Why Overworked Teams Lead to Enrollment Delays and How AI Can Help

 

The clinical research industry talks extensively about enrollment challenges—site selection, patient engagement, regulatory hurdles. But there’s one factor that rarely gets the attention it deserves: the human breaking point of research teams trying to do more with less.

The Burnout-Delay Connection

Nearly 80% of all trials fail to meet their original enrollment deadline and 55% of trials are terminated for failure to achieve full enrollment, according to research published in PMC. While we often blame “hard-to-find patients” or “complex protocols,” the reality is more human: overworked research coordinators are making split-second decisions about which patients to pursue, and they’re choosing the path of least resistance.

When your CRC is managing five active studies, covering for a colleague on leave, and fielding constant sponsor calls, that borderline-eligible patient in the EMR becomes a “maybe later” instead of a “let’s investigate further.” Those “maybe laters” accumulate into enrollment gaps that stretch timelines and inflate budgets.

The Hidden Triage Decisions

Research coordinators develop informal triage systems under pressure. They scan for the most obvious matches—the patients whose eligibility practically jumps off the page. Meanwhile, patients with complex medical histories or ambiguous documentation get deprioritized, even when they might be excellent candidates with deeper chart review.

This isn’t incompetence—it’s survival. When teams are stretched thin, they optimize for speed over thoroughness. The problem is that the “obvious” patients often represent only 30-40% of actually eligible candidates in most EMR databases.

Why Adding Staff Isn’t the Answer

The instinct is to hire more coordinators, but that creates new problems. New staff need months to become proficient at protocol interpretation. They make different judgment calls than experienced coordinators, creating inconsistency. And by the time they’re fully productive, the enrollment crisis has often passed—or the study has been terminated.

More fundamentally, adding humans to a fundamentally inefficient process just scales the inefficiency. Manual chart review will always be a bottleneck, regardless of how many people you assign to it.

The AI Solution: Eliminating Human Bottlenecks

This is where AI transforms the equation entirely. Rather than making humans work faster, AI removes humans from the bottleneck altogether.

BEKplatform processes the entire patient database continuously, not just when someone has time to look. It doesn’t get overwhelmed by complex medical histories or multiple comorbidities. It doesn’t develop unconscious biases about which types of patients are “easier” to work with. It evaluates every patient with the same thoroughness, every time.

The Clinical Language Advantage: Think of it like trying to pick a specific star out of the galaxy—except the galaxy is made up of thousands of pages of medical notes written in medical terminology. BEKplatform combines advanced natural language processing (NLP) with large language models (LLMs) to understand clinical context in ways that traditional keyword systems can’t. When a physician writes “patient reports intermittent dyspnea on exertion, likely related to deconditioning rather than cardiac etiology,” the system’s NLP engine extracts the clinical entities while the LLM understands the diagnostic reasoning and relationships—not just isolated keywords.

From Reactive to Predictive: Instead of waiting for coordinators to manually discover eligible patients, the system proactively identifies and ranks candidates based on protocol fit. Research teams shift from “hunting for patients” to “evaluating pre-qualified candidates.”

Complete Workflow Integration: But patient identification is only half the equation. BEKhealth partners with Delfa, the AI-powered enrollment platform, to create seamless end-to-end workflows. Once BEKplatform identifies qualified candidates, Delfa’s automated engagement handles outreach, pre-screening conversations, and appointment scheduling through voice, chat, and text. This integration means research teams can move from patient identification to scheduled appointments without manual handoffs—turning what used to be weeks of back-and-forth into automated workflows that work around the clock.

The Organizational Impact

Sites using BEKplatform report finding qualified patients in days rather than months, with each month of delay costing drug discovery companies $8 million in revenue according to research from PMC. But the organizational benefits go deeper than speed:

• Reduced Coordinator Burnout: When AI handles comprehensive screening, coordinators focus on patient interaction and study management—the work they trained for and find rewarding.
• Consistent Quality: AI doesn’t have “off days” or make different decisions when stressed. Every patient gets evaluated with the same rigor.
• Scalable Growth: Sites can take on more studies without proportionally increasing staff, improving economics and sponsor relationships. With integrated AI workflows handling both identification and initial engagement, research teams can manage larger patient pipelines with the same core staff.

The Competitive Reality

The research sites thriving in today’s environment aren’t just working harder—they’re working fundamentally differently. They’ve moved beyond manual processes that don’t scale and embraced AI that amplifies human expertise rather than replacing it.

Sites still using manual chart review are essentially competing with horse-drawn carriages in a highway race. The technology exists to eliminate the fundamental bottlenecks that create enrollment delays. The question is how quickly research organizations will adopt it.

Making the Transition

The shift from manual to AI-powered patient identification isn’t just a technology upgrade—it’s an operational transformation that requires rethinking how research teams spend their time and energy. But for organizations willing to make that leap, the results speak for themselves: 10x more patients identified, 2x faster enrollment goals, and research teams who can focus on what they do best—conducting excellent clinical research.