Development and Usability Testing of an Information Needs‐Based Chatbot for Individuals With Pathogenic Variants in <scp> <i>BRCA</i> </scp> Genes in Korea: A Methodological Study

Korean individuals with BRCA pathogenic/likely pathogenic (P/LP) variants have limited access to genetic counseling due to shortages of genetic professionals and lack of reimbursement. Despite the growing interest in digital health interventions, few digital counseling support tools have been designed to address the diverse information needs of this population. This study aimed to develop a user information needs-based chatbot for providing individualized information for individuals with BRCA pathogenic variants and to evaluate its usability. The chatbot, All about BRCA, was developed using a commercial chatbot platform through three stages from October 2021 to September 2022: identifying information needs via a systematic review and web-crawling analysis of patient-generative questions from online communities; developing a prototype chatbot validated by seven healthcare professionals and implementing it as rule-based system; conducting usability testing with individuals with P/LP variants in BRCA genes, using the modified mHealth App Usability Questionnaire and daily usage diaries. The modified mHealth App Usability Questionnaire scores were analyzed using descriptive statistics, and narrative diary data were analyzed using content analysis. Following iterative rounds of reviews and usability testing, the final chatbot comprised 10 information categories with 62 predefined question-scenario-responses. Nineteen participants evaluated the chatbot, yielding a mean modified mHealth App Usability Questionnaire score of 5.7 ± 0.8 out of 7. Scoring on all subscales, that is, ease of use, interface/satisfaction, and usefulness, exceeded 5.5. Responses on narrative diary data were predominantly positive, with users valuing systematic, detailed, and practical information. Key limitations of the chatbot included restricted question recognition and limited information depth. Despite limitations in question recognition, the chatbot demonstrated acceptable usability as a complementary tool to support individuals with BRCA pathogenic variants. It may help address constraints of limited consultation time and genetic expert shortages. Future development should strengthen conversational capabilities.

Authors

Institutions

Publication Details

Journal
Journal of Genetic Counseling
Published
2026-07-30
DOI
https://doi.org/10.1002/jgc4.70268
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and Usability Testing of an Information Needs‐Based Chatbot for Individuals With Pathogenic Variants in BRCA Genes in Korea: A Methodological Study

Maria C. Katapodi, Sue Kim, Sun Young Park, Youlim Kim
Journal of Genetic Counseling
Digital Mental Health Interventions
article

Development and Usability Testing of an Information Needs‐Based Chatbot for Individuals With Pathogenic Variants in BRCA Genes in Korea: A Methodological Study

Maria C. Katapodi, Sue Kim, Sun Young Park, Youlim Kim
article en

Abstract

Korean individuals with BRCA pathogenic/likely pathogenic (P/LP) variants have limited access to genetic counseling due to shortages of genetic professionals and lack of reimbursement. Despite the growing interest in digital health interventions, few digital counseling support tools have been designed to address the diverse information needs of this population. This study aimed to develop a user information needs-based chatbot for providing individualized information for individuals with BRCA pathogenic variants and to evaluate its usability. The chatbot, All about BRCA, was developed using a commercial chatbot platform through three stages from October 2021 to September 2022: identifying information needs via a systematic review and web-crawling analysis of patient-generative questions from online communities; developing a prototype chatbot validated by seven healthcare professionals and implementing it as rule-based system; conducting usability testing with individuals with P/LP variants in BRCA genes, using the modified mHealth App Usability Questionnaire and daily usage diaries. The modified mHealth App Usability Questionnaire scores were analyzed using descriptive statistics, and narrative diary data were analyzed using content analysis. Following iterative rounds of reviews and usability testing, the final chatbot comprised 10 information categories with 62 predefined question-scenario-responses. Nineteen participants evaluated the chatbot, yielding a mean modified mHealth App Usability Questionnaire score of 5.7 ± 0.8 out of 7. Scoring on all subscales, that is, ease of use, interface/satisfaction, and usefulness, exceeded 5.5. Responses on narrative diary data were predominantly positive, with users valuing systematic, detailed, and practical information. Key limitations of the chatbot included restricted question recognition and limited information depth. Despite limitations in question recognition, the chatbot demonstrated acceptable usability as a complementary tool to support individuals with BRCA pathogenic variants. It may help address constraints of limited consultation time and genetic expert shortages. Future development should strengthen conversational capabilities.

Journal of Genetic CounselingVol. 35(4)
University of Basel (CH), Yonsei University (KR), Kyungpook National University (KR), Kosin University (KR)
Openalex Percentile: Top 8%
Digital Mental Health Interventions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.