AI governance doesn’t end at deployment. Real risk and impact emerge in production, where oversight is often weakest.
Health systems have made notable progress in formalizing AI governance. Vendor intake processes are more structured, documentation is scrutinized, and multidisciplinary committees evaluate risk before deployment. These efforts reflect a maturing field and a legitimate concern for safety, bias, and compliance.
Yet most of this rigor is concentrated before go-live. Once an AI system is embedded into clinical workflows, systematic oversight often becomes less defined. The most consequential questions about performance and impact tend to emerge in production, but governance frameworks are still disproportionately oriented toward Day 0 decisions.
Pre-deployment review evaluates artifacts: validation studies, regulatory status, security controls, and training data summaries. These materials offer a necessary baseline and help organizations assess whether minimum standards are met before exposure to patients and clinicians.
However, static review cannot capture how a system behaves in the complexity of real-world practice. Variability in documentation patterns, patient populations, and workflow dynamics can meaningfully alter performance. Nor does onboarding review illuminate how clinicians actually use the tool—whether they override it, partially rely on it, or adapt their behavior around it.
For traditional predictive models, post-deployment monitoring rests on relatively established metrics. Binary classifiers can be evaluated against defined outcomes using sensitivity, specificity, and calibration. While implementation is not trivial, the conceptual foundation is stable.
Generative AI systems complicate this model. Tools that draft notes, summarize encounters, or provide open-ended clinical suggestions do not always have a clear ground truth. Quality is contextual, and errors may manifest as omissions or subtle distortions rather than discrete misclassifications. The field lacks consensus on which performance signals are most meaningful, leaving many organizations without a coherent monitoring framework.
Even when monitoring goals are defined, execution is demanding. AI tools are distributed across the enterprise — embedded in the EHR, imaging systems, and external SaaS platforms. Outputs, logs, and access controls differ across vendors and departments.
Meaningful oversight requires linking model outputs to clinical context and downstream outcomes across these silos. This entails integration work that spans data engineering, informatics, and cybersecurity. For many health systems, the operational burden is substantial, and monitoring remains fragmented or episodic as a result.
AI adoption is no longer confined to isolated pilots. Health systems are deploying multiple AI-enabled tools across clinical, operational, and administrative domains — many of which update iteratively or depend on external model providers. Over time, clinician reliance deepens and workflow dependencies solidify. In this context, the absence of structured Day 2 oversight creates meaningful blind spots. Performance drift, uneven effects across patient subpopulations, or subtle workflow distortions may emerge gradually rather than as discrete failures. Without systematic monitoring, such patterns are unlikely to be detected early, even as regulatory expectations increasingly emphasize ongoing accountability.
For CIOs, CMIOs and CAIOs, the implication is clear: governance must extend beyond approval. Monitoring expectations should be defined at the outset, with explicit plans for post-deployment review, clinician feedback loops, and escalation mechanisms. This shift also requires investment in data infrastructure capable of providing cross-system visibility. As AI portfolios expand, oversight cannot remain anchored to pre-deployment artifacts alone. The credibility of institutional AI governance will depend on whether health systems can demonstrate operational awareness of how these systems behave once embedded in everyday care.
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Originally published on the CHARGE blog. Republished here as part of the archive.
CHARGE publishes independently. Grid Health, its incubator, builds an AI operations center health systems use to monitor models, detect drift, and govern AI in production.
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