Autonomous Generative Analytics for FHIR Networks
Keywords:
FHIR-Based Healthcare Analytics, Generative Adversarial Networks, Graph Neural Networks, Clinical Decision Support, Healthcare Data Interoperability, FHIR Data Integration, AI in Healthcare Analytics, Clinical Inference Engines, Healthcare Data Standardization, Predictive Clinical Modeling, Healthcare Graph Analytics, AI-Driven Diagnostics, Clinical Data Normalization, Healthcare AI Frameworks, Medical Data Networks, Quality Assessment in AI, Synthetic Clinical Data Generation, Interoperable Health Systems, Data-Driven Clinical Insights, Advanced Healthcare Analytics.Abstract
The rapid growth of interoperable healthcare data has outpaced our ability to put it to analytical use. While FHIR (Fast Healthcare Interoperability Resources) has enabled substantial progress in data standardization and sharing, most existing research stops there—treating FHIR purely as a data-exchange standard rather than a foundation for analytics. This study addresses that gap by proposing a framework that integrates Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) directly with FHIR resources, enabling analytics to operate natively on interoperable clinical data.
We introduce a two-layer architecture. The data layer normalizes diverse FHIR resources—whether in standard form or adapted for specific use cases—into representations suitable for downstream modeling. The compute layer applies generative and graph-based analytics methods to these normalized datasets, allowing inference engines to be built simply by assembling the clinical data required for a given task. This design not only supports predictive inference but also extends naturally to two further capabilities: assessing the quality and safety of model outputs, and generating synthetic data via generative models.
To date, healthcare research has concentrated heavily on data interoperability, leaving the analytics layer comparatively underdeveloped relative to other domains. We demonstrate the framework's potential through a focal use case—clinical decision support—implementing a GCN (Graph Convolutional Network)-based inference engine alongside a quality-assessment module that evaluates the accuracy of the resulting decisions.
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