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Chatbot to Maximize Hereditary Cancer Genetic Risk Assessment
Sponsor: Weill Medical College of Cornell University
Summary
In this study, the investigators aim to compare a mobile health platform, known as a 'chatbot,' that leverages artificial intelligence and natural language processing to scale communication, to 'usual care' that patients would receive. This comparison will enable the investigators to determine if the chatbot system can improve rates of recommendation for genetic testing among patients at elevated risk of harboring a familial cancer syndrome in an all-Medicaid gynecology clinic. Furthermore, the investigators aim to evaluate facilitators of inequity in regard to patient access to and utilization of genetic testing services.
Official title: Evaluation of a Chatbot to Maximize Hereditary Cancer Genetic Risk Assessment in an Underserved Gynecology Population
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
150
Start Date
2023-01-15
Completion Date
2026-12
Last Updated
2025-07-28
Healthy Volunteers
Yes
Interventions
Chatbot
A chatbot is a software program designed to simulate human conversation, typically via text. Chatbots utilize natural language processing to gather patient data, anticipate questions, and predict responses. In this study, the Chatbot will simulate a text-like conversation with patients via a smartphone and use this platform to deal with the time-consuming nature of family history collection. The chatbots can then triage the collected family history against medical guidelines to determine which patients warrant genetic testing. For those patients meeting criteria for genetic testing, the chatbot can offer pre-test education and assist physicians with ordering genetic testing for interested patients meeting high-risk criteria.
Locations (4)
NYP Brooklyn Methodist Hospital
Brooklyn, New York, United States
NYP Lower Manhattan Hospital
New York, New York, United States
Weill Cornell Medicine
New York, New York, United States
NYP Medical Group Queens
Queens, New York, United States