ArticleFrontiers in digital health2026
Operationalizing clinical artificial intelligence through secure FHIR pipelines: a zero-trust cloud reference architecture for EHR interoperability.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Fast Healthcare Interoperability Resources (FHIR) standardizes clinical exchange but does not establish that a downstream artificial intelligence (AI) use remains authorized, semantically consistent, or accountable. This Hypothesis and Theory article proposes evidence-continuous boundary control: a material data, model, or workflow transition is governed by an applicable policy decision and linked to versioned evidence of its prerequisites, execution, and disposition. The framework arose from continuing healthcare-interoperability research and was formalized through standards analysis, targeted evidence synthesis, and explicit design inference. Documented revision searches distinguish external evidence from architectural decisions. Eight requirements are organized into five functional layers: clinical sources, an interoperability edge, a zero-trust control plane, a segregated cloud data and AI plane, and governance and monitoring. Applications are consumers, not a sixth layer. An illustrative adult readmission-risk workflow connects identity, policy, source versions, features, model release, scoring, presentation, and human action. Its symbolic FHIR R4 evidence linkage includes explicit R5 translation cautions and policy-aware authorization, including consent where applicable. Six propositions test incremental value against a well-run contemporary platform without removing its native safeguards; simulated ablations isolate particular links. Independent reference standards, denominators, support, counterevidence, and inconclusive conditions expose operational and human-factors trade-offs. A bounded synthetic-data companion implementation makes selected exchange controls inspectable; it does not implement the complete clinical-AI architecture or validate its propositions. No empirical architecture evaluation, patient dataset, clinical effectiveness, or independent expert validation is reported. Staged future studies must establish incremental benefit without disproportionate burden or inappropriate reliance.
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