How AI Can Help Solve the Top 3 Challenges in Modern Patient Recruitment

Recruiting patients for clinical trials has always been one of the hardest parts of clinical research. And despite advances in technology and outreach, recruitment challenges are only getting more complex. Today, sponsors and research teams are under pressure to move faster, meet tighter criteria, and deliver more representative populations. The traditional toolkit—flyers, physician referrals, generic ads—isn’t enough anymore.

Artificial Intelligence (AI) is becoming a key part of the solution, helping researchers meet three major challenges head-on:

1. Building awareness and engagement with potential participants
2. Finding patients who meet strict eligibility criteria
3. Enrolling more diverse, representative populations

Let’s take a closer look at how AI is supporting each of these areas—and why more research teams are turning to data-driven approaches to make recruitment more effective and sustainable.

First Hurdle: People Don’t Know These Trials Exist

Clinical trials don’t fail because there’s a lack of people who could participate. They fail because those people don’t know the trial exists, or don’t understand why it might be relevant to them. Traditional outreach methods tend to rely on:

• Physician referrals (which can be limited by time or awareness)
• Broad advertising (which is often inefficient and expensive)
• Site-level outreach (which varies dramatically in consistency and effectiveness)

AI can help reframe how outreach is done—making it more proactive, personalized, and timely using:

• Real-time patient identification: AI tools like the BEKplatform can search electronic medical records (EMRs) to identify individuals who match specific study criteria.
• Tailored outreach: Once candidates are identified, AI can support customized messaging based on individual patient data—like diagnosis, demographics, and health history.
• Operational follow-through: AI can also help with automated outreach, prescreening, appointment setting, and site coordination.

This end-to-end approach increases not just awareness but conversion. In fact, BEKhealth and Areti Health report a 63% conversion rate with their integrated approach—meaning more than half of the people contacted go on to engage with the study.

The Matching Problem: Strict Criteria, Narrow Populations

Many trials today have narrow inclusion and exclusion criteria. Whether it’s a specific biomarker, comorbidity, or previous treatment experience, finding the right match can be time-consuming—and expensive—if done manually.

This is where AI excels. Rather than relying on simple keyword searches or waiting for referrals, AI systems can:

• Parse structured and unstructured EMR data (like lab results and clinician notes)
• Spot patterns across multi-site health systems
• Update matches in real time as patient records change

For example, one health system using such an AI-powered approach was able to stand up an EPIC-compatible patient-matching system across multiple sites in just two weeks. That system immediately flagged thousands of potentially eligible patients for complex trials that previously struggled to recruit.

These gains aren’t just operational—they’re strategic. With better data and faster matching, research teams can shorten recruitment timelines and reduce site burden without compromising on precision.

Diversity Isn’t Optional—It’s Essential

If a clinical trial doesn’t reflect the population it’s intended to serve, the data doesn’t tell the full story. That’s why regulators, sponsors, and advocacy groups are pushing harder than ever for inclusive enrollment.

Traditional methods often fail to reach (or retain) diverse populations. Here’s what AI can do differently:

• Spot disparities early: AI can analyze demographic trends across EMR data to identify where gaps in enrollment might occur.
• Target outreach: AI-powered tools can prioritize engagement with underrepresented groups based on geography, language preference, or social determinants of health.
• Support retention: Systems like Areti Health’s AI Coordinator can track follow-ups and ensure that outreach is consistent across all populations—not just the easiest to reach.

Putting It All Together: A Smarter Recruitment Strategy

When AI is thoughtfully applied to recruitment, it creates a ripple effect:

• Better patient matching leads to faster recruitment
• Personalized engagement leads to higher conversion
• Broader outreach leads to more diverse, inclusive trials

Importantly, AI doesn’t replace site teams or recruitment specialists—it supports them. By automating manual tasks, surfacing insights from EMRs, and guiding communication strategies, AI gives research teams more time to do what they do best: engage with patients and move trials forward.

This kind of approach combines powerful data tools with proven partnerships to help researchers meet today’s recruitment demands—without overburdening teams or missing the mark on diversity and precision.

What to Look for in an AI Recruitment Solution

Not all AI platforms are built the same. If you’re evaluating options for supporting recruitment with AI, here are a few things to keep in mind:

• Can it integrate with your EHR system and workflows?
• Does it support structured and unstructured data?
• Can it scale across multiple sites or therapeutic areas?
• Does it help address both speed and inclusivity?

The right tool should be flexible enough to meet your study’s specific needs, but robust enough to grow with your pipeline.

Final Thought: A Smarter Way Forward

Recruitment may never be “easy”—but it can be smarter, faster, and more equitable. With AI, research teams can rethink how they identify, reach, and retain participants. And with tailored solutions like BEKplatform, they don’t have to do it alone.

If you’re ready to explore what’s possible with AI-supported recruitment, the first step is simple: take a look at your current challenges, and start a conversation about where this technology best fits in your study workflows.