The role of applied AI and FHIR-based Apis in building interoperable healthcare data ecosystems
Engineering Lead at Amazon.
Review
Open Access Research Journal of Science and Technology, 2026, 17(01), 033-043.
Article DOI: 10.53022/oarjst.2026.17.1.0059
Publication history:
Received on 28 April 2026; revised on 05 June 2026; accepted on 08 June 2026
Abstract:
Healthcare generates a ton of clinical data, but disjointed info systems, inconsistent standards, and limited interoperability still hold things back. This study takes a look at how AI and FHIR-based APIs are coming together to form an emerging framework for sharing health data more effectively. It uses a mixed-methods approach to check out health system structures, provider survey replies, AI tools linked with FHIR, and implementation stories from different medical settings. The findings show that FHIR APIs help make the data consistent and easy to work with for AI, which is great for getting those systems up and running across health orgs. Places using AI in a FHIR-friendly way saw better data quality, improved clinical support, smoother reconciliations, and more efficient workflows. Still, big challenges pop up too - think complex data governance, varying levels of org preparedness, basic infra issues, gaps in compatibility, biases in algorithms, and unclear regulations. Trust from clinicians and high data quality are also super important factors in making these FHIR-AI setups work well. The article suggests a combined approach for safely using AI. It points out main areas for further study: federated learning, fair AI use in health, tracking results over time, and flexible rules. These findings fit in with other proofs that AI and FHIR work great together to boost data-driven health care systems.
Keywords:
Artificial Intelligence; FHIR; Healthcare Interoperability; Electronic Health Records; Clinical Decision Support; Health Information Exchange; Data Governance; Federated Learning
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Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0