User testing of a previsit chatbot developed for population genomic screening and counseling

Many emerging service delivery models triage genetic counselor time with patients to postgenetic testing, including population genomic screening followed by genetic counseling (GC). To prime patients and help genetic counselors tailor such visits, a previsit (PV) chatbot was created to assess patient understanding, emotional response to, and family communication about genetic results. This study explored patients' perceptions of the PV chatbot and how they would use it. User testing was conducted via videoconference with patients who had completed GC. A codebook thematic analysis informed by Proctor's Conceptual Model of Implementation Research in a postpositivist paradigm was conducted. In total, 16 participants completed user testing, of whom 12 were women and 4 were men with a mean age of 55.4 (range 32-69). Participants had a variety of genetic results out of 78 genes among cancer (PMS2 n = 2; PALB2 n = 4; BRCA2 n = 1) and cardiovascular (LDLR n = 1; MYBPC3 n = 1; DSP n = 1; TTN n = 5; MYH7 n = 1) conditions. Participants reported high acceptability (M = 4.53/5, SD = 0.45) and feasibility (M = 4.43/5, SD = 1.04) of the chatbot. Participants reported liking the chatbot because of its ease of use and anticipated benefit to GC. Participants viewed the chatbot as complementary to GC and shared that the chatbot would have helped prepare them for GC in ways they may not have considered, including inviting a family member to join the appointment. Participants desired more personalization within the chatbot, including responsiveness to their personal/family history, optional supplementary education, and more emotionally supportive language. Some participants described challenges with certain aspects of the chatbot, including the repetitiveness and phrasing of validated scaled measures. Overall, participants perceived the PV chatbot to be of value in educating and preparing them for GC and reflected on how the PV chatbot may have enhanced GC. Many of the perceived benefits of this chatbot are applicable across GC settings.

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Publication Details

Journal
Journal of Genetic Counseling
Published
2025-08-01
DOI
https://doi.org/10.1002/jgc4.70090
Citations
1
Primary Topic
Mobile Health and mHealth Applications
Type
article
Field-Weighted Citation Impact
2.82
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User testing of a previsit chatbot developed for population genomic screening and counseling

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article

User testing of a previsit chatbot developed for population genomic screening and counseling

Gemme Campbell‐Salome, Nicole Walters, Andrew Brangan, Amy C. Sturm, Kelly Morgan, Sarah Savage, Zoe Lindsey‐Mills, Jessica Goehringer, Jasleen Kaur
article en
1 citations

Abstract

Many emerging service delivery models triage genetic counselor time with patients to postgenetic testing, including population genomic screening followed by genetic counseling (GC). To prime patients and help genetic counselors tailor such visits, a previsit (PV) chatbot was created to assess patient understanding, emotional response to, and family communication about genetic results. This study explored patients' perceptions of the PV chatbot and how they would use it. User testing was conducted via videoconference with patients who had completed GC. A codebook thematic analysis informed by Proctor's Conceptual Model of Implementation Research in a postpositivist paradigm was conducted. In total, 16 participants completed user testing, of whom 12 were women and 4 were men with a mean age of 55.4 (range 32-69). Participants had a variety of genetic results out of 78 genes among cancer (PMS2 n = 2; PALB2 n = 4; BRCA2 n = 1) and cardiovascular (LDLR n = 1; MYBPC3 n = 1; DSP n = 1; TTN n = 5; MYH7 n = 1) conditions. Participants reported high acceptability (M = 4.53/5, SD = 0.45) and feasibility (M = 4.43/5, SD = 1.04) of the chatbot. Participants reported liking the chatbot because of its ease of use and anticipated benefit to GC. Participants viewed the chatbot as complementary to GC and shared that the chatbot would have helped prepare them for GC in ways they may not have considered, including inviting a family member to join the appointment. Participants desired more personalization within the chatbot, including responsiveness to their personal/family history, optional supplementary education, and more emotionally supportive language. Some participants described challenges with certain aspects of the chatbot, including the repetitiveness and phrasing of validated scaled measures. Overall, participants perceived the PV chatbot to be of value in educating and preparing them for GC and reflected on how the PV chatbot may have enhanced GC. Many of the perceived benefits of this chatbot are applicable across GC settings.

Journal of Genetic CounselingVol. 34(4)
Nationwide Children's Hospital (US), The Ohio State University Wexner Medical Center (US), Geisinger Neuroscience Institute (US), Geisinger Health System (US), Geisinger Commonwealth School of Medicine (US)
Quality Education
Openalex Percentile: Top 9%
Mobile Health and mHealth Applications
2.82
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