MedSyn: AI-driven Medical History Summarization Using EHR Data

The increasing complexity and volume of Electronic Health Records (EHRs) present ongoing challenges for health-care professionals in obtaining meaningful clinical insights. The Intention of this paper is to introduce an AI-driven solution that employs advanced natural language processing (NLP) techniques and Large Language Models (LLMs) to summarize unstructured EHR data through a question-answering framework. Our approach combines Retrieval-Augmented Generation (RAG) with the Mixtral-8×7b-v0.1 LLM, augmented by Knowledge Graphs, to provide context-aware responses to medical queries based on FHIR-standard EHR data. By leveraging knowledge graphs, the system is able to establish structured data relationships, enhancing retrieval efficiency and interpretability. Fully aligned with the Fast Healthcare Interoperability Resources (FHIR) standard, this method ensures interoperability and smooth integration into existing healthcare systems.