Patient recruitment is the single largest bottleneck in clinical trial timelines. Sites screen hundreds of EHR records manually per eligible patient. An AI matching engine reads unstructured clinical notes, lab results, and imaging reports against trial eligibility criteria and surfaces qualified candidates in seconds.
Clinical trial recruitment accounts for 30–40% of total trial duration and is the primary reason trials run over budget. The bottleneck is not patient availability — it is identification. Site coordinators manually review hundreds of EHR records against complex, multi-criteria eligibility checklists, a process that takes days per cohort and misses candidates buried in unstructured clinical notes, pathology reports, and imaging summaries that standard SQL queries cannot reach.
TrialGPT (NIH National Cancer Institute, 2024) demonstrated that LLMs reading free-text clinical notes against trial eligibility criteria achieve matching accuracy that exceeds trained site coordinators, at a fraction of the time. An enterprise deployment integrates with the EHR system via FHIR API, processes structured and unstructured patient data simultaneously, applies the full eligibility criterion set in a single pass, and ranks candidates by match confidence with source citations for every criterion matched or excluded.
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