Ask Rosa – The making of a digital genetic conversation tool, a chatbot, about hereditary breast and ovarian cancer

Siglen, Elen; Høberg-Vetti, Hildegunn; Lunde, Aslaug Beathe Forberg; Hatlebrekke, Thomas Akselberg; Strømsvik, Nina; Hamang, Anniken; Hovland, Sigrid Tronsli; Rettberg, Jill Walker; Steen, Vidar M.; Bjorvatn, Cathrine

doi:10.1016/j.pec.2021.09.027

Cited by 44 publications

(53 citation statements)

References 16 publications

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“…Chatbots are web-based tools that use artificial intelligence to simulate human conversation (Ireland et al 2021 ) and are currently in use in many areas of medicine to support diagnostics, disease management, and treatment (Ahmed et al 2021 ; Cooper and Ireland 2018 ; Dosovitsky et al 2020 ; Gaffney et al 2019 ; Ghosh, Bhatia, & Bhatia, 2018; Greer et al 2019 ; Kobori et al 2018 ; Milne-Ives et al 2020 ; Stein and Brooks 2017 ; Watson et al 2012 ). In genomics, chatbots are being developed to assist with delivering various components of care (Heald et al 2020 ; Ireland et al 2021 ; Nazareth et al 2021 ; Sato et al 2021 ; Schmidlen et al 2019 ; Siglen et al 2021 ). For example, chatbots exist or are in development to support patients with pre-test counseling, result disclosure, communication with the health care team, notification of family members about results or the availability of cascade testing, and attending to distress and anxiety (Heald et al 2020 ; Ireland et al 2021 ; Nazareth et al 2021 ; Sato et al 2021 ; Schmidlen et al 2022 , 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…A 2020 systematic review of the use of chatbots in health care found that patients generally reported high satisfaction with chatbots (Milne-Ives et al 2020 ). However, research aimed at understanding patients’ attitudes towards, acceptance of, trust in, and comfort with chatbots within genetics is only just emerging (Nazareth et al 2021 ; Siglen et al 2021 ). For example, initial feedback on a genetics chatbot designed to answer questions about hereditary breast and ovarian cancer indicated that pilot testers found it to be trustworthy and user-friendly (Siglen et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…However, research aimed at understanding patients’ attitudes towards, acceptance of, trust in, and comfort with chatbots within genetics is only just emerging (Nazareth et al 2021 ; Siglen et al 2021 ). For example, initial feedback on a genetics chatbot designed to answer questions about hereditary breast and ovarian cancer indicated that pilot testers found it to be trustworthy and user-friendly (Siglen et al 2021 ). Another chatbot designed to calculate hereditary cancer risk prior to a clinic appointment was rated to be satisfactory on a one-item scale completed by users (Nazareth et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…These findings indicated that the chatbot was a viable tool for sharing information and cascade testing (Schmidlen et al 2022 ). While these studies provide preliminary evidence of positive user experiences and patient satisfaction with chatbots, evaluation has been limited to the functions of information provision or triage, with a focus on adult cancer or screening populations (Nazareth et al 2021 ; Schmidlen et al 2022 , 2019 ; Siglen et al 2021 ; Wang et al 2015 ). A more in-depth understanding of the appropriateness of these tools, including evidence on diverse users’ needs and preferences in non-cancer and non-screening settings, is required to develop patient-centered digital tool for a range of settings.…”

Section: Introductionmentioning

confidence: 99%

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Finding the sweet spot: a qualitative study exploring patients’ acceptability of chatbots in genetic service delivery

et al. 2023

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Chatbots, web-based artificial intelligence tools that simulate human conversation, are increasingly in use to support many areas of genomic medicine. However, patient preferences towards using chatbots across the range of clinical settings are unknown. We conducted a qualitative study with individuals who underwent genetic testing for themselves or their child. Participants were asked about their preferences for using a chatbot within the genetic testing journey. Thematic analysis employing interpretive description was used. We interviewed 30 participants (67% female, 50% 50 + years). Participants considered chatbots to be inefficient for very simple tasks (e.g., answering FAQs) or very complex tasks (e.g., explaining results). Chatbots were acceptable for moderately complex tasks where participants perceived a favorable return on their investment of time and energy. In addition to achieving this “sweet spot,” participants anticipated that their comfort with chatbots would increase if the chatbot was used as a complement to but not a replacement for usual care. Participants wanted a “safety net” (i.e., access to a clinician) for needs not addressed by the chatbot. This study provides timely insights into patients’ comfort with and perceived limitations of chatbots for genomic medicine and can inform their implementation in practice. Supplementary Information The online version contains supplementary material available at 10.1007/s00439-022-02512-2.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finding the sweet spot: a qualitative study exploring patients’ acceptability of chatbots in genetic service delivery

et al. 2023

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“…In genetics, chatbots are being used to collect family history (Ponathil et al, 2020), inform women about the utility of expanded carrier screening (ECS) (Nazareth et al, 2021), identify patients at risk for inherited colorectal cancer syndromes (Heald et al, 2020), provide information and support to breast and ovarian cancer patients undergoing genetic testing (Siglen et al, 2021), improve cancer risk communication and decision‐making for adolescents and young adults (Mack, 2020), and provide pre‐test decision support to patients about the option to receive secondary genomic findings (Ireland et al, 2021). Schmidlen et al (2019) found patients in a rural setting supported using chatbots to consent for genomics research, interact with clinicians after receiving a genetic test result, and share genetic risk information with relatives.…”

