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Blog
Andy Pollen

The Missing Layer of AI Oversight: Analytics

aiOS, Analytics, Data Management
Table of Contents

Walk into many hospitals today and you’ll see clinical AI in action: flagging suspected findings, prompting earlier interventions and supporting care across a range of specialties. It’s a powerful tool, but in many organizations, its impact is hard to quantitatively measure. 

What’s missing is something more foundational: on-demand visibility to trends that quantifies AI performance and usage impact over time.

Across health systems, there’s growing recognition of a core problem: Leaders can’t clearly see how AI is being used, how it’s performing or what impact it’s having — in real time or at scale. That absence isn’t just a data gap, it’s a governance challenge.

Governance Without Sightlines

Most AI oversight today still relies on point-in-time reporting. Health systems pull usage statistics intermittently, review outcomes in hindsight or depend on anecdotal feedback from clinicians. While these methods offer some directional insight, they’re often too slow, fragmented or shallow.

Meanwhile, leadership is expected to justify investment, ensure ethical use and track clinical impact — all without a complete picture. Governance and oversight, in this context, becomes reactive at best. At worst, it’s performative.

What Real-Time Governance Looks Like

Leading institutions are beginning to move beyond point-in-time metrics. They’re seeking full-lifecycle insight — from deployment and adoption to real-time performance and downstream outcomes. This shift isn’t just about dashboards; it’s about embedding oversight into the operational system itself.

With real-time capabilities into key AI metrics and trends, clinical and operational leaders can finally ask — and answer — the questions that matter:

  • Are the algorithm results being consistently used — and by whom?
  • How engaged are users, and how effectively are they using AI?
  • Is my algorithm’s performance stable over time? 
  • What impact is the tool having on workflows or patient outcomes?

As one clinical leader described it, gaining that level of visibility is “like having a window open” into how your algorithms are actually performing.

Tracking What Matters

To make AI sustainable, health systems need continuous visibility across three critical areas:

  • Performance: Metrics like sensitivity, specificity, PPV and prevalence, tracked longitudinally across sites, service lines and algorithms
  • Usage: Organization, algorithm, and user-level insight into usage, adoption and engagement — who’s interacting with AI, how frequently and how usage evolves 
  • Impact: Downstream indicators such as reduction in wait time for AI results, length of stay and time-to-treatment

These are the metrics that tell you whether AI is doing what it’s supposed to and where adjustments are needed. They enable effective governance, guide strategic decisions and build trust with frontline users.

Meeting the Expectations of Care

Health systems are now expected to show that their AI tools are not only accurate but also ethically deployed, equitably accessed and producing measurable clinical and operational value. Regulatory pressure is growing, and so is demand for ROI. Yet many organizations can only treat AI oversight as an episodic task.

That approach no longer matches the pace or complexity of AI in practice. Governance, therefore, can’t be something that happens before or after the fact. It has to be continuous and embedded in the daily rhythm of care.

Analytics, then, isn’t something on the periphery. It’s a core component of an enterprise clinical AI platform, a systemwide capability that enables continuous oversight and supports operational trust. Without it, scaling AI responsibly — or even proving it’s working — becomes nearly impossible.

Many factors will shape the future of clinical AI, from technological breakthroughs to new regulatory frameworks. But one element is foundational: insight. Oversight doesn’t only begin with policy, it also begins with visibility. That’s something too many systems are still missing, and it’s one that will define the next phase of AI maturity.

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Andy Pollen
Andy Pollen is a former Aidocee.
Andy Pollen
Director, Marketing Communications

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