Clinicians often need rapid, evidence-based answers that integrate patient-specific electronic health records (EHRs) with clinical guidelines, but existing decision support tools are limited in real-time personalization. While large language models (LLMs) offer strong medical reasoning, they are prone to hallucinations and lack direct access to local EHR data, making them unsafe for standalone clinical use; meanwhile, traditional retrieval systems cannot synthesize coherent, context-aware responses. This paper proposes a retrieval-augmented generation (RAG) framework that combines dual-source retrieval from both institutional EHRs and clinical guideline databases. The system includes an EHR indexer, a guideline repository, a semantic retriever, an LLM-based generator, and a safety filter for hallucination mitigation. By grounding outputs in retrieved patient data and evidence-based recommendations, the model improves factual reliability, explainability, and clinical trustworthiness. Overall, the framework enables safe, real-time clinical question answering by integrating LLM reasoning with verified medical sources, with future validation planned on public EHR and guideline datasets.
Clinical Intelligence Research Press