The ENGAGE study: evaluation of a conversational virtual agent that provides tailored pre-test genetic education to cancer patients.
AI for cancer survivorship
AI in cancers
AI in healthcare
AI-assisted patient education
Conversational AI
Scaling clinical processes with conversational AI
Journal
Journal of cancer survivorship : research and practice
ISSN: 1932-2267
Titre abrégé: J Cancer Surviv
Pays: United States
ID NLM: 101307557
Informations de publication
Date de publication:
08 Dec 2023
08 Dec 2023
Historique:
received:
26
05
2023
accepted:
31
10
2023
medline:
8
12
2023
pubmed:
8
12
2023
entrez:
8
12
2023
Statut:
aheadofprint
Résumé
Novel approaches are needed to ensure all patients with cancer have access to quality genetic education before genetic testing to enable informed treatment decisions. The purpose of this study was to test the use of an artificial intelligence (AI) intervention for the delivery of genetic education by non-genetic providers to patients with cancer undergoing active treatment. A conversational AI-based application was developed on the HealthFAX platform to provide tailored genetic education to patients with cancer and tested at Johns Hopkins Hospital between April 2021 and Feb 2022. Patients' responses around the adoption, use, and experience of the AI application were assessed. Out of 64 individuals who consented to the study, 51 accessed the tool. The responding participants had a mean age of 61 years (ranging from 30-90 years) with 39 individuals undergoing active treatment for breast cancer and 12 for advanced prostate cancer. All patients chose to complete the tool at home. The median time between study enrollment and AI application initiation was 1 day, and the median time to complete the application was 24 min. All participants in their survey responses felt that the tool was secure, easy to use, liked the convenience of viewing it at home, and felt it provided valuable information. Eighteen percent of participants viewed the application with a family member. Ninety-eight percent of the participants completed their genetic education prior to receiving their test results. In 16%, a pathogenic variant was identified. The 51 patients who adopted the AI application were highly satisfied with its usability and convenience. Our results support the continued evaluation of this cost-effective AI application in a large-scale study. Tailored pre-test genetic education can be successfully delivered to patients with cancer undergoing active treatment via an AI application at their convenience.
Identifiants
pubmed: 38064163
doi: 10.1007/s11764-023-01495-x
pii: 10.1007/s11764-023-01495-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for genetic/familial high-risk assessment: colorectal V..2022. © National Comprehensive Cancer Network, Inc. 2022. All rights reserved. Accessed April 6, 2023. To view the most recent and complete version of the guideline, go online to NCCN.org.
Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for genetic/familial high-risk assessment: breast, ovarian, and pancreatic V.3.2023. © National Comprehensive Cancer Network, Inc. 2023. All rights reserved. Accessed April 6, 2023. To view the most recent and complete version of the guideline, go online to NCCN.org.
Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for prostate cancer early detection V.1.2023. © National Comprehensive Cancer Network, Inc. 2023. All rights reserved. Accessed January 19, 2023. To view the most recent and complete version of the guideline, go online to NCCN.org.
Riley BD, et al. Essential elements of genetic cancer risk assessment, counseling, and testing: updated recommendations of the National Society of Genetic Counselors. J Genet Couns. 2012. https://doi.org/10.1007/s10897-011-9462-x .
doi: 10.1007/s10897-011-9462-x
pubmed: 22566244
Penon-Portmann M, et al. Genetics workforce: distribution of genetics services and challenges to health care in California. Genet Med. 2020. https://doi.org/10.1038/s41436-019-0628-5 .
doi: 10.1038/s41436-019-0628-5
pubmed: 31767985
Robson ME, et al. American Society of Clinical Oncology policy statement update: genetic and genomic testing for cancer susceptibility. J Clin Oncol. 2015. https://doi.org/10.1200/jco.2015.63.0996 .
doi: 10.1200/jco.2015.63.0996
pubmed: 26324357
National Accreditation Program for Breast Centers Standards Manual. American College of Surgeons. 2018. https://www.facs.org/media/pofgxojm/napbc_standards_manual_2018.pdf . Accessed Apr 2023.
Frey MK, et al. Cascade testing for hereditary cancer syndromes: should we move toward direct relative contact? A systematic review and meta-analysis. J Clin Oncol. 2022. https://doi.org/10.1200/jco.22.00303 .
doi: 10.1200/jco.22.00303
pubmed: 35960887
Domchek SM, et al. Association of risk-reducing surgery in BRCA1 or BRCA2 mutation carriers with cancer risk and mortality. JAMA. 2010. https://doi.org/10.1001/jama.2010.1237 .
doi: 10.1001/jama.2010.1237
pubmed: 20810374
pmcid: 2948529
Gross AL, Blot WJ, Visvanathan K. BRCA1 and BRCA2 testing in medically underserved medicare beneficiaries with breast or ovarian cancer. JAMA. 2018. https://doi.org/10.1001/jama.2018.8258 .
