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Blog
Andy Pollen

Embedded Insights: The Future of Clinical AI Analytics

Adoption, aiOS, Data Management
Table of Contents

There’s a strange irony in healthcare today: while AI tools are becoming more advanced and widely deployed, the environments in which they operate still lack the visibility to fully understand what those tools are doing — and whether they’re delivering value.

Clinical AI may be active across hospitals, service lines and workflows, but in too many health systems, adoption and performance data remain fragmented, delayed or entirely opaque. As a result, executives and clinical leaders can’t always act on insights, and health systems are left without a clear line of sight into the tools they’re expected to govern.

The Power of a Unified Platform: Breadth, Depth and Timeliness

Understanding the impact of clinical AI needs to be continuous, while also offering a retrospective look that results in understanding trends and areas of opportunity. That requires continuous visibility into three core areas:

  • Systemwide Performance: How AI is functioning across time, use cases and service lines
  • Usage and Adoption: Who’s using it, how and where gaps exist
  • Impact on Quality: What clinical and operational value is being delivered

Yet in most organizations today, these data points are still being pulled manually – and too late to inform meaningful action. The result is an oversight and governance model that may check a compliance box but does little to support clinical integration or scale.

A Turning Point for Governance

Recognizing this gap, many health systems attempted to build oversight tools themselves. Even those with strong internal teams found the process complex, resource-heavy and ultimately unsustainable.

“We tried to do this work internally with our own data science and AI teams — and it was extremely painful,” said Fernando Collado-Mesa, MD, FSBI, Associate Vice Chair, AI Research & Ethical Use at the University of Miami Health System.

He’s not alone. Few health systems have the architecture or resources to continuously track performance, adoption and impact across an entire AI portfolio, and yet that’s exactly what governance now requires.

That challenge, echoed by partners and frontline leaders, was a major catalyst in our development of Aidoc Analytics: a built-in self-serve analytics layer designed to give health systems the operational clarity needed to govern AI effectively and at scale.

The Turning Point: Platform-Enabled Oversight

Other solutions offer limited ability to integrate smoothly — marketplaces consist of disparate solutions, point solutions are fragmented and offer no unified governance and self-developed solutions are often complex and labor intensive. 

With the aiOS™ platform you get a connected experience that wasn’t designed just for analytics, it was built to unify deployment, integration and governance across the enterprise. In doing so, it unlocked a critical capability: continuous, embedded oversight — not as an add-on, but as part of the platform itself.

Aidoc Analytics, the insights layer within aiOSTM, turns AI performance, usage, and downstream impact from a black box into a transparent, measurable system of record. It enables health systems to:

  • Govern through a unified hub, tracking usage, performance and impact — without delays or manual effort
  • Track algorithm performance with longitudinal metrics like sensitivity, specificity, PPV and prevalence
  • Monitor adoption and engagement, from high-level usage trends to individual behavior and alert response
  • Demonstrate value, with data on workflow efficiencies and downstream clinical impact
  • Identify optimization opportunities, based on real-world utilization patterns
  • Enable governance at scale, with self-serve access to the metrics that matter

By embedding analytics directly into the infrastructure, Aidoc moves beyond one-off reports and manual pulls to deliver always-on intelligence — actionable, scalable and built for operational use.

“Being able to show the data to our leadership and radiologists boosts confidence and helps embed AI into clinical practice,” said Thiago Braga, MD, Assistant Professor of Clinical Radiology and Officer for Imaging Informatics at UM/JMH Radiology. “The performance tab gave us a whole new dimension — now we can proactively manage algorithm quality across the enterprise.”

Governance by Design

The era of siloed dashboards and post-hoc audits is ending. Health systems are being asked to prove what’s working, intervene when it isn’t and connect AI to clinical impact — not periodically, but continuously.

That level of governance can’t be patched together. It has to be built in. Aidoc Analytics is a step toward that future, built not as a bolt-on, but as part of the platform itself.

See for Yourself

Connect with our team to see how Aidoc Analytics can support your AI governance needs.

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Andy Pollen
Andy Pollen is an experienced healthcare communicator and strategist who currently serves as the Director of Marketing Communications for Aidoc. Previously, he was the global marketing communications lead for critical care solutions within 3M Health Care's Medical Solutions Division, now Solventum. Pollen has also held communications positions with the University of Minnesota Academic Health Center, Indiana University Health and several business functions within Eli Lilly and Company through Borshoff, a creative services agency. He earned a bachelor’s degree in public relations and journalism from Ball State University and holds a master’s degree in business administration from Anderson University.
Andy Pollen
Director, Marketing Communications

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