Feasibility of the Genetic Information Assistant Chatbot to Provide Genetic Education and Study Genetic Test Adoption Among Pancreatic Cancer Patients at Johns Hopkins Hospital

Nidhi Soley; Alison Klein; Casey Overby Taylor; Michelle Nguyen; Gabriella Ewachiw; Hridaya Shah; Joann Bodurtha

Feasibility of the Genetic Information Assistant Chatbot to Provide Genetic Education and Study Genetic Test Adoption Among Pancreatic Cancer Patients at Johns Hopkins Hospital

AMIA Jt Summits Transl Sci Proc. 2023 Jun 16:2023:497-504. eCollection 2023.

Authors

Nidhi Soley^{1

2}, Alison Klein^{3

4}, Casey Overby Taylor^{1

3}, Michelle Nguyen^{1

3}, Gabriella Ewachiw⁵, Hridaya Shah⁵, Joann Bodurtha^{3

4}

Affiliations

¹ Johns Hopkins University, Institute for Computational Medicine.
² Johns Hopkins Whiting School of Engineering.
³ Johns Hopkins University School of Medicine.
⁴ Johns Hopkins Bloomberg School of Public Health.
⁵ Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD.

PMID: 37350913
PMCID: PMC10283105

Abstract

Genetic testing is a valuable tool to guide care of pancreatic cancer patients, yet personal and family uncertainty about the benefits of genetic testing (i.e., decisional conflict) may lead to low adoption. Enabling patients to learn more about genetic testing before their scheduled appointments may help to address this decisional conflict problem. We completed a feasibility assessment of a chatbot to provide genetic education (GEd) with 60 pancreatic cancer patients and using the chatbot to deliver surveys to assess: (a) opinions about the GEd, and (b) decisional conflict about genetic testing. Findings demonstrate intervention and study feasibility with about 80% of participants engaging with the GEd chatbot, 71% of which completed at least one survey. Overall, participants appear to have favorable opinions of the chatbot-delivered education and thought it was helpful to decide about genetic testing. Furthermore, patients who chose to get genetic testing spent more time interacting with the chatbot. Findings will be used to improve chatbot design and to facilitate a well-powered future trial.