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Related Experiment Video

Updated: Sep 12, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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LLM-Integrated Normalization and Knowledge for FHIR (LINK-FHIR).

Zhen Hou1, Ming Jiang2, Hao Liu3

  • 1Department of Biomedical Engineering and Informatics.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary

LINK-FHIR efficiently converts diverse healthcare data into Fast Healthcare Interoperability Resources (FHIR). This novel system enhances health data exchange and operational efficiency in healthcare institutions.

Keywords:
Automatic Data ProcessingHealth Information InteroperabilityLarge Language ModelsMachine LearningNatural Language Processing

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Existing methods struggle to efficiently convert varied healthcare data into standardized Fast Healthcare Interoperability Resources (FHIR).
  • Lack of interoperability hinders seamless Health Information Exchange (HIE).

Purpose of the Study:

  • To introduce LINK-FHIR, a novel system for converting diverse Electronic Health Records (EHRs) into FHIR-compliant resources.
  • To address the need for efficient and standardized healthcare data conversion.

Main Methods:

  • Utilizes fine-tuned Large Language Models (LLMs) within a unified pipeline.
  • Processes unstructured clinical notes, semi-structured lab reports, and structured tables.
  • Features dual interfaces for automated integration and user-driven data management.

Main Results:

  • Demonstrates robust performance across diverse healthcare data formats.
  • Achieves efficient conversion of EHR data to FHIR resources.
  • Offers flexible deployment options ensuring regulatory compliance.

Conclusions:

  • LINK-FHIR significantly enhances Health Information Exchange interoperability.
  • The system has the potential to improve operational efficiency in healthcare institutions.
  • Facilitates standardized healthcare data management through advanced LLM capabilities.