Beyond RAG: Reinforced Reasoning Augmented Generation for Clinical Notes
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
Clinical note generation aims to automatically produce free-text summaries of
a patient's condition and diagnostic process, with discharge instructions being
a representative long-form example. While recent large language model
(LLM)-based methods pre-trained on general clinical corpora show promise in
clinical text generation, they fall short in producing long-form notes from
limited patient information. In this paper, we propose R2AG, the first
reinforced retriever for long-form discharge instruction generation based on
pre-admission data. R2AG is trained with reinforcement learning to retrieve
reasoning paths from a medical knowledge graph, providing explicit semantic
guidance to the LLM. To bridge the information gap, we propose Group-Based
Retriever Optimization (GRO) which improves retrieval quality with
group-relative rewards, encouraging reasoning leaps for deeper inference by the
LLM. Comprehensive experiments on the MIMIC-IV-Note dataset show that R2AG
outperforms baselines in both clinical efficacy and natural language generation
metrics. Further analysis reveals that R2AG fills semantic gaps in sparse input
scenarios, and retrieved reasoning paths help LLMs avoid clinical
misinterpretation by focusing on key evidence and following coherent reasoning.
