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

A comparative evaluation of ChatGPT 3.5 and ChatGPT 4 in responses to selected genetics questions

OBJECTIVES

To evaluate the efficacy of ChatGPT 4 (GPT-4) in delivering genetic information about BRCA1, HFE, and MLH1, building on previous findings with ChatGPT 3.5 (GPT-3.5). To focus on assessing the utility, limitations, and ethical implications of using ChatGPT in medical settings.

MATERIALS AND METHODS

A structured survey was developed to assess GPT-4's clinical value. An expert panel of genetic counselors and clinical geneticists evaluated GPT-4's responses to these questions. We also performed comparative analysis with GPT-3.5, utilizing descriptive statistics and using Prism 9 for data analysis.

RESULTS

The findings indicate improved accuracy in GPT-4 over GPT-3.5 (P < .0001). However, notable errors in accuracy remained. The relevance of responses varied in GPT-4, but was generally favorable, with a mean in the "somewhat agree" range. There was no difference in performance by disease category. The 7-question subset of the Bot Usability Scale (BUS-15) showed no statistically significant difference between the groups but trended lower in the GPT-4 version.

DISCUSSION AND CONCLUSION

The study underscores GPT-4's potential role in genetic education, showing notable progress yet facing challenges like outdated information and the necessity of ongoing refinement. Our results, while showing promise, emphasizes the importance of balancing technological innovation with ethical responsibility in healthcare information delivery.

Chatbot for the Return of Positive Genetic Screening Results for Hereditary Cancer Syndromes: a Prompt Engineering Study

Background

The growing demand for genomic testing and limited access to experts necessitate innovative service models. While chatbots have shown promise in supporting genomic services like pre-test counseling, their use in returning positive genetic results, especially using the more recent large language models (LLMs) remains unexplored.

Objective

This study reports the prompt engineering process and intrinsic evaluation of the LLM component of a chatbot designed to support returning positive population-wide genomic screening results.

Methods

We used a three-step prompt engineering process, including Retrieval-Augmented Generation (RAG) and few-shot techniques to develop an open-response chatbot. This was then evaluated using two hypothetical scenarios, with experts rating its performance using a 5-point Likert scale across eight criteria: tone, clarity, program accuracy, domain accuracy, robustness, efficiency, boundaries, and usability.

Results

The chatbot achieved an overall score of 3.88 out of 5 across all criteria and scenarios. The highest ratings were in Tone (4.25), Usability (4.25), and Boundary management (4.0), followed by Efficiency (3.88), Clarity and Robustness (3.81), and Domain Accuracy (3.63). The lowest-rated criterion was Program Accuracy, which scored 3.25.

Discussion

The LLM handled open-ended queries and maintained boundaries, while the lower Program Accuracy rating indicates areas for improvement. Future work will focus on refining prompts, expanding evaluations, and exploring optimal hybrid chatbot designs that integrate LLM components with rule-based chatbot components to enhance genomic service delivery.

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

Importance

Artificial 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.

Objective

To 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 Participants

This 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.

Exposures

Random 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 Measures

The 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.

Results

Responses 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 < .001), empathy (mean, 3.62 [95% CI, 3.53-3.70] vs 2.43 [95% CI, 2.32-2.53]; P < .001), and readability (mean, 3.79 [95% CI, 3.72-3.87] vs 3.07 [95% CI, 3.00-3.15]; P < .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 > .99) but was lower than those from chatbot 1 (mean, 12.33 [95% CI, 11.84-12.83]; P < .001) and chatbot 2 (mean, 11.32 [95% CI, 11.05-11.79]; P = .01).

Conclusions and Relevance

The 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.

Cancer Patients' Perspectives and Requirements of Digital Health Technologies: A Scoping Literature Review

Digital health technologies have the potential to alleviate the increasing cancer burden. Incorporating patients' perspectives on digital health tools has been identified as a critical determinant for their successful uptake in cancer care. The main objective of this scoping review was to provide an overview of the existing evidence on cancer patients' perspectives and requirements for patient-facing digital health technologies. Three databases (CINAHL, MEDLINE, Science Direct) were searched and 128 studies were identified as eligible for inclusion. Web-based software/platforms, mobile or smartphone devices/applications, and remote sensing/wearable technologies employed for the delivery of interventions and patient monitoring were the most frequently employed technologies in cancer care. The abilities of digital tools to enable care management, user-friendliness, and facilitate patient-clinician interactions were the technological requirements predominantly considered as important by cancer patients. The findings from this review provide evidence that could inform future research on technology-associated parameters influencing cancer patients' decisions regarding the uptake and adoption of patient-facing digital health technologies.

