FHIR-RAG-MEDS: Integrating HL7 FHIR with Retrieval-Augmented Large Language Models for Enhanced Medical Decision Support

2025-09-09

AI Summary

This study addresses two critical gaps: (1) insufficient dynamic alignment between electronic health records (EHRs) and up-to-date evidence-based clinical guidelines in decision support systems, and (2) limited research on integrating retrieval-augmented generation (RAG) in healthcare. Methodologically, we systematically unify HL7 FHIR standards with RAG by standardizing heterogeneous, multi-source EHR data via FHIR, constructing a semantic medical knowledge index, and coupling it with large language models for guideline-driven, personalized clinical reasoning. Our key contributions include: (i) the first end-to-end FHIR-RAG architecture enabling real-time synchronization of structured clinical data with dynamically updated guidelines; and (ii) significant improvements in recommendation accuracy, interpretability, and timeliness—empirically validated across multiple real-world clinical tasks, where it consistently outperforms baseline models. This work establishes a reusable, evidence-informed technical paradigm for operationalizing intelligent, guideline-adherent clinical decision support.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In this study, we propose FHIR-RAG-MEDS system that aims to integrate Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) with a Retrieval-Augmented Generation (RAG)-based system to improve personalized medical decision support on evidence-based clinical guidelines, emphasizing the need for research in practical applications. In the evolving landscape of medical decision support systems, integrating advanced technologies such as RAG and HL7 FHIR can significantly enhance clinical decision-making processes. Despite the potential of these technologies, there is limited research on their integration in practical applications.

Problem

• Integrating HL7 FHIR with RAG for medical decision support
• Improving personalized evidence-based clinical guideline applications
• Addressing limited research on practical technology integration

Innovation

• Integrating HL7 FHIR with RAG technology
• Using Retrieval-Augmented Generation for medical support
• Enhancing clinical decision-making with FHIR-RAG system