Clinical Trial Patient Matching: A Real-Time, Common Data Model and Artificial Intelligence–Driven System for Semiautomated Patient Prescreening in Cancer Clinical Trials
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.
Authors
- M. Cecchini
- Edward Kaftan
- M. Lustberg
- So Yeon Kim (ORCID: https://orcid.org/0000-0003-4102-5880)
- Ian E. Krop (ORCID: https://orcid.org/0000-0002-6380-5944)
- Guannan Gong (ORCID: https://orcid.org/0000-0002-4972-6705)
- Pamela Kunz
- Christina Wiess
- Anne K. Mongiu (ORCID: https://orcid.org/0000-0002-4676-3674)
- Charles J Torre (ORCID: https://orcid.org/0000-0002-0348-7593)
- Lourdes Mendez (ORCID: https://orcid.org/0000-0002-5922-2877)
- Nathalie Wiesendanger
- Will Byron
- Rachel M. Brodeur (ORCID: https://orcid.org/0000-0002-2675-6397)
- Andreas Coppi
- Neal Fischbach
- Cristian Taborda
- Courtney Barganier
- Jessica Liu
- Haley Dunning
- Nate Price
- Patrick Young
- Mariya Rozenblit
- Patricia LoRusso
- Lajos Pusztai
- Wade Schulz
- Sameer Pandya
Institutions
- Yale New Haven Hospital (US)
- Yale Cancer Center (US)
- Yale University (US)
- Yale New Haven Health System (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1200/cci-25-00262
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00