Designing, Developing, and Testing a Chatbot for Parents and Caregivers of Children and Young People With Rheumatological Conditions (the IMPACT Study): Protocol for a Co-Designed Proof-of-Concept Study

BACKGROUND

Pediatric rheumatology is a term that encompasses over 80 conditions affecting different organs and systems. Children and young people with rheumatological chronic conditions are known to have high levels of mental health problems and therefore are at risk of poor health outcomes. Clinical psychologists can help children and young people manage the daily difficulties of living with one of these conditions; however, there are insufficient pediatric psychologists in the United Kingdom. We urgently need to consider other ways of providing early, essential support to improve their current well-being. One way of doing this is to empower parents and caregivers to have more of the answers that their children and young people need to support them further between their hospital appointments.

OBJECTIVE

The objective of this co-designed proof-of-concept study is to design, develop, and test a chatbot intervention to support parents and caregivers of children and young people with rheumatological conditions.

METHODS

This study will explore the needs and views of children and young people with rheumatological conditions, their siblings, parents, and caregivers, as well as health care professionals working in pediatric rheumatology. We will ask approximately 100 participants in focus groups where they think the gaps are in current clinical care and what ideas they have for improving upon them. Creative experience-based co-design workshops will then decide upon top priorities to develop further while informing the appearance, functionality, and practical delivery of a chatbot intervention. Upon completion of a minimum viable product, approximately 100 parents and caregivers will user-test the chatbot intervention in an iterative sprint methodology to determine its worth as a mechanism for support for parents.

RESULTS

A total of 73 children, young people, parents, caregivers, and health care professionals have so far been enrolled in the study, which began in November 2023. The anticipated completion date of the study is April 2026. The data analysis is expected to be completed in January 2026, with the results being published in April 2026.

CONCLUSIONS

This study will provide evidence on the accessibility, acceptability, and usability of a chatbot intervention for parents and caregivers of children and young people with rheumatological conditions. If proven useful, it could lead to a future efficacy trial of one of the first chatbot interventions to provide targeted and user-suggested support for parents and caregivers of children with chronic health conditions in health care services. This study is unique in that it will detail the needs and wants of children, young people, siblings, parents, and caregivers to improve the current support given to families living with pediatric rheumatological conditions. It will be conducted across the whole of the United Kingdom for all pediatric rheumatological conditions at all stages of the disease trajectory.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/57238.

Development of a culturally targeted chatbot to inform living kidney donor candidates of African ancestry about APOL1 genetic testing: a mixed methods study

Clinical chatbots are increasingly used to help integrate genetic testing into clinical contexts, but no chatbot exists for Apolipoprotein L1 (APOL1) genetic testing of living kidney donor (LKD) candidates of African ancestry. Our study aimed to culturally adapt and assess perceptions of the Gia® chatbot to help integrate APOL1 testing into LKD evaluation. Ten focus groups and post-focus group surveys were conducted with 54 LKDs, community members, and kidney transplant recipients of African ancestry. Data were analyzed through thematic analysis and descriptive statistics. Key themes about making Gia culturally targeted included ensuring: (1) transparency by providing Black LKDs' testimonials, explaining patient privacy and confidentiality protections, and explaining how genetic testing can help LKD evaluation; (2) content is informative by educating Black LKDs about APOL1 testing instead of aiming to convince them to undergo testing, presenting statistics, and describing how genetic discrimination is legally prevented; and (3) content avoids stigma about living donation in the Black community. Most agreed Gia was neutral and unbiased (82%), trustworthy (82%), and words, phrases, and expressions were familiar to the intended audience (85%). Our culturally adapted APOL1 Gia chatbot was well regarded. Future research should assess how this chatbot could supplement provider discussion prior to genetic testing to scale APOL1 counseling and testing for LKD candidate clinical evaluation.

