Streamlining Patient Recruitment With AI–Driven Site Identification

Effective patient recruitment and site identification are critical to the success of clinical trials, yet remain persistent challenges—especially for conditions like chronic induced corneal pain (CICP) that lack diagnostic codes and have complex patient journeys. This study highlights how Verana Health, leveraging its exclusive access to the American Academy of Ophthalmology’s IRIS® Registry and advanced AI techniques, significantly improved recruitment efficiency for a phase 2 clinical trial targeting CICP after laser vision correction.

By extracting relevant inclusion criteria from unstructured EHR notes using supervised natural language processing and machine learning, Verana Health enabled precise patient prescreening across high-volume ophthalmology practices. Through its secure, HITRUST-certified Verana Trial Connect platform, curated candidate lists were delivered directly to clinicians, dramatically reducing the manual burden of patient identification. Notably, one practice narrowed a pool of over 200,000 potential patients to just 12,000 and successfully identified and enrolled eligible participants. This approach also surfaced previously overlooked high-potential sites. Ultimately, 12 trial sites used the platform, reviewing 791 candidates. This RWD-driven, AI-enabled recruitment model not only increased trial efficiency but also laid the groundwork for future scalability, showcasing a transformative strategy for clinical development in hard-to-recruit populations.