to help patients better understand their health through a novel framework of learning as conversation. We introduce a new learning paradigm that leverages a multi-agent large language model (LLM) and reinforcement learning (RL) framework—without relying on costly human-generated training data. Specifically, NoteAid-Chatbot was built on a lightweight 3-billion-parameter LLaMA 3.2 model using a two-stage training approach: initial supervised fine-tuning on conversational data synthetically generated using medical conversation strategies, followed by RL with rewards derived from patient understanding assessments in simulated hospital discharge scenarios. Our evaluation, which includes comprehensive human-aligned assessments and case studies, demonstrates that NoteAid-Chatbot exhibits key emergent behaviors critical for patient education—such as clarity, relevance, and structured dialogue—even though it received no explicit supervision for these attributes. Our results show that even simple Proximal Policy Optimization (PPO)-based reward modeling can successfully train lightweight, domain-specific chatbots to handle multi-turn interactions, incorporate diverse educational strategies, and meet nuanced communication objectives. Our Turing test demonstrates that NoteAid-Chatbot surpasses non-expert human. Although our current focus is on healthcare, the framework we present illustrates the feasibility and promise of applying low-cost, PPO-based RL to realistic, open-ended conversational domains—broadening the applicability of RL-based alignment methods.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: Dialogue and Interactive systems

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency

Languages Studied: English

Keywords: chatbot, healthcare, discharge, patient education

Submission Number: 5064