P1.16-14 Effects of an Artificial Intelligence (AI) System on Clinical Trial Enrollment in Lung Cancer

Leventakos, Konstantinos; Helgeson, J.; Mansfield, Aaron S.; Deering, Erin; Schwecke, Anna J.; Adjei, Alex A.; Molina, Julian R.; Hocum, Craig; Parikh, Kaushal; Halfdanarson, Thorvardur R.; Marks, Randolph S.; Bleeker, T.; Pomerleau, K.; Coverdill, Sadie; Rammage, Melissa; Preininger, Anita M.; Jackson, Gretchen Purcell; Haddad, Tufia C.

doi:10.1016/j.jtho.2019.08.1240

Cited by 6 publications

(2 citation statements)

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“…32 One site increased lung cancer clinical trial enrollment by 58% when they implemented artificial intelligence-based patient matching. 33 Software-based screening, although effective, also typically comes with costs associated with licensing or technical support; improvements can be made without technology simply by standardizing manual processes. 34 One public hospital increased cancer clinical trial enrollment 4.6-fold after instituting manual processes: enrolled patients were 56% non-White, 57% non-English speaking, and over 80% resided in low-income areas.…”

Section: Expanding the Screening Processmentioning

confidence: 99%

Strategies to Advance Equity in Cancer Clinical Trials

Guerra

Fleury

Byatt

et al. 2022

American Society of Clinical Oncology Educational Book

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Cancer clinical trials are critical for testing new treatments, yet less than 5% of patients with cancer enroll in these trials. Minority groups, elderly individuals, and rural populations are particularly underrepresented in cancer treatment trials. Strategies for advancing equity in cancer clinical trials for these populations include (1) optimizing clinical trial matching by broadening eligibility criteria, screening all patients for trial eligibility, expanding the number of trials against which patients are screened, and following up on all patient matches with an enrollment invitation; (2) conducting site self-assessments to identify clinical-, patient-, provider-, and system-level barriers that contribute to low rates of clinical trial screening and enrollment; (3) creating a quality improvement plan that addresses the barriers to enrollment and incorporates the use of tools and strategies such as clinical trial checklists; workforce development and trainings to improve cultural competence and reduce unconscious bias; guides to promote community education, outreach and engagement with cancer clinical trials; screening and accrual logs designed to measure participation by demographics; models of informed consent that improve understanding; clinical trial designs that reduce accessibility barriers; use of cancer clinical trial patient navigators; and programs to eliminate barriers to participation and out-of-pocket expenses; and (4) working with stakeholders to develop both protocols that are inclusive of diverse populations’ geographic locations, and strategies to access those trials. These actions will support greater access for populations that have remained underrepresented in cancer clinical trials and thereby increase the generalizability and efficiency of cancer research.

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Section: Expanding the Screening Processmentioning

confidence: 99%