Using chatbots to screen for heritable cancer syndromes in patients undergoing routine colonoscopy

Depiction of conversational agents as health professionals: a scoping review

OBJECTIVE

The purpose of this scoping review was to examine the depiction of conversational agents as health professionals. We identified the professional characteristics that are used with these depictions and determined the prevalence of these characteristics among conversational agents that are used for health care.

INTRODUCTION

The depiction of conversational agents as health professionals has implications for both the users and the developers of these programs. For this reason, it is important to know more about these depictions and how they are implemented in practical settings.

INCLUSION CRITERIA

This review included scholarly literature on conversational agents that are used for health care. It focused on conversational agents designed for patients and health seekers, not health professionals or trainees. Conversational agents that address physical and/or mental health care were considered, as were programs that promote healthy behaviors.

METHODS

This review was conducted in accordance with JBI methodology for scoping reviews. The databases searched included MEDLINE (PubMed), Embase, CINAHL with Full Text (EBSCOhost), Scopus, Web of Science, ACM Guide to Computing Literature (Association for Computing Machinery Digital Library), and IEEE Xplore (IEEE). The main database search was conducted in June 2021, and an updated search was conducted in January 2022. Extracted data included characteristics of the report, basic characteristics of the conversational agent, and professional characteristics of the conversational agent. Extracted data were summarized using descriptive statistics. Results are presented in a narrative summary and accompanying tables.

RESULTS

A total of 38 health-related conversational agents were identified across 41 reports. Six of these conversational agents (15.8%) had professional characteristics. Four conversational agents (10.5%) had a professional appearance in which they displayed the clothing and accessories of health professionals and appeared in professional settings. One conversational agent (2.6%) had a professional title (Dr), and 4 conversational agents (10.5%) were described as having professional roles. Professional characteristics were more common among embodied vs disembodied conversational agents.

CONCLUSIONS

The results of this review show that the depiction of conversational agents as health professionals is not particularly common, although it does occur. More discussion is needed on the potential ethical and legal issues surrounding the depiction of conversational agents as health professionals. Future research should examine the impact of these depictions, as well as people's attitudes toward them, to better inform recommendations for practice.

Retrospective chart analysis to determine the impact of a patient-facing digital risk stratification tool combined with a clinical screener for hereditary cancer genetic risk assessment triage in a community oncology clinic

The purpose of this study was to evaluate the utility of adding a clinical screener to the patient-facing digital risk stratification tool triage process for the identification of patients eligible for a genetic risk assessment for hereditary cancer. Digital risk stratification entries were retrospectively reviewed to determine the overall number of patients eligible for genetic risk assessment. These were also analyzed to determine how many patients were re-contacted by the clinical screener, and how many of those recontacted patients met criteria after their personal and family history was revised by the clinical screener. There was an 89.9% digital risk stratification triage tool completion rate, with 22.6% requiring contact from the clinical screener. Of the 640 patients who completed the digital tool, 5.9% met criteria for testing after their personal and/or family history was revised by the clinical screener. Overall, 51.1% of patients met criteria for a genetic risk assessment. The addition of a clinical screener further increased identification of patients eligible for genetic risk assessment. About half of patients who met criteria after being contacted by the clinical screener met criteria based on their personal diagnosis of cancer alone. Incorporation of a clinical screener to the digital screening process may serve to reduce barriers to patient completion of the tool and increase rates of patient identification for cancer genetic services.

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.

A Randomized Trial Comparing the Effectiveness of Pre-test Genetic Counseling Using an Artificial Intelligence Automated Chatbot and Traditional In-person Genetic Counseling in Women Newly Diagnosed with Breast Cancer

BACKGROUND

Alternative service delivery models are critically needed to address the increasing demand for genetics services and limited supply of genetics experts available to provide pre-test counseling.

METHODS

We conducted a prospective randomized controlled trial of women with stage 0-III breast cancer not meeting National Comprehensive Cancer Network (NCCN) criteria for genetic testing. Patients were randomized to pre-test counseling with a Chatbot or a certified genetic counselor (GC). Participants completed a questionnaire assessing their knowledge of breast cancer genetics and a survey assessing satisfaction with their decision regarding pre-test counseling.

RESULTS

A total of 39 patients were enrolled and 37 were randomized to genetic counseling with an automated Chatbot or a GC; 19 were randomized to Chatbot and 18 to traditional genetic counseling, and 13 (38.2%) had a family member with breast cancer but did not meet NCCN criteria. All patients opted to undergo genetic testing. Testing revealed six pathogenic variants in five patients (13.5%): CHEK2 (n = 2), MSH3 (n = 1), MUTYH (n = 1), and BRCA1 and HOXB13 (n = 1). No patients had a delay in time-to-treatment due to genetic testing turnaround time, nor did any patients undergo additional risk reducing surgery. There was no significant difference in median knowledge score between Chatbot and traditional counseling (11 vs. 12, p = 0.09) or in median patient satisfaction score (30 vs. 30, p = 0.19).

