Adaptive FHIR Native AI Governance for Clinical Decision Support: A Modular, Auditable Deployment Framework for Real World Clinical AI

Background Clinical artificial intelligence systems are frequently deployed as static decision support tools that apply a single predictive model and threshold across heterogeneous patients, care settings, and time. In real world use, contextual heterogeneity and asymmetric error costs can produce systematic deployment failures even when constituent models have acceptable retrospective performance. These risks are amplified when health systems lack interoperable mechanisms to govern model selection, document decisions, and support post deployment accountability. Objective To develop and evaluate a FHIR native governance framework that adaptively governs the deployment of pre validated clinical prediction models using standardized clinical context, while generating machine readable provenance and audit artifacts. Methods We implemented a modular governance layer operating on HL7 FHIR R4 resources that treats model deployment as a sequential decision problem. Patient context is derived deterministically from FHIR resources without representation learning. A policy engine selects among a portfolio of frozen predictive models using contextual, cost aware policies that update internal policy state but do not modify model parameters. Exploration based policies are evaluated only in simulation and are not presented as deployment ready without additional safety constraints. For each decision, the framework emits FHIR Provenance and AuditEvent resources capturing the selected model, decision timing, and governance metadata to support traceability and reconstruction. We evaluated feasibility using simulation based experiments on synthetic FHIR data, with an optional local validation hook for restricted access deidentified clinical data. Outcomes included safety weighted error, cumulative policy regret relative to an oracle selector, stability of model selection across contexts, and audit completeness. Results Across fixed-seed simulation scenarios comprising 2,272 sequential deployment decisions, adaptive governance policies reduced cumulative safety-weighted error relative to suboptimal static deployment without retraining predictive models. Under the primary cost specification, the epsilon-greedy governance policy achieved a cumulative safety-weighted cost of 120 (mean 0.211 per decision), compared with 129 (mean 0.227) for the higher-cost static baseline, corresponding to a 7.0% reduction. Adaptive policies converged to oracle-equivalent deployment behavior, with zero final cumulative regret. All deployment decisions generated complete HL7 FHIR AuditEvent and Provenance artifacts, enabling 100% deterministic reconstruction of deployment pathways. Results represent point estimates from deterministic simulations and should be interpreted as evidence of methodological feasibility rather than clinical effectiveness. Conclusions FHIR native infrastructure can operationalize adaptive and auditable governance for clinical AI deployment in a simulation based feasibility setting. Separating governance logic from model development provides a reusable approach for implementing accountable clinical decision support that can be evaluated, monitored, and reviewed using interoperable audit artifacts, with additional empirical validation and safety constraints required prior to clinical use.