Evaluation framework for conversational agents with artificial intelligence in health interventions: a systematic scoping review

OBJECTIVES

Conversational agents (CAs) with emerging artificial intelligence present new opportunities to assist in health interventions but are difficult to evaluate, deterring their applications in the real world. We aimed to synthesize existing evidence and knowledge and outline an evaluation framework for CA interventions.

MATERIALS AND METHODS

We conducted a systematic scoping review to investigate designs and outcome measures used in the studies that evaluated CAs for health interventions. We then nested the results into an overarching digital health framework proposed by the World Health Organization (WHO).

RESULTS

The review included 81 studies evaluating CAs in experimental (n = 59), observational (n = 15) trials, and other research designs (n = 7). Most studies (n = 72, 89%) were published in the past 5 years. The proposed CA-evaluation framework includes 4 evaluation stages: (1) feasibility/usability, (2) efficacy, (3) effectiveness, and (4) implementation, aligning with WHO's stepwise evaluation strategy. Across these stages, this article presents the essential evidence of different study designs (n = 8), sample sizes, and main evaluation categories (n = 7) with subcategories (n = 40). The main evaluation categories included (1) functionality, (2) safety and information quality, (3) user experience, (4) clinical and health outcomes, (5) costs and cost benefits, (6) usage, adherence, and uptake, and (7) user characteristics for implementation research. Furthermore, the framework highlighted the essential evaluation areas (potential primary outcomes) and gaps across the evaluation stages.

DISCUSSION AND CONCLUSION

This review presents a new framework with practical design details to support the evaluation of CA interventions in healthcare research.

PROTOCOL REGISTRATION

The Open Science Framework (https://osf.io/9hq2v) on March 22, 2021.

Barriers and facilitators to the implementation of family cancer history collection tools in oncology clinical practices

INTRODUCTION

This study aimed to identify barriers and facilitators to the implementation of family cancer history (FCH) collection tools in clinical practices and community settings by assessing clinicians' perceptions of implementing a chatbot interface to collect FCH information and provide personalized results to patients and providers.

OBJECTIVES

By identifying design and implementation features that facilitate tool adoption and integration into clinical workflows, this study can inform future FCH tool development and adoption in healthcare settings.

MATERIALS AND METHODS

Quantitative data were collected using survey to evaluate the implementation outcomes of acceptability, adoption, appropriateness, feasibility, and sustainability of the chatbot tool for collecting FCH. Semistructured interviews were conducted to gather qualitative data on respondents' experiences using the tool and recommendations for enhancements.

RESULTS

We completed data collection with 19 providers (n = 9, 47%), clinical staff (n = 5, 26%), administrators (n = 4, 21%), and other staff (n = 1, 5%) affiliated with the NCI Community Oncology Research Program. FCH was systematically collected using a wide range of tools at sites, with information being inserted into the patient's medical record. Participants found the chatbot tool to be highly acceptable, with the tool aligning with existing workflows, and were open to adopting the tool into their practice.

DISCUSSION AND CONCLUSIONS

We further the evidence base about the appropriateness of scripted chatbots to support FCH collection. Although the tool had strong support, the varying clinical workflows across clinic sites necessitate that future FCH tool development accommodates customizable implementation strategies. Implementation support is necessary to overcome technical and logistical barriers to enhance the uptake of FCH tools in clinical practices and community settings.

The NYCKidSeq randomized controlled trial: Impact of GUÍA digitally enhanced genetic results disclosure in diverse families

Digital solutions are needed to support rapid increases in the application of genetic/genomic tests (GTs) in diverse clinical settings and patient populations. We developed GUÍA, a bilingual digital application that facilitates disclosure of GT results. The NYCKidSeq randomized controlled trial enrolled diverse children with neurologic, cardiac, and immunologic conditions who underwent GTs. The trial evaluated GUÍA's impact on understanding the GT results by randomizing families to results disclosure genetic counseling with GUÍA (intervention) or standard of care (SOC). Parents/legal guardians (participants) completed surveys at baseline, post-results disclosure, and 6 months later. Survey measures assessed the primary study outcomes of participants' perceived understanding of and confidence in explaining their child's GT results and the secondary outcome of objective understanding. The analysis included 551 diverse participants, 270 in the GUÍA arm and 281 in SOC. Participants in the GUÍA arm had significantly higher perceived understanding post-results (OR = 2.8, CI[1.004, 7.617], p = 0.049) and maintained higher objective understanding over time (OR = 1.1, CI[1.004, 1.127], p = 0.038) compared to SOC. There was no impact on perceived confidence. Hispanic/Latino(a) individuals in the GUÍA arm maintained higher perceived understanding (OR = 3.9, CI[1.603, 9.254], p = 0.003), confidence (OR = 2.7, CI[1.021, 7.277], p = 0.046), and objective understanding (OR = 1.1, CI[1.009, 1.212], p = 0.032) compared to SOC. This trial demonstrates that GUÍA positively impacts understanding of GT results in diverse parents of children with suspected genetic conditions and builds a case for utilizing GUÍA to deliver complex results. Continued development and evaluation of digital applications in diverse populations are critical for equitably scaling GT offerings in specialty clinics.