CONCLUSION

Satisfaction and comprehension in patients with breast cancer undergoing pre-test genetic counseling using an automated Chatbot is comparable to in-person genetic testing. Utilization of this technology can offer improved access to care and a much-needed alternative for pre-test counseling.

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.

Chatbot Artificial Intelligence for Genetic Cancer Risk Assessment and Counseling: A Systematic Review and Meta-Analysis

PURPOSE

Most individuals with a hereditary cancer syndrome are unaware of their genetic status to underutilization of hereditary cancer risk assessment. Chatbots, or programs that use artificial intelligence to simulate conversation, have emerged as a promising tool in health care and, more recently, as a potential tool for genetic cancer risk assessment and counseling. Here, we evaluated the existing literature on the use of chatbots in genetic cancer risk assessment and counseling.

METHODS

A systematic review was conducted using key electronic databases to identify studies which use chatbots for genetic cancer risk assessment and counseling. Eligible studies were further subjected to meta-analysis.

RESULTS

Seven studies met inclusion criteria, evaluating five distinct chatbots. Three studies evaluated a chatbot that could perform genetic cancer risk assessment, one study evaluated a chatbot that offered patient counseling, and three studies included both functions. The pooled estimated completion rate for the genetic cancer risk assessment was 36.7% (95% CI, 14.8 to 65.9). Two studies included comprehensive patient characteristics, and none involved a comparison group. Chatbots varied as to the involvement of a health care provider in the process of risk assessment and counseling.

CONCLUSION

Chatbots have been used to streamline genetic cancer risk assessment and counseling and hold promise for reducing barriers to genetic services. Data regarding user and nonuser characteristics are lacking, as are data regarding comparative effectiveness to usual care. Future research may consider the impact of chatbots on equitable access to genetic services.

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.

Patient decision support resources inform decisions about cancer susceptibility genetic testing and risk management: a systematic review of patient impact and experience

Background

Patients with genetic cancer susceptibility are presented with complex management options involving difficult decisions, for example about genetic testing, treatment, screening and risk-reducing surgery/medications. This review sought to explore the experience of patients using decision support resources in this context, and the impact on decision-making outcomes.

Methods

Systematic review of quantitative, qualitative and mixed-methods studies involving adults with or without cancer who used a decision support resource pre- or post-genetic test for any cancer susceptibility. To gather a broad view of existing resources and gaps for development, digital or paper-based patient resources were included and not limited to decision aids. Narrative synthesis was used to summarise patient impact and experience.

Results

Thirty-six publications describing 27 resources were included. Heterogeneity of resources and outcome measurements highlighted the multiple modes of resource delivery and personal tailoring acceptable to and valued by patients. Impact on cognitive, emotional, and behavioural outcomes was mixed, but mainly positive. Findings suggested clear potential for quality patient-facing resources to be acceptable and useful.

Conclusions

Decision support resources about genetic cancer susceptibility are likely useful to support decision-making, but should be co-designed with patients according to evidence-based frameworks. More research is needed to study impact and outcomes, particularly in terms of longer term follow-up to identify whether patients follow through on decisions and whether any increased distress is transient. Innovative, streamlined resources are needed to scale up delivery of genetic cancer susceptibility testing for patients with cancer in mainstream oncology clinics. Tailored patient-facing decision aids should also be made available to patients identified as carriers of a pathogenic gene variant that increases future cancer risks, to complement traditional genetic counselling.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020220460, identifier: CRD42020220460.

Web-based tool for cancer family history collection: A prospective randomized controlled trial

OBJECTIVES

Approximately 1% of individuals have a hereditary cancer predisposition syndrome, however, the majority are not aware. Collecting a cancer family history (CFH) can triage patients to receive genetic testing. To rigorously assess different methods of CFH collection, we compared a web-based tool (WBT) to usual care (clinician collects CFH) in a randomized controlled trial.

METHODS

New gynecologic oncology patients (seen 9/2019-9/2021) were randomized to one of three arms in a 2:2:1 allocation ratio: 1) usual care clinician CFH collection, 2) WBT completed at home, or 3) WBT completed in office. The WBT generated a cancer-focused pedigree and scores on eight validated cancer risk models. The primary outcome was collection of an adequate CFH (based on established guidelines) with usual care versus the WBT.

