An innovative X-RAG technique combined with GPT-4o for summarizing medical information from EHR and EMR to assist doctors in clinical decision-making effectively and efficiently.

Abstract

Background: Large language models (LLM) still face challenges in accurately extracting and summarizing medical information from EHR and EMR. The variability in EHR and EMR formats across institutions further complicates information integration. Moreover, doctors need to spend a lot of time reviewing patient information, which affects the efficiency and effectiveness of clinical decision-making. Objective: This study aims to develop a medical record summarization system that uses the innovative X-RAG technique with GPT-4o to extract medical information from EHR and EMR and convert them into structured FHIR format. The system ultimately generates a doctor-friendly report to improve the efficiency and effectiveness of clinical decision-making. Methods: We propose an innovative X-RAG, which adds page-based chunking, chunk filtering, and guided extraction prompting to the basic framework of RAG and combines it with GPT-4o to extract medical measurement data, diagnostic reports, and medication history records from EHR and EMR with high accuracy. Results: The system achieved 96.5% accuracy in medical data extraction and reduced approximately 40% of the time doctors spend reviewing patient information in clinical applications. Conclusion: The proposed system improves the efficiency and effectiveness of clinical decision-making and provides a valuable tool to optimize medical information management and clinical workflows.