Facilitating family communication of familial hypercholesterolemia genetic risk: Assessing engagement with innovative chatbot technology from the IMPACT-FH study

Objective

To assess use of two web-based conversational agents, the Family Sharing Chatbot (FSC) and One Month Chatbot (OMC), by individuals with familial hypercholesterolemia (FH).

Methods

FSC and OMC were sent using an opt-out methodology to a cohort of individuals receiving a FH genetic result. Data from 7/1/2021 through 5/12/2022 was obtained from the electronic health record and the chatbots' HIPAA-secure web portal.

Results

Of 175 subjects, 21 (12%) opted out of the chatbots. Older individuals were more likely to opt out. Most (91/154, 59%) preferred receiving chatbots via the patient EHR portal. Seventy-five individuals (49%) clicked the FSC link, 62 (40%) interacted, and 36 (23%) shared a chatbot about their FH result with at least one relative. Ninety-two of the subjects received OMC, 22 (23%) clicked the link and 20 (21%) interacted. Individuals who shared were majority female and younger on average than the overall cohort. Reminders tended to increase engagement.

Conclusion

Results demonstrate characteristics relevant to chatbot engagement. Individuals may be more inclined to receive chatbots if integrated within the patient EHR portal. Frequent reminders can potentially improve chatbot utilization.

Innovation

FSC and OMC employ innovative digital health technology that can facilitate family communication about hereditary conditions.

Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science

The release of ChatGPT has initiated new thinking about AI-based Chatbot and its application and has drawn huge public attention worldwide. Researchers and doctors have started thinking about the promise and application of AI-related large language models in medicine during the past few months. Here, the comprehensive review highlighted the overview of Chatbot and ChatGPT and their current role in medicine. Firstly, the general idea of Chatbots, their evolution, architecture, and medical use are discussed. Secondly, ChatGPT is discussed with special emphasis of its application in medicine, architecture and training methods, medical diagnosis and treatment, research ethical issues, and a comparison of ChatGPT with other NLP models are illustrated. The article also discussed the limitations and prospects of ChatGPT. In the future, these large language models and ChatGPT will have immense promise in healthcare. However, more research is needed in this direction.

Identifying Mediators of Intervention Effects Within a Randomized Controlled Trial to Motivate Cancer Genetic Risk Assessment Among Breast and Ovarian Cancer Survivors

BACKGROUND

A theory-guided Tailored Counseling and Navigation (TCN) intervention successfully increased cancer genetic risk assessment (CGRA) uptake among cancer survivors at increased risk of hereditary breast and ovarian cancer (HBOC). Understanding the pathways by which interventions motivate behavior change is important for identifying the intervention's active components.

PURPOSE

We examined whether the TCN intervention exerted effects on CGRA uptake through hypothesized theoretical mediators.

METHODS

Cancer survivors at elevated risk for HBOC were recruited from three statewide cancer registries and were randomly assigned to three arms: TCN (n = 212), Targeted Print (TP, n = 216), and Usual Care (UC, n = 213). Theoretical mediators from the Extended Parallel Process Model, Health Action Planning Approach, and Ottawa Decision Support Framework were assessed at baseline and 1-month follow-up; CGRA uptake was assessed at 6 months. Generalized structural equation modeling was used for mediation analysis.

RESULTS

The TCN effects were most strongly mediated by behavioral intention alone (β = 0.49 and 0.31) and by serial mediation through self-efficacy and intention (β = 0.041 and 0.10) when compared with UC and TP, respectively. In addition, compared with UC, the TCN also increased CGRA through increased perceived susceptibility, knowledge of HBOC, and response efficacy.