Section: Introductionmentioning

confidence: 99%

Use of a chatbot to increase uptake of cascade genetic testing

Schmidlen

Jones²,

Campbell‐Salome³

et al. 2022

Journal of Genetic Counseling

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Successful proband‐mediated family communication and subsequent cascade genetic testing uptake requires interventions that present information clearly, in sufficient detail, and with medical authority. To facilitate family communication for patients receiving clinically actionable results via the MyCode® Community Health Initiative, a Family Sharing Tool (FST) and a cascade chatbot were developed. FST is an electronic mechanism allowing patients to share genetic test results with relatives via chatbot. The cascade chatbot describes the proband's result, associated disease risks, and recommended management and captures whether the user is a blood relative or caregiver, sex, and relationship to the proband. FST and cascade chatbot uptake among MyCode® probands and relatives was tracked from August 2018 through February 2020. Cascade genetic testing uptake was collected from testing laboratories as number of cascades per proband. Fifty‐eight percent (316/543) of probands consented to FST; 42% (227/543) declined. Receipt preferences were patient electronic health record (EHR) portal (52%), email (29%), and text (19%). Patient EHR portal users (p < 0.001) and younger patients were more likely to consent (p < 0.001). FST was deployed to 308 probands. Fifty‐nine percent (183/308) opened; of those, 56% (102/183) used FST to send a cascade chatbot to relatives. These 102 probands shared a cascade chatbot with 377 relatives. Sixty‐two percent (235/377) of relatives opened; of these, 69% (161/235) started, and of these, 57% (92/161) completed the cascade chatbot. Cascade genetic testing uptake was significantly greater among relatives of probands who consented to the FST (M = 2.34 cascades, SD = 2.10) than relatives of probands who declined (M = 1.40 cascades, SD = 0.82, p < 0.001). Proband age was not a significant predictor of cascade genetic testing uptake. Further work is needed to better understand factors impacting proband use of FST and relative use of cascade chatbots.

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Physician and Artificial Intelligence Chatbot Responses to Cancer Questions From Social Media

Chen,

Parsa,

Hope

et al. 2024

JAMA Oncol

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ImportanceArtificial intelligence (AI) chatbots pose the opportunity to draft template responses to patient questions. However, the ability of chatbots to generate responses based on domain-specific knowledge of cancer remains to be tested.ObjectiveTo evaluate the competency of AI chatbots (GPT-3.5 [chatbot 1], GPT-4 [chatbot 2], and Claude AI [chatbot 3]) to generate high-quality, empathetic, and readable responses to patient questions about cancer.Design, Setting, and ParticipantsThis equivalence study compared the AI chatbot responses and responses by 6 verified oncologists to 200 patient questions about cancer from a public online forum. Data were collected on May 31, 2023.ExposuresRandom sample of 200 patient questions related to cancer from a public online forum (Reddit r/AskDocs) spanning from January 1, 2018, to May 31, 2023, was posed to 3 AI chatbots.Main Outcomes and MeasuresThe primary outcomes were pilot ratings of the quality, empathy, and readability on a Likert scale from 1 (very poor) to 5 (very good). Two teams of attending oncology specialists evaluated each response based on pilot measures of quality, empathy, and readability in triplicate. The secondary outcome was readability assessed using Flesch-Kincaid Grade Level.ResultsResponses to 200 questions generated by chatbot 3, the best-performing AI chatbot, were rated consistently higher in overall measures of quality (mean, 3.56 [95% CI, 3.48-3.63] vs 3.00 [95% CI, 2.91-3.09]; P &lt; .001), empathy (mean, 3.62 [95% CI, 3.53-3.70] vs 2.43 [95% CI, 2.32-2.53]; P &lt; .001), and readability (mean, 3.79 [95% CI, 3.72-3.87] vs 3.07 [95% CI, 3.00-3.15]; P &lt; .001) compared with physician responses. The mean Flesch-Kincaid Grade Level of physician responses (mean, 10.11 [95% CI, 9.21-11.03]) was not significantly different from chatbot 3 responses (mean, 10.31 [95% CI, 9.89-10.72]; P &gt; .99) but was lower than those from chatbot 1 (mean, 12.33 [95% CI, 11.84-12.83]; P &lt; .001) and chatbot 2 (mean, 11.32 [95% CI, 11.05-11.79]; P = .01).Conclusions and RelevanceThe findings of this study suggest that chatbots can generate quality, empathetic, and readable responses to patient questions comparable to physician responses sourced from an online forum. Further research is required to assess the scope, process integration, and patient and physician outcomes of chatbot-facilitated interactions.

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Ask Rosa – The making of a digital genetic conversation tool, a chatbot, about hereditary breast and ovarian cancer

Cited by 44 publications

References 16 publications

Finding the sweet spot: a qualitative study exploring patients’ acceptability of chatbots in genetic service delivery

Finding the sweet spot: a qualitative study exploring patients’ acceptability of chatbots in genetic service delivery

Use of a chatbot to increase uptake of cascade genetic testing

Physician and Artificial Intelligence Chatbot Responses to Cancer Questions From Social Media

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