Evidence map›Paper›PMID 34792472›Full record

ArticleJournal of medical Internet research2021

Patient Interactions With an Automated Conversational Agent Delivering Pretest Genetics Education: Descriptive Study.

Daniel Chavez-Yenter, Kadyn E Kimball, Wendy Kohlmann, Rachelle Lorenz Chambers, Richard L Bradshaw, Whitney F Espinel, Michael Flynn, Amanda Gammon, Eric Goldberg, Kelsi J Hagerty and 11 more

2 registry-linked trialsAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07073898 nacompletednot on this mapstarted 2025, after this paper: background citation

Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial

TypeinterventionalSponsorUniversity of UtahRan2025 to 2026Enrolled65ConditionsLung CancerArmsRepeated Text Messages (TM+), Conversational Agent (CA), Educational Video, Proactive Patient Navigation (PPN), Reactive Patient Navigation (RPN)
NCT07410689 nanot yet recruitingnot on this mapstarted 2026, after this paper: background citation

SAFE.AI: Developing and Testing an AI-based Hybrid Chatbot for Financial Empowerment in Rural Cancer Care

TypeinterventionalSponsorUniversity of UtahRan2026 to 2027Enrolled60ConditionsCancer, Financial ToxicityArmsRule-Based Chatbot, Hybrid Chatbot
3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

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  10. Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026
    Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Daniel Chavez-YenterDepartment of Communication, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0001-7764-4443
Kadyn E KimballCancer Control and Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT, United States.ORCID 0000-0002-2821-3046
Wendy KohlmannHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0002-9134-9640
Rachelle Lorenz ChambersPerlmutter Cancer Center, New York University Langone Health, New York, NY, United States.ORCID 0000-0003-2478-8835
Richard L BradshawDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0001-7363-0327
Whitney F EspinelHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0002-7159-9239
Michael FlynnUniversity of Utah Health, Salt Lake City, UT, United States.ORCID 0000-0003-3203-1437
Amanda GammonHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0001-9189-7156
Eric GoldbergDepartment of Medicine, New York University Grossman School of Medicine, New York University, New York, NY, United States.ORCID 0000-0003-1182-8084
Kelsi J HagertyHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0002-6464-1785
Rachel HessDepartment of Population Health Sciences, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0003-2545-8504
Cecilia KesslerHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0003-3130-216X
Rachel MonahanPerlmutter Cancer Center, New York University Langone Health, New York, NY, United States.ORCID 0000-0003-1000-2790
Danielle TemaresPerlmutter Cancer Center, New York University Langone Health, New York, NY, United States.ORCID 0000-0002-6771-8725
Katie TobikHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0001-5760-5981
Devin M MannDepartment of Population Health, New York University Grossman School of Medicine, New York University, New York, NY, United States.ORCID 0000-0002-2099-0852
Kensaku KawamotoDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0003-4282-9338
Guilherme Del FiolDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0001-9954-6799
Saundra S BuysHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0003-4120-5697
Ophira GinsburgPerlmutter Cancer Center, New York University Langone Health, New York, NY, United States.ORCID 0000-0002-1384-2675
Kimberly A KaphingstDepartment of Communication, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0003-2668-9080

Funding

Leveraging an electronic medical record infrastructure to identify primary care patients eligible for genetic testing for hereditary cancer and evaluate novel cancer genetics service delivery modelsU01CA232826 · NCI · UNIVERSITY OF UTAH · PI KAPHINGST, KIMBERLY A, SIGIREDDI, MEENAKSHI · 2018 to 2022
$5.5M
Scalable Clinical Decision Support for Individualized Cancer Risk ManagementU24CA204800 · NCI · UNIVERSITY OF UTAH · PI DEL FIOL, GUILHERME, KAWAMOTO, KENSAKU · 2017 to 2021
$3.7M
Promoting Real World Implementation of an Evidence-Based Weight Management Intervention in Primary CareR18DK123372 · NIDDK · UNIVERSITY OF UTAH · PI CONROY, MARGARET B, KAWAMOTO, KENSAKU · 2020 to 2024
$3.1M
NCI NIH HHS U01 CA232826NCI NIH HHS U24 CA204800NIDDK NIH HHS R18 DK123372
6 · The paper itself

Abstract

backgroundCancer genetic testing to assess an individual's cancer risk and to enable genomics-informed cancer treatment has grown exponentially in the past decade. Because of this continued growth and a shortage of health care workers, there is a need for automated strategies that provide high-quality genetics services to patients to reduce the clinical demand for genetics providers. Conversational agents have shown promise in managing mental health, pain, and other chronic conditions and are increasingly being used in cancer genetic services. However, research on how patients interact with these agents to satisfy their information needs is limited.

objectiveOur primary aim is to assess user interactions with a conversational agent for pretest genetics education.

methodsWe conducted a feasibility study of user interactions with a conversational agent who delivers pretest genetics education to primary care patients without cancer who are eligible for cancer genetic evaluation. The conversational agent provided scripted content similar to that delivered in a pretest genetic counseling visit for cancer genetic testing. Outside of a core set of information delivered to all patients, users were able to navigate within the chat to request additional content in their areas of interest. An artificial intelligence-based preprogrammed library was also established to allow users to ask open-ended questions to the conversational agent. Transcripts of the interactions were recorded. Here, we describe the information selected, time spent to complete the chat, and use of the open-ended question feature. Descriptive statistics were used for quantitative measures, and thematic analyses were used for qualitative responses.

resultsWe invited 103 patients to participate, of which 88.3% (91/103) were offered access to the conversational agent, 39% (36/91) started the chat, and 32% (30/91) completed the chat. Most users who completed the chat indicated that they wanted to continue with genetic testing (21/30, 70%), few were unsure (9/30, 30%), and no patient declined to move forward with testing. Those who decided to test spent an average of 10 (SD 2.57) minutes on the chat, selected an average of 1.87 (SD 1.2) additional pieces of information, and generally did not ask open-ended questions. Those who were unsure spent 4 more minutes on average (mean 14.1, SD 7.41; P=.03) on the chat, selected an average of 3.67 (SD 2.9) additional pieces of information, and asked at least one open-ended question.

conclusionsThe pretest chat provided enough information for most patients to decide on cancer genetic testing, as indicated by the small number of open-ended questions. A subset of participants were still unsure about receiving genetic testing and may require additional education or interpersonal support before making a testing decision. Conversational agents have the potential to become a scalable alternative for pretest genetics education, reducing the clinical demand on genetics providers.

Indexed as

Artificial IntelligenceCommunicationChronic DiseaseGenetic CounselingHumansMental Healthcancergenetic testingmobile phonesmartphoneuser interactionvirtual conversational agent

Identifiers

PMID34792472
PMCPMC8663668

What OpenQuestion holds

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LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.