CONCLUSIONS

Risk communication and behavioral change interventions for hereditary cancer should stress a person's increased genetic risk and the potential benefits of genetic counseling and testing, as well as bolster efficacy beliefs by helping remove barriers to CGRA. System-level and policy interventions are needed to further expand access.

Evaluation of the Rosa Chatbot Providing Genetic Information to Patients at Risk of Hereditary Breast and Ovarian Cancer: Qualitative Interview Study

BACKGROUND

Genetic testing has become an integrated part of health care for patients with breast or ovarian cancer, and the increasing demand for genetic testing is accompanied by an increasing need for easy access to reliable genetic information for patients. Therefore, we developed a chatbot app (Rosa) that is able to perform humanlike digital conversations about genetic BRCA testing.

OBJECTIVE

Before implementing this new information service in daily clinical practice, we wanted to explore 2 aspects of chatbot use: the perceived utility and trust in chatbot technology among healthy patients at risk of hereditary cancer and how interaction with a chatbot regarding sensitive information about hereditary cancer influences patients.

METHODS

Overall, 175 healthy individuals at risk of hereditary breast and ovarian cancer were invited to test the chatbot, Rosa, before and after genetic counseling. To secure a varied sample, participants were recruited from all cancer genetic clinics in Norway, and the selection was based on age, gender, and risk of having a BRCA pathogenic variant. Among the 34.9% (61/175) of participants who consented for individual interview, a selected subgroup (16/61, 26%) shared their experience through in-depth interviews via video. The semistructured interviews covered the following topics: usability, perceived usefulness, trust in the information received via the chatbot, how Rosa influenced the user, and thoughts about future use of digital tools in health care. The transcripts were analyzed using the stepwise-deductive inductive approach.

RESULTS

The overall finding was that the chatbot was very welcomed by the participants. They appreciated the 24/7 availability wherever they were and the possibility to use it to prepare for genetic counseling and to repeat and ask questions about what had been said afterward. As Rosa was created by health care professionals, they also valued the information they received as being medically correct. Rosa was referred to as being better than Google because it provided specific and reliable answers to their questions. The findings were summed up in 3 concepts: "Anytime, anywhere"; "In addition, not instead"; and "Trustworthy and true." All participants (16/16) denied increased worry after reading about genetic testing and hereditary breast and ovarian cancer in Rosa.

CONCLUSIONS

Our results indicate that a genetic information chatbot has the potential to contribute to easy access to uniform information for patients at risk of hereditary breast and ovarian cancer, regardless of geographical location. The 24/7 availability of quality-assured information, tailored to the specific situation, had a reassuring effect on our participants. It was consistent across concepts that Rosa was a tool for preparation and repetition; however, none of the participants (0/16) supported that Rosa could replace genetic counseling if hereditary cancer was confirmed. This indicates that a chatbot can be a well-suited digital companion to genetic counseling.

Applications of artificial intelligence in clinical laboratory genomics

The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

The NYCKidSeq randomized controlled trial: Impact of GUÍA digitally enhanced genetic counseling in racially and ethnically diverse families

Background

Digital solutions are needed to support rapid increases in the application of genetic and genomic tests (GT) in diverse clinical settings and patient populations. We developed GUÍA, a bi-lingual web-based platform that facilitates disclosure of GT results. The NYCKidSeq randomized controlled trial evaluated GUÍA's impact on understanding of GT results.

Methods

NYCKidSeq enrolled diverse children with neurologic, cardiac, and immunologic conditions who underwent GT. Families were randomized to genetic counseling with GUÍA (intervention) or standard of care (SOC) genetic counseling for results disclosure. Parents/legal guardians (participants) completed surveys at baseline, post-results disclosure, and 6-months later. Survey measures assessed the primary study outcomes of perceived understanding of and confidence in explaining their child's GT results and the secondary outcome of objective understanding. We used regression models to evaluate the association between the intervention and the study outcomes.