RESULTS

We enrolled 250 participants (usual care - 110; WBT home - 105; WBT office - 35 [closed early due to COVID-19]). Within WBT arms, 109 (78%) participants completed the tool, with higher completion for office versus home (33 [94%] vs. 76 [72%], P = 0.008). Among participants completing the WBT, 63 (58%) had an adequate CFH versus 5 (5%) for usual care (P < 0.001). Participants completing the WBT were significantly more likely to complete genetic counseling (34 [31%] vs. 15 [14%], P = 0.002) and genetic testing (20 [18%] vs. 9 [8%], P = 0.029). Participant and provider WBT experience was favorable.

CONCLUSIONS

WBTs for CFH collection are a promising application of health information technology, resulting in more comprehensive CFH and a significantly greater percentage of participants completing genetic counseling and testing.

Implementing digital systems to facilitate genetic testing for hereditary cancer syndromes: An observational study of 4 clinical workflows

PURPOSE

National efforts have prioritized the identification of effective methods for increasing case ascertainment and delivery of evidence-based health care for individuals at elevated risk for hereditary cancers.

METHODS

This study examined the uptake of genetic counseling and testing following the use of a digital cancer genetic risk assessment program implemented at 27 health care sites in 10 states using 1 of 4 clinical workflows: (1) traditional referral, (2) point-of-care scheduling, (3) point-of-care counseling/telegenetics, and (4) point-of-care testing.

RESULTS

In 2019, 102,542 patients were screened and 33,113 (32%) were identified as at high risk and meeting National Comprehensive Cancer Network genetic testing criteria for hereditary breast and ovarian cancer, Lynch syndrome, or both. Among those identified at high risk, 5147 (16%) proceeded with genetic testing. Genetic counseling uptake was 11% among the sites with workflows that included seeing a genetic counselor before testing, with 88% of patients proceeding with genetic testing after counseling. Uptake of genetic testing across sites varied significantly by clinical workflow (6% referral, 10% point-of-care scheduling, 14% point-of-care counseling/telegenetics, and 35% point-of-care testing, P < .0001).

CONCLUSION

Study findings highlight the potential heterogeneity of effectiveness attributable to different care delivery approaches for implementing digital hereditary cancer risk screening programs.

"Development and Implementation of Novel Chatbot-based Genomic Research Consent"

Objective

To conduct a retrospective analysis comparing traditional human-based consenting to an automated chat-based consenting process.

Materials and Methods

We developed a new chat-based consent using our IRB-approved consent forms. We leveraged a previously developed platform (GiaⓇ, or "Genetic Information Assistant") to deliver the chat content to candidate participants. The content included information about the study, educational information, and a quiz to assess understanding. We analyzed 144 families referred to our study during a 6-month time period. A total of 37 families completed consent using the traditional process, while 35 families completed consent using Gia.

Results

Engagement rates were similar between both consenting methods. The median length of the consent conversation was shorter for Gia users compared to traditional (44 vs. 76 minutes). Additionally, the total time from referral to consent completion was faster with Gia (5 vs. 16 days). Within Gia, understanding was assessed with a 10-question quiz that most participants (96%) passed. Feedback about the chat consent indicated that 86% of participants had a positive experience.

Discussion

Using Gia resulted in time savings for both the participant and study staff. The chatbot enables studies to reach more potential candidates. We identified five key features related to human-centered design for developing a consent chat.

Conclusion

This analysis suggests that it is feasible to use an automated chatbot to scale obtaining informed consent for a genomics research study. We further identify a number of advantages when using a chatbot.

Implementation of a Population-Based Cancer Family History Screening Program for Lynch Syndrome

OBJECTIVES

Lynch syndrome increases risks for colorectal and other cancers. Though published Lynch syndrome cancer risk-management guidelines are effective for risk-reduction, the condition remains under-recognized. The Cancer Genetics Program at an academic medical center implemented a population-based cancer family history screening program, Detecting Unaffected Individuals with Lynch syndrome, to aid in identification of individuals with Lynch syndrome.

METHODS

In this retrospective cohort study, simple cancer family history screening questionnaires were used to identify those at risk for Lynch syndrome. Program navigators triaged and educated those who screened positive about hereditary cancer, and genetic counseling and testing services, offering genetic counseling if eligible. Genetic counseling was provided primarily via telephone. Genetic counselors performed hereditary cancer risk assessment and offered genetic testing via hereditary cancer panels to those eligible. Remote service delivery models via telephone genetic counseling and at-home saliva testing were used to increase access to medical genetics services.

