Cracking the Hardest Code in Clinical Trials: How AI Unlocks Rare Disease Recruitment

Recruiting for any clinical trial is a challenge. But recruiting for rare disease trials? That’s a whole different ballgame—one where the stakes are higher, the timelines are tighter, and the margin for error is razor thin.

In these trials, every patient truly counts. Yet enrollment obstacles often delay studies, inflate costs, or, worse, cause them to fail entirely. The result? Promising therapies never make it to market, sponsors absorb major financial losses, and patients are left waiting—sometimes without other options. But what if we could flip that script?

Artificial intelligence (AI) isn’t a silver bullet, but when it comes to rare disease recruitment, it might be the closest thing we’ve got. And if AI can work here—in the most difficult corner of the research landscape—it’s a strong sign of what’s possible across the board.

The High Stakes of Rare Disease Trial Recruitment

Rare diseases affect more than 300 million people globally, but each condition may impact only a few hundred or thousand individuals. That means:

• The eligible population for a trial is extremely limited

• Patients are often geographically dispersed

• Diagnoses may be delayed or incorrect

• Standard keyword-based searches miss nuanced clinical indicators

As a result, rare disease trials are among the most likely to struggle—or fail—due to recruitment issues. According to Tufts Center for the Study of Drug Development, 81% of patients screened for rare disease trials are not eligible to enroll, compared to 57% for non-rare diseases. And 56% of rare disease volunteers fail to be randomized, compared to 36% for non-rare disease trials.

These are high-risk studies in every sense:

• Financial risk for sponsors investing millions before a single patient enrolls

• Operational risk for CROs and sites stuck in stalled studies

• Human cost for patients left waiting for treatments they urgently need

Where Traditional Approaches Fall Short

When time is tight and every patient matters, conventional recruitment methods often fall short. These include:

• Manual chart reviews across disparate systems

• Outdated or incomplete registry mining

• Referrals based on informal networks

• Broad outreach campaigns with poor targeting

While these may yield modest results in large-population studies, they’re often too slow, too passive, and too imprecise to succeed in rare disease. You can’t afford to cast a wide net—you need to cast a smart one.

AI Recruitment: Built for the Hard Stuff

This is where AI-driven solutions like BEKplatform make a transformative difference. Unlike systems limited to structured data or rule-based filtering, BEKhealth’s platform applies advanced AI to analyze both structured and unstructured EHR data—surfacing insights that traditional methods miss.

With BEKplatform, research teams can:

• Parse narrative notes for signs of rare diseases—even without a formal diagnosis

• Recognize proxies and patterns across patients with varied terminology

• Apply complex inclusion/exclusion logic across millions of records in seconds

Let’s say you’re running a trial for a rare metabolic disorder that requires:

• A confirmed genetic mutation

• A pattern of abnormal labs over time

• Symptoms documented in free text

• A recent misdiagnosis masking the real condition

That’s a needle in a haystack—but BEKhealth’s AI knows how to look. To see this in action, check out our case study on patient matching.

Lowering Risk, Opening Doors

For rare disease sponsors, this level of speed and precision can be a game-changer. AI-driven recruitment helps:

• Accelerate time-to-first-patient-in

• Prevent site delays and trial failures

• Build confidence in high-risk pipelines

• Extend access to previously missed or misdiagnosed patients

And for the patients? It’s about more than eligibility. It’s about finally being seen.

If It Works Here, It Works Anywhere

If AI can solve the recruitment puzzle for rare disease—arguably the most complex use case in clinical research—imagine what it can do elsewhere:

• Oncology trials with biomarker subgroups

• Cardiovascular studies with overlapping comorbidities

• Neurodegenerative studies that depend on historical symptom patterns

The same underlying capabilities—advanced data processing, context-aware matching, real-time identification—apply across all of them.

A Smarter Path Forward

Clinical research is evolving rapidly. Protocols are more complex. Timelines are tighter. “Good enough” isn’t good enough anymore—especially not in rare disease.

That’s why more sponsors, CROs, and research sites are turning to AI to de-risk recruitment from day one—not just to make studies faster, but to make them possible.

Want to see how BEKplatform identifies the patients others miss? Contact us today to learn how AI recruitment can accelerate even your most challenging trials.