The EHR Vendor Paradox: Why Healthcare's AI Future Depends on Independent Monitoring

How market concentration in EHR systems is accelerating AI adoption—and why that makes independent evaluation more critical than ever

The Power Dynamic Reshaping Healthcare AI

A recent study published in JAMA Network Open reveals a striking reality about artificial intelligence adoption in healthcare: a handful of EHR vendors—particularly Epic—are essentially determining which hospitals adopt AI, how quickly they do so, and which AI tools make it into clinical workflows.

The numbers tell a clear story:

• Hospitals using Epic were significantly more likely to be early adopters of generative AI

• Health systems already using predictive AI from their EHR vendor rapidly adopted generative AI from the same source

• The three largest EHR vendors hold substantial influence over competition among health AI firms

This isn't necessarily a problem. Market leaders often drive innovation, and integration within existing EHR workflows reduces friction for clinicians. The real issue lies in what comes next.

The Governance Gap

Here's where the JAMA study identified a critical vulnerability: the majority of hospitals are deploying AI with incomplete local evaluation and monitoring capabilities.

Think about that for a moment. Healthcare organizations are rapidly implementing algorithms that influence clinical decisions, often because their EHR vendor made those tools conveniently available—but they lack the infrastructure to independently verify those tools are working as intended in their specific patient populations.

The study's authors put it bluntly: "Some hospitals that have rapidly adopted generative AI are not well-equipped to participate in governance of AI."

This creates an uncomfortable dependency. Hospitals are trusting vendors to ensure AI is "effective and safe," but trust isn't a governance framework. It's a risk.

Why Vendor-Only Validation Isn't Enough

EHR vendors and AI developers conduct rigorous testing before deployment. But several critical questions can only be answered with real-world, site-specific monitoring:

• Does this algorithm perform equally well across our patient demographics? Pre-deployment testing often relies on limited datasets that may not represent your population's age distribution, racial composition, or disease prevalence.

• Is performance degrading over time? Model drift is real. Clinical protocols change. Patient populations shift. An algorithm that worked well at launch may perform differently six months later.

• Are clinicians using this tool as intended? Even the best AI can fail if it's implemented in ways that don't match clinical workflows or if alert fatigue leads to override patterns that undermine safety.

• What's happening at the edges? Rare but serious failure modes often only emerge at scale, in real-world conditions that couldn't be fully anticipated during development.
  

These aren't questions you can answer once during procurement. They require continuous monitoring, automated surveillance, and the ability to detect problems before they become patient safety issues.

The ALIGNMT AI Solution

This is precisely why we built ALIGNMT AI—to give healthcare organizations the independent monitoring infrastructure they need in an era of vendor-driven AI adoption.

Real-World Evidence Monitoring at Scale

We work directly with leading EHR providers to embed continuous performance monitoring into clinical workflows. Our platform tracks AI behavior in production environments, generating real-world evidence on:

• Performance metrics across different patient subgroups

• Temporal trends that reveal model drift or degradation

• Usage patterns that identify implementation challenges

• Outcome correlations that validate (or question) AI recommendations

From Vendor Trust to Vendor Verification

Our philosophy is simple: trust, but verify. EHR vendors and AI developers are essential partners in healthcare innovation. But healthcare organizations need independent tools to validate that vendor promises translate to real-world performance.

ALIGNMT AI provides:

• Automated dashboards that surface performance issues without requiring data science teams

• Configurable alerts for significant deviations in AI behavior

• Comparative analytics that benchmark performance across sites and time periods

• Regulatory-grade documentation that supports both internal governance and external reporting
  

Built for the Reality of Healthcare IT

We understand that most healthcare organizations don't have dedicated AI governance teams. That's why our platform integrates with existing EHR infrastructure, works within current IT security frameworks, and delivers insights in formats that clinical and operational leaders can actually use.

You shouldn't need a PhD in machine learning to know whether your AI is working correctly. You should have clear, actionable intelligence that enables evidence-based governance.

The Path Forward

The JAMA study's conclusion is clear: "This dynamic increases the importance of EHR developers and other AI developers acting to ensure the AI that they provide their customers is effective and safe."

We agree completely. And we'd add one more critical stakeholder: healthcare organizations themselves must build the capacity to independently evaluate the tools they deploy.

Market concentration among EHR vendors isn't going away. Their influence over AI adoption will likely grow as generative AI becomes more sophisticated and more deeply integrated into clinical care. That makes independent monitoring not just valuable—it makes it essential.

Taking Control of Your AI Future

If your organization is deploying AI through your EHR vendor—and the data suggests most of you are—ask yourself these questions:

1. Can we detect if this AI's performance changes over time in our patient population?

2. Do we know if the algorithm performs differently for different demographic groups?

3. Could we identify a safety issue before it becomes a pattern of harm?

4. If a regulator or accreditor asks for evidence of AI governance, what would we show them?
   

If you're not confident in your answers, you're not alone. Most healthcare organizations are in the same position, riding a wave of vendor-driven innovation without the infrastructure to steer.

ALIGNMT AI was built to change that equation. We give you the monitoring tools, the analytical frameworks, and the governance infrastructure to move from passive adoption to active oversight.

Because in an era where a handful of vendors shape the AI future of healthcare, independent verification isn't just good practice—it's your organization's responsibility to patients.

Ready to build AI governance capacity that matches your deployment pace? Contact ALIGNMT AI to learn how we partner with healthcare organizations and EHR vendors to enable continuous, real-world monitoring of AI in clinical settings.