Enhancing Healthcare Data Interoperability with AI-Driven Synthetic Datasets Using FHIR Standards

Abstract

This article addresses how one can combine AI-generated synthetic medical datasets as well as FHIR standard semantic standards and APIs to better enable cross-system interoperability between health care providers. In order to create data sets with the desired level of analytical utility, the author have as a study objective to demonstrate how the generation of synthetics in the context of FHIR resource and transport via the R5 mechanisms of profiles, subscriptions and asynchronous export to NDJSON removes both legal and technical barriers to accessing clinical data. Because data-sharing is complicated by the risk of regulatory noncompliance; a significant amount of clinical data is contained in unstructured documents; and clinical data grows at an exponential rate, the need for this type of solution has increased. This work is innovative in that it brings together three families of generative models: sequential LLM-like models, GANs and diffusion networks. Additionally, the author use a direct mapping pipeline from generative models to FHIR resources and validate profile IG and automated REST tests using built-in systems. To train the generative models on rare nosologies, and to perform load-testing, the author describe an architecture that enables private, secure access to multiple, representative cohorts, which are used to generate the synthetic data. Key Findings: (1) The use of synthetics inside the FHIR shell ensures compatibility with the current infrastructure while reducing legal barriers. (2) They also allow risk-free testing of extreme scenarios and (3) they also reduce sample bias. A rational and practical strategy for accelerating the implementation of analytics in clinics would include a strategy of generating synthetics in a continuous integration/continuous delivery (CI/CD) environment and (2) implementing mandatory validation through profile management. The author believe that the article will provide valuable assistance to medical AI developers, integration solution architects, IT service managers, and regulatory analysts.