RESULTS

This program screened 212,827 individuals, over half of whom were considered underserved, and identified 133 clinically actionable genetic variants associated with hereditary cancer. Of these, 47 (35%) were associated with Lynch syndrome while notably, 70 (53%) were not associated with hereditary colorectal cancer. Of 3,344 patients offered genetic counseling after initial triage, 2,441 (73%) elected to schedule the appointment and 1,775 individuals (73%) completed genetic counseling. Among underserved patients, telephone genetic counseling completion rates were significantly higher than in-person appointment completion rates (P < .05). While remote service delivery improved appointment completion rates, challenges with genetic test completion using at-home saliva sample collection kits were observed, with 242 of 1592 individuals (15%) not completing testing.

CONCLUSION

Population-based cancer family history screening and navigation can help identify individuals with hereditary cancer syndromes across diverse patient populations, but logistics of certain downstream service delivery models can impact outcomes.

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.

Mainstreaming germline genetic testing for patients with pancreatic cancer increases uptake

Germline genetic testing is recommended for all patients with pancreatic cancer (PC) but uptake rates are low. We implemented a mainstreaming program in oncology clinics to increase testing for PC patients. Genetic counselors trained oncology providers to offer a standardized multigene panel and obtain informed consent using an educational video. Pre-test genetic counseling was available upon request. Otherwise, patients with identified pathogenic variants, strong family history, or questions regarding their results were referred for post-test genetic counseling. We measured rates of testing and genetic counseling visits. From September 2019 to April 2021, 245 patients with PC underwent genetic testing. This represents a 6.5-fold increase in germline testing volume (95% confidence interval 5.2-8.1) compared to previous years. At least one pathogenic or likely pathogenic variant (PV/LPV) was found in 34 (13.9%) patients, including 17 (6.9%) PV/LPVs in high or moderate risk genes and 18 (7.3%) in low risk or recessive genes. Five (2.0%) PVs had implications on treatment selection. 22 of the positive patients (64.7%) and an additional 8 PC patients (1 negative, 3 VUS, and 4 pre-test) underwent genetic counseling during the study period. Genetic counselors saw 2.0 PC patients/month prior to this project, 1.6 PC patients/month during this project, and would have seen 2.2 PC patients/month if all patients with pathogenic variants attended post-test counseling. Conclusions Mainstreaming genetic testing expands access for PC patients without overwhelming genetic counseling resources.

Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study

Background

Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system in a natural language. However, only little comprehensive research is conducted about patients' perceptions and possible applications of virtual assistant in healthcare with cancer patients. This research aims to investigate the key acceptance factors and value-adding use cases of a virtual assistant for patients diagnosed with cancer.

Methods

Qualitative interviews with eight former patients and four doctors of a Dutch radiotherapy institute were conducted to determine what acceptance factors they find most important for a virtual assistant and gain insights into value-adding applications. The unified theory of acceptance and use of technology (UTAUT) was used to structure perceptions and was inductively modified as a result of the interviews. The subsequent research model was triangulated via an online survey with 127 respondents diagnosed with cancer. A structural equation model was used to determine the relevance of acceptance factors. Through a multigroup analysis, differences between sample subgroups were compared.

Results

The interviews found support for all factors of the UTAUT: performance expectancy, effort expectancy, social influence and facilitating conditions. Additionally, self-efficacy, trust, and resistance to change, were added as an extension of the UTAUT. Former patients found a virtual assistant helpful in receiving information about logistic questions, treatment procedures, side effects, or scheduling appointments. The quantitative study found that the constructs performance expectancy (ß = 0.399), effort expectancy (ß = 0.258), social influence (ß = 0.114), and trust (ß = 0.210) significantly influenced behavioral intention to use a virtual assistant, explaining 80% of its variance. Self-efficacy (ß = 0.792) acts as antecedent of effort expectancy. Facilitating conditions and resistance to change were not found to have a significant relationship with user intention.

Conclusions

Performance and effort expectancy are the leading determinants of virtual assistant acceptance. The latter is dependent on a patient’s self-efficacy. Therefore, including patients during the development and introduction of a VA in cancer treatment is important. The high relevance of trust indicates the need for a reliable, secure service that should be promoted as such. Social influence suggests using doctors in endorsing the VA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08189-7.

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.

Use of a chatbot to increase uptake of cascade genetic testing

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.

Digital technologies in cancer care: a review from the clinician's perspective

Physicians are increasingly utilizing digital health technologies (DHT) such as smartphone applications, network-enabled wearable devices, web-based communication platforms, videoconferencing, chatbots, artificial intelligence and virtual reality to improve access to, and quality of, care. DHT aid in cancer screening, patient education, shared decision-making, promotion of positive health habits, symptom monitoring and intervention, patient-provider communication, provision of psychological support and delivery of effective survivorship care. This narrative review outlines how physicians may utilize digital health to improve or augment their delivery of cancer care. For the full potential of DHT to be realized, experts must develop appropriate solutions to issues surrounding the regulation, liability, quality, security, equity and reimbursement of DHT.