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Healthcare AI has moved well beyond experimentation. Health systems are rapidly deploying AI tools for areas such as ambient documentation, clinical evidence summarization, patient engagement, and operational automation, but scaling clinical AI health systems effectively requires far more than simply selecting the right tools.
The Data Behind Scaling Clinical AI Health Systems
Wolters Kluwer’s 2026 Future Ready Healthcare Report found that 74% of doctors and 70% of nurses use AI tools at least once per week. Additionally, the share of clinicians using AI multiple times per day tripled for doctors, from 10% in 2025 to 38% in 2026, and doubled for nurses, from 16% to 32%, compared to the previous year.
Why Enthusiasm Alone Isn’t Enough
Clinician enthusiasm highlights a significant opportunity to improve workflows. But as discussed at a panel conversation at the 2026 Scottsdale Institute Annual Conference, realizing it at scale and in a governed manner requires focusing on key aspects of care delivery and business operations, such as safety, equity, timeliness, affordability, appropriateness, and outcomes.
Evaluating Workflows First Within Scaling Clinical AI Health Systems
The health systems seeing the strongest AI outcomes share a common discipline: they evaluate underlying workflows before layering technology on top. AI can dramatically accelerate a well-designed process, helping surface insights faster, reduce documentation burden, and free clinicians to focus on patient care. But no tool, however sophisticated, can compensate for a workflow that wasn’t serving clinicians in the first place.
The Scale of the Shadow AI Problem
Wolters Kluwer’s Shadow AI survey found 57% of healthcare providers and administrators had encountered an unauthorized AI tool in the workplace. Of those who reported using them, 50% did so for a faster workflow, and 1 in 3 cited either a lack of approved tools or that the approved tools lacked the desired functionality.
Building Governance That Moves at the Speed of Innovation
Governance has become one of the most consequential factors in successfully scaling AI within health systems. A strong governance framework includes clear criteria for tool evaluation, explicit policies on approved and unapproved use, and regular review cycles that keep pace with the technology landscape.
Why Policy Awareness Remains a Gap
Embedding those policies within EHR workflows, rather than relying solely on enterprise communications, helps ensure they’re accessible for clinicians and improves policy awareness. This communication is essential, the Future Ready Healthcare Report found 44% of clinicians weren’t aware of AI policies in their organization, and 29% weren’t sure if they existed.
Why Effective Governance Drives Success in Scaling Clinical AI Health Systems
“Effective governance is crucial. Success is more likely in organizations that have well-defined strategic objectives and a tested framework for deploying, measuring, pivoting, and scaling,” said Peter Bonis, MD, CMO of Wolters Kluwer Health.
How Governance Empowers Rather Than Restricts
Governance done well doesn’t restrict innovation, it focuses it and empowers care teams. Governance can also include streamlining evaluation pathways, upskilling clinical teams on AI literacy, and creating structured channels that connect frontline needs directly to senior decision-makers to continue adjusting policy as innovation and workflow needs evolve.
Defining Success Metrics Before Deployment
One of the most practical aspects of scaling effectively is to define success metrics before starting, using existing dashboards if available. AI should demonstrate value in the actual workflow, with non-negotiable clinical measures, such as decision quality, time to appropriate care, avoidable variation, adverse events, clinician cognitive burden, or patient experience.
Why Generic Benchmarks Fall Short
It’s also critical to evaluate the tool against its intended clinical use, not a generic benchmark. Evaluating AI against these specific metrics creates a direct line between technology investment and operational outcomes, a distinction that matters when justifying continued AI spending to health system leadership.
What This Means for Health Systems Going Forward
The health systems making the most meaningful progress share a common orientation: they’ve moved from thinking about AI adoption to thinking about AI accountability. That means measuring outcomes rigorously, scaling what works, and building cross-functional alignment between clinical, operational, and technology leadership. Given the persistent gap in policy awareness Wolters Kluwer identified, health systems pursuing scaling clinical AI health systems strategies may need to prioritize embedding governance directly into clinical workflows rather than relying on standalone policy communications that clinicians may never see.
What to Watch Going Forward
As health systems continue navigating the tension between rapid clinician AI adoption and the governance structures needed to manage it responsibly, Wolters Kluwer’s upcoming three-part webinar series, “Making AI Meaningful: Clinical Strategies for Scalable Healthcare Transformation,” running from August 12 through September 2, may offer additional detail on workflow integration, organizational transformation, and frontline specialty perspectives. Given the sharp rise in clinicians using AI multiple times daily, from roughly one in ten to nearly four in ten doctors in a single year, health system leaders may find the pace of adoption outstripping their current governance capacity unless they act quickly to close that gap.
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