To address the increasing demand for inherited cancer genetic testing, we developed and evaluated a web-based educational tool to streamline genetic counseling (GC). Consented patients viewed the initial prototype containing core content (Version 1-Core) and provided feedback through three open-ended survey questions. Additional data were collected through individual interviews from a subgroup who viewed an enhanced version (Version 1-Enhanced), consisting of the same core content and additional optional content. Data were coded to synthesize most commonly repeated themes and conceptualize action items to guide refinement strategies. Of 305 participants, 80 responded to open-ended survey questions to suggest refinement strategies, after viewing Version 1-Core. Interviews with a subgroup of seven participants, who viewed Version 1-Enhanced, provided additional feedback. Of 11 unique action items identified, five overlapped across datasets (provide instructions, simplify language, improve visuals, embed knowledge questions with explanations, include more insurance-related information), three were identified only through open-ended survey data (incorporate automatic progression, clarify test result information, increase interactive content), and three were identified only through interviews (ensure core content is viewed, incorporate progress bar, feature embedded optional content at the end of the tool). Ten action items aligned with underlying tool objectives to provide an interactive online pre-test GC solution and were used to guide refinement strategies. Our results demonstrate the value of rigorous qualitative data collection and analysis in health research and the use of the self-directed learning framework and eHealth strategies to leverage technology in scaling up and innovating the delivery of pre-test GC for inherited cancer.
Tezak A, et al. Qualitative Methods for Refining a Web-Based Educational Tool for Patients Focused on Inherited Cancer Predisposition. J Cancer Educ. 2021 Jan 5; Online ahead of print. PMID: 33400205.