Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notes

TL;DR

This paper introduces ReinRAG, a method that enhances clinical note generation by retrieving reasoning paths from a medical knowledge graph and optimizing retrieval to improve long-form discharge summaries from limited patient data.

Contribution

ReinRAG combines knowledge graph retrieval with reinforced optimization to improve reasoning and accuracy in clinical note generation from sparse inputs.

Findings

ReinRAG outperforms baseline models in clinical and language metrics.

It effectively fills semantic gaps in sparse input scenarios.

Retrieved reasoning paths help prevent clinical misinterpretation.

Abstract

Clinical note generation aims to produce free-text summaries of a patient's condition and diagnostic process, with discharge instructions being a representative long-form example. While recent 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 ReinRAG, a reinforced reasoning augmented generation (RAG) for long-form discharge instructions based on pre-admission information. ReinRAG retrieves reasoning paths from a medical knowledge graph to provide explicit semantic guidance to the LLM. To bridge the information gap, we propose group-based retriever optimization (GRO) which improves retrieval quality with group-normalized rewards, encouraging reasoning leaps for deeper inference by the LLM. Comprehensive experiments on the real-world dataset show that ReinRAG outperforms baselines in both clinical efficacy and natural language generation metrics. Further analysis reveals that ReinRAG 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.