This clinical trial compares the use of artificial intelligence (AI)-informed colorectal cancer screening navigation versus standard of care to increase screening in patients 45-49 years old who have not undergone prior screening. Colorectal cancer (CRC) is the fourth most commonly diagnosed cancer and the second leading cause of cancer death in the United States. While rates of colorectal cancer have been declining among adults 50 years and older, the incidence of CRC is increasing among adults under age 50. One way to identify patients at high risk for colorectal cancer is via a machine learning algorithm (MLA) which is a type of AI. The MLA identifies individuals between the ages of 45-49 who are more likely to have colorectal pathology and merit prioritized screening and patient navigation. The patient navigators will provide assistance in completing either a fecal immunochemical test (FIT) or colonoscopy screening. AI-informed colorectal cancer screening navigation may be more effective in increasing screening uptake in screening naive patients age 45-49.
Study sponsor and potential other locations can be found on ClinicalTrials.gov for NCT07230795.
Locations matching your search criteria
United States
Pennsylvania
Philadelphia
University of Pennsylvania/Abramson Cancer CenterStatus: Active
Contact: Carmen Guerra
PRIMARY OBJECTIVES:
I. Implement a randomized controlled trial to evaluate risk-informed patient navigation in improving CRC screening rates for Penn Internal Medicine and Family Medicine patients aged 45-49.
II. Test the feasibility, usability, and effectiveness of algorithm-informed navigation on colorectal cancer screening (fecal immunochemical test [FIT] and colonoscopy) rates and precancerous lesion and CRC detection rates among Penn Internal Medicine and Family Medicine patients aged 45-49.
III. Evaluate barriers to CRC screening among patients aged 45-49, using a survey to identify individual and systemic factors that influence screening uptake across recommended screening modalities.
IV. Conduct individual qualitative interviews to explore patient perspectives on the use of AI for CRC risk stratification among individuals whose health data have already been processed by an AI model, with particular attention to attitudes toward consent, perceived benefits and harms, trust, and expectations for transparency and governance.
OUTLINE: Patients are randomized to 1 of 2 arms.
ARM I: Patients receive outreach via phone call from a CRC navigator who communicates their relative increased risk of CRC, assesses and addresses barriers to completion of CRC screening. The navigator assists them in coordinating colonoscopy or a FIT test screening. They may address barriers such as lack of awareness/knowledge about screening, misinformation, negative attitudes and fear, scheduling, inability to afford the prep, and lack of transportation and escort.
ARM II: Patients receive standard of care treatment consisting of a letter informing them of their potential increased risk for CRC, encouraging them to obtain age-appropriate screening and providing information for patient navigation should they be interested. Patients may also be referred to a nurse navigator, if initiated by a healthcare staff member.
After completion of the study intervention, patients are followed up periodically until month 12.
Trial PhaseNo phase specified
Trial Typescreening
Lead OrganizationUniversity of Pennsylvania/Abramson Cancer Center
Principal InvestigatorCarmen Guerra
