Enhancing clinical trial site efficiency and unlocking patient access to clinical trials using technology-enabled pre-screening.

e23122 Background: Despite significant advancements in cancer therapies, only 7% of adult cancer patients participate in clinical trials, according to the National Cancer Institute. A major contributing factor is the inconsistency in methods for participant identification and follow-up. Effective and consistent pre-screening is critical for expanding access to clinical trials, particularly in oncology where timely identification and treatment can be life-altering. Trial Library addresses this challenge by combining expert-led pre-screening with a proprietary platform that connects community-based providers to trial sponsors. This approach ensures timely identification and participation of eligible patients in community-based clinical settings. Methods: Three Clinical Data Coordinators (CDCs) with expertise in the disease indication and experience at clinical trial sites were assigned to pre-screen patients for a phase 2 biomarker-selected non-small cell lung cancer study across three community oncology settings. Using a systematic, phased approach, the CDCs performed manual chart reviews according to established standard operating procedures. In the first phase, patients were screened based on key criteria predetermined by the clinical team. Those who met these criteria were further reviewed in the second phase. The final phase involved ongoing monitoring to track any changes in patients' medical histories that could affect their trial eligibility. Results: The CDCs reviewed a total of 302 patient charts using OncoEMR over a two-week period in August 2024. In Phase 1, 145 patients (48%) met the initial eligibility criteria. The average review time was 45.6 seconds per patient in Phase 1 and 1.33 minutes in Phase 2, with an overall average of 57 seconds per chart. These metrics were consistent across all three CDCs involved. Currently, 62 patients (20.5%) are being monitored for any progression or changes in their medical history that may impact their trial eligibility. Trial Library plans to extend this approach to other oncology studies, aiming to refine and optimize its methodology for diverse clinical contexts. Conclusions: Trial Library's phased approach demonstrated greater efficiency in identifying potential candidates for clinical trials compared to the industry standard. Future integration of AI-assisted pre-screening with Trial Library’s platform could further enhance the speed and accuracy of patient identification. Moving forward, Trial Library plans to extend this approach to other oncology studies, aiming to refine and optimize its methodology for diverse clinical contexts.