doi: 10.1001/jama.2018.8258
pubmed: 30512090
pmcid: 6583477
Reid S, et al. Disparities in BRCA counseling across providers in a diverse population of young breast cancer survivors. Genet Med. 2020. https://doi.org/10.1038/s41436-020-0762-0 .
doi: 10.1038/s41436-020-0762-0
pubmed: 32606442
pmcid: 7606791
Gutierrez AM, et al. Examining access to care in clinical genomic research and medicine: experiences from the CSER Consortium. J Clin Transl Sci. 2021. https://doi.org/10.1017/cts.2021.855 .
doi: 10.1017/cts.2021.855
pubmed: 34888063
pmcid: 8634302
Choi JJ, et al. The role of race and insurance status in access to genetic counseling and testing among high-risk breast cancer patients. Oncologist. 2022. https://doi.org/10.1093/oncolo/oyac132 .
doi: 10.1093/oncolo/oyac132
pubmed: 36124631
pmcid: 9526492
Kurian AW, et al. Gaps in incorporating germline genetic testing into treatment decision-making for early-stage breast cancer. J Clin Oncol. 2017. https://doi.org/10.1200/jco.2016.71.6480 .
doi: 10.1200/jco.2016.71.6480
pubmed: 28402748
pmcid: 5501363
Villegas C, Haga SB. Access to genetic counselors in the Southern United States. J Pers Med. 2019. https://doi.org/10.3390/jpm9030033 .
doi: 10.3390/jpm9030033
pubmed: 31266141
pmcid: 6789777
Reid S, et al. An overview of genetic services delivery for hereditary breast cancer. Breast Cancer Res Treat. 2022. https://doi.org/10.1007/s10549-021-06478-z .
doi: 10.1007/s10549-021-06478-z
pubmed: 35079980
pmcid: 8789372
Vadaparampil ST, et al. Pre-test genetic counseling services for hereditary breast and ovarian cancer delivered by non-genetics professionals in the state of Florida. Clin Genet. 2015. https://doi.org/10.1111/cge.12405 .
doi: 10.1111/cge.12405
pubmed: 25640009
pmcid: 4522387
Cragun D, et al. A web-based tool to automate portions of pretest genetic counseling for inherited cancer. J Natl Compr Canc Netw. 2020. https://doi.org/10.6004/jnccn.2020.7546 .
doi: 10.6004/jnccn.2020.7546
pubmed: 32634774
Watson CH, et al. Video-assisted genetic counseling in patients with ovarian, fallopian and peritoneal carcinoma. Gynecol Oncol. 2016. https://doi.org/10.1016/j.ygyno.2016.07.094 .
doi: 10.1016/j.ygyno.2016.07.094
pubmed: 27416795
pmcid: 9813871
Schmidlen T, et al. Patient assessment of chatbots for the scalable delivery of genetic counseling. J Genet Couns. 2019. https://doi.org/10.1002/jgc4.1169 .
doi: 10.1002/jgc4.1169
pubmed: 31549758
Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Health. 2017. https://doi.org/10.2196/mental.7785 .
doi: 10.2196/mental.7785
pubmed: 28588005
pmcid: 5478797
Philip P, et al. Virtual human as a new diagnostic tool, a proof of concept study in the field of major depressive disorders. Sci Rep. 2017. https://doi.org/10.1038/srep42656 .
doi: 10.1038/srep42656
pubmed: 28970515
pmcid: 5624884
Dhinagaran DA, et al. Conversational agent for healthy lifestyle behavior change: web-based feasibility study. JMIR Form Res. 2021. https://doi.org/10.2196/27956 .
doi: 10.2196/27956
pubmed: 34870611
pmcid: 8686401
Hauser-Ulrich S, et al. A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA): pilot randomized controlled trial. JMIR Mhealth Uhealth. 2020. https://doi.org/10.2196/15806 .
doi: 10.2196/15806
pubmed: 32242820
pmcid: 7165314
Chavez-Yenter D, et al. Patient interactions with an automated conversational agent delivering pretest genetics education: descriptive study. J Med Internet Res. 2021. https://doi.org/10.2196/29447 .
doi: 10.2196/29447
pubmed: 34792472
pmcid: 8663668
Nazareth S, et al. Hereditary cancer risk using a genetic chatbot before routine care visits. Obstet Gynecol. 2021. https://doi.org/10.1097/aog.0000000000004596 .
doi: 10.1097/aog.0000000000004596
pubmed: 34735417
pmcid: 8594498
Heald B, et al. Using chatbots to screen for heritable cancer syndromes in patients undergoing routine colonoscopy. J Med Genet. 2020. https://doi.org/10.1136/jmedgenet-2020-107294 .
doi: 10.1136/jmedgenet-2020-107294
pubmed: 33168571
McLellan S, Muddimer A, Peres SC. The effect of experience on system usability scale ratings. J Usability Stud. 2012;7:56–67.
Dobosh, MA. The Sage encyclopedia of communication research methods. In: Allen M, editor. SAGE Publications, Inc. 2017. p. 1702.