{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:23:30Z","timestamp":1786116210177,"version":"build-2736575974"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":15,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2024 Conquer Cancer\u2014Janssen Research & Development LLC Career Development Award in Health Equity","award":["AWD00003905"],"award-info":[{"award-number":["AWD00003905"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Participation in cancer clinical trials is low in community oncology settings, partly because institution-specific trial information is fragmented. We evaluated feasibility of embedding curated trial content in an AI-enabled knowledge management application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>At a regional community oncology network, coordinators and disease teams compiled actively recruiting trials. Core elements (title, conditions, biomarkers, stage\/line, and recruiting status) were structured for point-of-care display and uploaded. AI-assisted extraction generated protocol summaries and eligibility elements, which underwent systematic human validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fifty-three trials across 10 disease groups were embedded and validated; 91% were recruiting. Trials covered 28 cancer types; 30% were biomarker-specific and most enrolled advanced\/metastatic disease. Initial configuration took 2-4 weeks per disease group using existing personnel, without added staffing or electronic health record (EHR) build.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Embedding institution-specific trial content within an AI-enabled knowledge application is feasible in community oncology using existing clinical and research infrastructure, establishing a prerequisite for future usability and implementation studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag140","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T13:38:59Z","timestamp":1784727539000},"source":"Crossref","is-referenced-by-count":0,"title":["Development of a clinical trial knowledge management application for community oncology"],"prefix":"10.1093","volume":"9","author":[{"given":"Elizabeth","family":"O\u2019Brien","sequence":"first","affiliation":[{"name":"Cancer Institute, Hartford HealthCare , Hartford, CT 06106,","place":["United States"]},{"name":"Frank H. 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