@inproceedings{12d16f8e5d604a5b969e7065ed54bcdc,
title = "LLM-Integrated Normalization and Knowledge for FHIR (LINK-FHIR)",
abstract = "Current approaches lack efficient methods to convert diverse healthcare data formats into standardized Fast Healthcare Interoperability Resources (FHIR). LINK-FHIR is a novel system for converting diverse Electronic Health Records into FHIR-compliant resources. The system leverages fine-tuned Large Language Models through a unified pipeline to efficiently process unstructured clinical notes, semi-structured lab reports, and structured tables. LINK-FHIR features dual interfaces that offer automated machine-to-machine integration and an intuitive user interface for data visualization and management. The system offers flexible deployment options to ensure compliance with healthcare security and privacy regulations. Comprehensive evaluation demonstrates LINK-FHIR{\textquoteright}s robust performance across diverse data formats. LINK-FHIR has the potential to enhance Health Information Exchange interoperability significantly, operational efficiency across healthcare institutions.",
keywords = "Automatic Data Processing, Health Information Interoperability, Large Language Models, Machine Learning, Natural Language Processing",
author = "Zhen Hou and Ming Jiang and Hao Liu and Yan Zhuang",
note = "Publisher Copyright: {\textcopyright} 2025 The Authors.; 20th World Congress on Medical and Health Informatics, MEDINFO 2025 ; Conference date: 09-08-2025 Through 13-08-2025",
year = "2025",
month = aug,
day = "7",
doi = "10.3233/SHTI250793",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "17--21",
editor = "Househ, \{Mowafa S.\} and Househ, \{Mowafa S.\} and Tariq, \{Zain Ul Abideen\} and Mahmood Al-Zubaidi and Uzair Shah and Elaine Huesing",
booktitle = "MEDINFO 2025 - Healthcare Smart x Medicine Deep",
}