Sep 2026· International Journal of Medical Informatics· Vol 222, pp.
106716
· 0 citations· 24 references
Medicine
TL;DR
FHIR has successfully catalyzed widespread technical innovation and serves as the foundation for advanced clinical applications, however, a significant translational gap persists and future efforts should expand beyond technical development.
Abstract
Background
The Fast Healthcare Interoperability Resources (FHIR) standard is heralded as a key solution to healthcare's pervasive interoperability challenges, promising to enable a new ecosystem of connected clinical applications. However, a comprehensive synthesis of its real-world implementation landscape is lacking.
Objective
This narrative review aims to map the current domains of FHIR-based clinical applications, assess their implementation maturity, and identify critical trends and translational gaps.
Methods
A systematic search was conducted in Scopus and PubMed for English-language articles published between January 2019 and January 2026. Studies describing concrete FHIR-based tools with empirical findings in clinical or research settings were included. A thematic synthesis was performed, structuring the findings into a functional taxonomy and an implementation maturity framework.
Results
The analysis of 20 studies reveals that FHIR enables six core functional paradigms: (1) Artificial Intelligence and Decision Support, (2) Large-Scale Surveillance and Research, (3) Patient Safety and Care Coordination, (4) Patient Empowerment and Telehealth, (5) Semantic Interoperability and Advanced Modeling, and (6) Data Harmonization. The maturity assessment indicates a vibrant innovation pipeline, with the majority (12/20) of studies at the Proof-of-Concept stage, focused on technical validation. A smaller subset has progressed to clinical Pilots (4/20) or Institutional Integration (4/20), where challenges shift from technical feasibility to organizational adoption, workflow integration, and demonstrating clinical utility.
Conclusion
FHIR has successfully catalyzed widespread technical innovation and serves as the foundation for advanced clinical applications. However, a significant translational gap persists. Future efforts should expand beyond technical development. To bridge the divide between prototype and widespread clinical impact, rigorous studies are needed in three areas: implementation science, user-centered design, and sustainable value demonstration.
The study explores the role of ontology in healthcare by surveying numerous research articles to provide a comprehensive overview of its applications, benefits, and challenges, and identifies prevalent issues, such as limited standardization, difficulty in updating ontologies to reflect the latest medical insights, and...
U. Priyadharshini, R. Vijayan· Frontiers in Artificial Inte...· 0 citations
A review examines how free full-text PubMed literature describes the real-world implementation of AI in healthcare and identifies the technical, organizational, ethical, and social conditions that shape adoption.
Jagoda Pałubska, Oliwer Műller, Zuzanna Rafałowska et al.· International Journal of Inn...· 0 citations
Current evidence supports cautious deployment of LLMs in selected healthcare tasks under structured oversight, and the proposed five-dimensional framework coupled with a three-tier risk model is intended to support researchers and healthcare organisations in assessing readiness and implementing LLM-enabled tools respon...
J. C. Ferreira, Isabel Rosa· Frontiers in Digital Health· 0 citations
Abstract Scientific progress increasingly relies on large, complex biomedical datasets; data management concerns (heterogeneity, volume, sensitivity, and a pervasive lack of standardization) present significant challenges, especially in the context of AI adoption and precision medicine. This fragmented data landscape l...
C. Chute, A. Bahmani, Alex H. Wagner et al.· Journal of Clinical and Tran...· 0 citations
The growing integration of AI in healthcare has increased demands for transparency, trust, and accountability. XAI tools address these needs by providing interpretable insights into AI-generated outcomes. However, there is still a lack of empirically grounded guidance on how to implement XAI tools effectively in health...
A HL7 Fast Healthcare Interoperability Resources (FHIR) system for Mobile Patient Survey (MoPat), which supports multiple versions and is designed to integrate PROMs into various FHIR-based infrastructures, validates the potential of electronic PROM tools to adopt standards like the MII core dataset.
Yannik Warnecke, Dominik Heider, M. Storck· Studies in Health Technology...· 0 citations