Clinical Trial Patient Matching: A Real-Time, Common Data Model and Artificial Intelligence-Driven System for...

Cancer clinical trial enrollment remains critically low at 5%-7% of adult patients despite exponential growth in available trials. Manual patient-trial matching represents a fundamental bottleneck, whereas current artificial intelligence (AI) and machine learning patient-trial matching systems lack data standardization and compatibility across health systems. We developed and validated a semiautomated clinical trial patient matching (CTPM) tool to improve recruitment efficiency and scalability.

We created a hybrid rules-based and natural language processing (NLP)-based pipeline that automatically screens patients using structured and unstructured electronic health record data standardized to the Observational Medical Outcomes Partnership (OMOP) common data model. CTPM performance was first evaluated on one metastatic colorectal cancer (CRC) trial by comparing CTPM accuracy and efficiency to manual chart review. Following the single-trial validation, we then implemented the system across 29 clinical trials spanning multiple cancer specialties and phases.

For the single CRC trial, CTPM achieved 94% retrospective and 88% prospective accuracy, matching gold standard clinical chart review with 100% sensitivity. Implementation reduced chart review workload 10-fold and screening time by 41% (3.1 to 1.8 minutes per chart) for those patients who did undergo review. Since September 2022, the system has screened 98,348 patients across 29 trials, identifying 825 eligible candidates and facilitating 117 patient enrollments with 9%-37% consent rates.

This AI and NLP tool demonstrates improved efficiency in clinical trial recruitment by enabling research teams to focus on qualified candidates rather than exhaustive chart reviews. The OMOP-based framework supports scalability across health systems, with potential to address enrollment challenges that limit patient access to clinical trials.