Health systems have moved past the exploring phase of artificial intelligence. Today, AI is fully embedded into clinical workflows, particularly for documentation, imaging, operational forecasting and population health. However, implementation is just the beginning; the real work lies in continuous oversight.
Independent analyses of AI adoption and enterprise IT consistently show that organizations are more likely to scale AI and sustain impact when leaders define governance and accountability upfront instead of trying to retrofit them after deployment. That lesson matters in health care, where the stakes are higher and the “go-live” moment is often when risk truly begins to surface.
Unlike traditional technology, many AI tools are designed to learn and adapt over time. That makes post-deployment monitoring a critical part of your AI governance plan.
Through the launch of our first-ever Health Care AI Accreditation, URAC identified three pillars for success: risk management, operations and infrastructure and performance monitoring and improvement. We believe the real test of an AI tool is how it behaves with real patients in real workflows. For health system leaders, here are the areas that require ongoing attention after go-live.
1. Clinical outcomes and patient safety
The first question is simple: Is this tool making care better, and for whom?
After deployment, health systems should track:
- Clinical accuracy and error patterns, such as false positives/negatives, missed diagnoses or inappropriate recommendations.
- Outcome trends, such as changes in readmissions, complications, time-to-diagnosis or treatment and adverse events tied to AI-supported decisions.
- Near-misses and overrides. When clinicians disagree with the AI, why were they right or wrong?
URAC’s accreditation frames clinical AI more like a pharmaceutical drug than a gadget: there should be pre-deployment testing and “post-marketing” surveillance to catch problems that only appear at scale. That mindset pushes organizations to conduct ongoing, structured quality review.
2. Model behavior: drift, bias and hallucinations
Given AI’s ability to learn and adapt over time, leaders should expect and monitor for three specific risks:
- Drift: When the model’s outputs change because the underlying data or environment has shifted.
- Bias: Performance differences across race, language, geography, payer type or care setting.
- Hallucinations: Confident, plausible but false outputs, especially in generative or summarization tools.
In developing our standards, we explicitly called out the need to watch for bias, drift and hallucinations and then build auditable processes around them. That means defining acceptable performance thresholds, creating dashboards for ongoing monitoring and having rules when errors are detected.
3. Privacy, security and secondary use of data
Many of the most popular tools today are ambient documentation and workflow assistants that sit in the exam room with patients and clinicians.
Post-deployment, health systems should:
- Validate where data is stored, how long it is retained and who can access it.
- Confirm vendors are not using clinical conversations to fuel unrelated commercial models.
- Regularly audit for inappropriate data sharing, re-identification risk and HIPAA violations.
Patient trust is vital in any health care setting and building and maintaining that trust should be a part of your AI framework.
4. Human factors: training, over-reliance and workload
AI isn’t trustworthy on its own; people must be ethical and trustworthy in how they use it. That’s why monitoring human factors is as important as monitoring the code.
Health systems should track:
- Training and competency: Are clinicians trained on indications, limitations and failure modes before they’re expected to rely on the tool?
- Over-reliance: Do clinicians accept AI recommendations without critical thinking (automation bias), or are they appropriately skeptical?
- Workload and burnout: Have documentation time, inbox burden or after-hours work improved, or have new alerts and clicks offset the gains?
5. Governance, vendors and independent oversight
Finally, leaders should monitor the system around the system: governance structures and vendor performance.
URAC takes these expert principles into auditable steps, including clinical oversight committee with documented meetings, clear responsibilities and clear evidence that it reviews and acts on AI performance.
On the vendor side, organizations need to watch:
- Whether vendors are meeting uptime, support and update obligations.
- How they communicate known issues, patches and model changes.
- Whether they are aligned with independent third-party standards, such as AI accreditation programs designed to validate safe, ethical and responsible use.
Accreditation provides independent, third-party validation that health systems have established clear standards and apply them consistently in practice. In health care AI, that validation must extend well beyond go-live through continuous monitoring and meaningful oversight to ensure tools remain safe, effective and aligned with clinical realities. When organizations maintain accountability across safety, performance, privacy and human factors, and can demonstrate that those controls function in real workflows, patients and partners feel more confident and AI can advance responsibly as part of care delivery.
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