Enrollment timelines keep slipping, delaying readouts and burning budget while sites struggle to find eligible patients.
Slow and unpredictable enrollment is one of the most common causes of clinical trial delays and cost overruns. Eligible patients are hard to find, sites over-promise on recruitment, and feasibility decisions are made on stale assumptions. AI approaches use real-world data — EHR, claims, and genomic sources — to size eligible populations, target the right sites, and match patients to protocols faster and more reliably.
How AI helps with patient recruitment in clinical trials
Recruitment AI works from real-world data — de-identified electronic health records, medical claims, labs, and sometimes genomic data — to answer two questions feasibility questionnaires cannot: how many patients actually meet your eligibility criteria, and where are they treated today? Platforms size the eligible population against your specific inclusion/exclusion criteria rather than a diagnosis code alone, then map that population to the health systems and physicians who see those patients. A second family of tools applies natural language processing to clinical records at the site level, screening charts against protocol criteria to surface likely-eligible candidates for site staff to review. The AI identifies candidates; qualified staff still confirm eligibility, approach the patient, and manage consent — no credible platform claims to enroll patients by itself.
What to evaluate before buying recruitment AI
Data coverage is the deciding factor: a platform can only find patients in data it actually has. Ask for patient counts in your specific indication and target geographies before contracting — not global totals. Probe data recency (claims data often lags months), how the platform handles your hardest eligibility criteria (biomarkers and prior-line-of-therapy rules are frequently invisible in structured data), and how identification converts into a compliant site workflow. Privacy governance matters equally: understand whether data is de-identified or federated, who can re-identify a patient (typically only the treating site), and how the platform documents HIPAA and GDPR compliance for your use.
How teams typically get started
A low-risk entry point is a retrospective feasibility exercise: give the vendor a completed or currently-enrolling protocol and compare their eligible-population estimates and site rankings against what actually happened. That tests the data's relevance to your indication before any patient-facing workflow is deployed, and produces an internal evidence base for a larger commitment.
AI Use Cases That Address This Problem
Patient Recruitment & Enrollment
Site Selection & Performance
Frequently asked questions
How does AI find eligible patients for clinical trials?
By querying large sets of de-identified real-world data — EHR, claims, and lab records — against a protocol's eligibility criteria, and in some platforms by using natural language processing to read unstructured chart notes where key details like biomarker status or prior treatments are recorded. The output is candidate identification; screening, consent, and enrollment remain clinical activities performed by site staff.
Does AI-driven recruitment work for rare diseases?
It can, but the constraint is data footprint: if few patients with the condition appear in the platform's data sources, no algorithm can find more. For rare indications, ask vendors to demonstrate actual patient counts in your indication and geography before contracting, and weigh platforms that can reach specialty centers where those patients are concentrated.
Is patient privacy at risk with these platforms?
Reputable platforms work with de-identified or federated data, where patient-level records never leave the treating institution and re-identification is possible only by the patient's own care team. Before buying, verify how de-identification is performed, where data physically resides, and how the vendor documents HIPAA, GDPR, and local-law compliance for your specific workflow.
What should we ask a recruitment AI vendor in a demo?
Ask for eligible-patient counts for your actual protocol criteria (not a simplified version), the data sources and their refresh frequency, how criteria that live in free text are handled, what the site-facing workflow looks like, and for references from sponsors who ran trials in your therapeutic area.
Launched the Verana Research Network on the AAO IRIS Registry to accelerate clinical research
Verana Health and the American Academy of Ophthalmology launched the Verana Research Network, an IRIS Registry initiative in which academic medical centers and ophthalmology practices use IRIS Registry data and Verana's platform to increase clinical trial access, accelerate recruitment, and advance ophthalmic research.