Results

The analysis included 551 participants, 270 in the GUÍA arm and 281 in SOC. Participants' mean age was 41.1 years and 88.6% were mothers. Most participants were Hispanic/Latino(a) (46.3%), White/European American (24.5%), or Black/African American (15.8%). Participants in the GUÍA arm had significantly higher perceived understanding post-results (OR=2.8, CI[1.004,7.617], P=0.049) and maintained higher objective understanding over time (OR=1.1, CI[1.004, 1.127], P=0.038) compared to those in the SOC arm. There was no impact on perceived confidence. Hispanic/Latino(a) individuals in the GUÍA arm maintained higher perceived understanding (OR=3.9, CI[1.6, 9.3], P=0.003), confidence (OR=2.7, CI[1.021, 7.277], P=0.046), and objective understanding (OR=1.1, CI[1.009, 1.212], P=0.032) compared to SOC .

Conclusions

This trial demonstrates that GUÍA positively impacts understanding of GT results in diverse parents of children with suspected genetic conditions. These findings build a case for utilizing GUÍA to deliver complex and often ambiguous genetic results. Continued development and evaluation of digital applications in diverse populations are critical for equitably scaling GT offerings in specialty clinics.

Trial Registration

Clinicaltrials.gov identifier NCT03738098.

Improving Uptake of Cancer Genetic Risk Assessment in a Remote Tailored Risk Communication and Navigation Intervention: Large Effect Size but Room to Grow

PURPOSE

Cancer genetic risk assessment (CGRA) is recommended for women with ovarian cancer or high-risk breast cancer, yet fewer than 30% receive recommended genetic services, with the lowest rates among underserved populations. We hypothesized that compared with usual care (UC) and mailed targeted print (TP) education, CGRA uptake would be highest among women receiving a phone-based tailored risk counseling and navigation intervention (TCN).

METHODS

In this three-arm randomized trial, women with ovarian or high-risk breast cancer were recruited from statewide cancer registries in Colorado, New Jersey, and New Mexico. Participants assigned to TP received a mailed educational brochure. Participants assigned to TCN received the mailed educational brochure, an initial phone-based psychoeducational session with a health coach, a follow-up letter, and a follow-up navigation phone call.

RESULTS

Participants' average age was 61 years, 25.4% identified as Hispanic, 5.9% identified as non-Hispanic Black, and 17.5% lived in rural areas. At 6 months, more women in TCN received CGRA (18.7%) than those in TP (3%; odds ratio, 7.4; 95% CI, 3.0 to 18.3; P < .0001) or UC (2.5%; odds ratio, 8.9; 95% CI, 3.4 to 23.5; P < .0001). There were no significant differences in CGRA uptake between TP and UC. Commonly cited barriers to genetic counseling were lack of provider referral (33.7%) and cost (26.5%), whereas anticipated difficulty coping with test results (14.0%) and cost (41.2%) were barriers for genetic testing.

CONCLUSION

TCN increased CGRA uptake in a group of geographically and ethnically diverse high-risk breast and ovarian cancer survivors. Remote personalized interventions that incorporate evidence-based health communication and behavior change strategies may increase CGRA among women recruited from statewide cancer registries.

Something to chat about: An analysis of genetic counseling via asynchronous messaging following direct-to-consumer genetic testing

Advances in technology, decreasing cost of genetic testing, and growing public interest in genetics marked by an increased uptake of genetic testing, particularly direct-to-consumer genetic testing (DTC-GT), have led to an overwhelming demand for genetic counseling services. As such, various alternative service delivery models have been proposed to increase access to genetic counseling. Some service delivery models, such as asynchronous messaging, remain unexplored in the genetic counseling literature. The purpose of this study was to assess communication during genetic counseling for DTC-GT through asynchronous messaging. A thematic analysis was conducted on 34 de-identified chat transcripts between genetic counselors and clients who underwent DTC-GT. Six categories of communication were identified and were grouped based on communication sources from either the client or the genetic counselor. Categories observed in client communication were motivations for seeking DTC testing and/or genetic counseling services, questions posed to the genetic counselor, responses provided during the session, and psychosocial aspects of the session related to the clients' mental, emotional, social, and spiritual needs. Categories of communication that emerged from the genetic counselors' communications were educational aspects of the session and counseling strategies to address concerns that are not related to educational or informational needs. Most clients had specific questions about variants detected or specific conditions. Many clients asked about appropriate subsequent steps related to additional testing or medical management. Genetic counselors discussed the limitations of DTC-GT and recommendations for clinical grade testing in almost all chat transcripts. In several chats, the genetic counselor provided advice to the client related to minimizing time sorting through likely benign results and refraining from altering medical management. Results suggest that genetic counselors are able to provide genetic information to clients and respond to their mental and emotional needs through asynchronous chat following DTC-GT. Findings from this study provide initial insight into a unique genetic counseling delivery model and reveal the informational and counseling needs of clients following DTC-GT.

