Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notes
This work addresses the challenge of producing accurate and coherent long-form clinical notes for healthcare professionals, representing an incremental improvement over existing LLM-based methods by enhancing retrieval and reasoning capabilities.
The paper tackled the problem of generating long-form clinical discharge notes from limited patient information by proposing ReinRAG, a reinforced reasoning augmented generation method that retrieves reasoning paths from a medical knowledge graph and uses group-based retriever optimization to improve retrieval quality, resulting in outperforming baselines in clinical efficacy and natural language generation metrics on a real-world dataset.
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.