Exploring types of conversational agents for resolving cancer patients' questions and concerns: Analysis of 100 telephone consultations on breast cancer

OBJECTIVE

This study was conducted to investigate the types of conversational agents (CA) that can help address questions and concerns ("lay topics" [LTs]).

METHODS

We analyzed audio recordings of telephone consultations with 100 breast cancer patients and their families. (1) We identified the content and mode of expression of LTs about breast cancer raised during actual telephone consultations. (2) We checked for the presence of clue information (CI) that can help patients resolve their LTs.

RESULTS

None of the 805 LTs of the 100 callers were the same. Treatment-related questions occurred in 70 of the 100 consultations. CIs were present in 52.5% of the LTs.

CONCLUSION

The results suggest that chatbots (a type of CA) that offer CIs are more feasible than chatbots that answer each question directly in cancer consultations. Moreover, it is difficult to answer questions directly because preparing answers to all LTs in a breast cancer consultation is challenging owing to LT differences. Therefore, preparing high-quality CIs focused on treatments is required.

PRACTICE IMPLICATIONS

An increasing number of cancer patients are seeking information to resolve their LTs. CAs can help supplement the limited human resources available if they are supplied with appropriate CIs.

A scoping review of natural language processing of radiology reports in breast cancer

Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing.

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

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.

The Use of Chatbots in Oncological Care: A Narrative Review

Background

Few reports have investigated chatbots in patient care. We aimed to assess the current applications, limitations, and challenges in the literature on chatbots employed in oncological care.

Methods

We queried the PubMed database through April 2022 and included studies that investigated the use of chatbots in different phases of oncological care. The search used five different combinations of the specific terms "chatbot", "cancer", "oncology", and "conversational agent". Inclusion criteria were chatbot use in any aspect of oncological care-prevention, patient education, treatment, and surveillance.

Results

The initial search yielded 196 records, 21 of which met inclusion criteria. The identified chatbots mostly focused on breast and ovarian cancer (n=8), with the second most common being cervical cancer (n=3). Good patient satisfaction was reported among 14 of 21 chatbots. The most reported chatbot applications were cancer screening, prevention, risk stratification, treatment, monitoring, and management. Of 12 studies examining efficacy of care via chatbot, 9 demonstrated improvements compared to standard care.

Conclusion

Chatbots used for oncological care to date demonstrate high user satisfaction, and many have shown efficacy in improving patient-centered communication, accessibility to cancer-related information, and access to care. Currently, chatbots are primarily limited by the need for extensive user-testing and iterative improvement before widespread implementation.

Digital health-enabled genomics: Opportunities and challenges

Digital health solutions, with apps, virtual care, and electronic medical records, are gaining momentum across all medical disciplines, and their adoption has been accelerated, in part, by the COVID-19 pandemic. Personal wearables, sensors, and mobile technologies are increasingly being used to identify health risks and assist in diagnosis, treatment, and monitoring of health and disease. Genomics is a vanguard of digital healthcare as we witness a convergence of the fields of genomic and digital medicine. Spurred by the acute need to increase health literacy, empower patients' preference-sensitive decisions, or integrate vast amounts of complex genomic data into the clinical workflow, there has been an emergence of digital support tools in genomics-enabled care. We present three use cases that demonstrate the application of these converging technologies: digital genomics decision support tools, conversational chatbots to scale the genetic counseling process, and the digital delivery of comprehensive genetic services. These digital solutions are important to facilitate patient-centered care delivery, improve patient outcomes, and increase healthcare efficiencies in genomic medicine. Yet the development of these innovative digital genomic technologies also reveals strategic challenges that need to be addressed before genomic digital health can be broadly adopted. Alongside key evidentiary gaps in clinical and cost-effectiveness, there is a paucity of clinical guidelines, policy, and regulatory frameworks that incorporate digital health. We propose a research agenda, guided by learning healthcare systems, to realize the vision of digital health-enabled genomics to ensure its sustainable and equitable deployment in clinical care.