Evidence map›Paper›PMID 39348188›Full record

ArticleJournal of medical Internet research2024

Usability, Engagement, and Report Usefulness of Chatbot-Based Family Health History Data Collection: Mixed Methods Analysis.

Michelle Hoang Nguyen, João Sedoc, Casey Overby Taylor

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

3 authors.

Michelle Hoang NguyenInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-7689-646X
João SedocDepartment of Technology, Operations and Statistics, Stern School of Business, New York University, New York, NY, United States.ORCID 0000-0001-6369-3711
Casey Overby TaylorInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0001-9302-5968

Funding

NRSA Training CoreTL1TR003100 · NCATS · JOHNS HOPKINS UNIVERSITY · PI SAHA, SOMNATH · 2019 to 2023
$4.5M
Clinical Decision Support for Unsolicited Genomic ResultsR35HG010714 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI TAYLOR, CASEY OVERBY · 2020 to 2024
$2.8M
NCATS NIH HHS TL1 TR003100NHGRI NIH HHS R35 HG010714
6 · The paper itself

Abstract

backgroundFamily health history (FHx) is an important predictor of a person's genetic risk but is not collected by many adults in the United States.

objectiveThis study aims to test and compare the usability, engagement, and report usefulness of 2 web-based methods to collect FHx.

methodsThis mixed methods study compared FHx data collection using a flow-based chatbot (KIT; the curious interactive test) and a form-based method. KIT's design was optimized to reduce user burden. We recruited and randomized individuals from 2 crowdsourced platforms to 1 of the 2 FHx methods. All participants were asked to complete a questionnaire to assess the method's usability, the usefulness of a report summarizing their experience, user-desired chatbot enhancements, and general user experience. Engagement was studied using log data collected by the methods. We used qualitative findings from analyzing free-text comments to supplement the primary quantitative results.

resultsParticipants randomized to KIT reported higher usability than those randomized to the form, with a mean System Usability Scale score of 80.2 versus 61.9 (P<.001), respectively. The engagement analysis reflected design differences in the onboarding process. KIT users spent less time entering FHx information and reported more conditions than form users (mean 5.90 vs 7.97 min; P=.04; and mean 7.8 vs 10.1 conditions; P=.04). Both KIT and form users somewhat agreed that the report was useful (Likert scale ratings of 4.08 and 4.29, respectively). Among desired enhancements, personalization was the highest-rated feature (188/205, 91.7% rated medium- to high-priority). Qualitative analyses revealed positive and negative characteristics of both KIT and the form-based method. Among respondents randomized to KIT, most indicated it was easy to use and navigate and that they could respond to and understand user prompts. Negative comments addressed KIT's personality, conversational pace, and ability to manage errors. For KIT and form respondents, qualitative results revealed common themes, including a desire for more information about conditions and a mutual appreciation for the multiple-choice button response format. Respondents also said they wanted to report health information beyond KIT's prompts (eg, personal health history) and for KIT to provide more personalized responses.

conclusionsWe showed that KIT provided a usable way to collect FHx. We also identified design considerations to improve chatbot-based FHx data collection: First, the final report summarizing the FHx collection experience should be enhanced to provide more value for patients. Second, the onboarding chatbot prompt may impact data quality and should be carefully considered. Finally, we highlighted several areas that could be improved by moving from a flow-based chatbot to a large language model implementation strategy.

Indexed as

Medical History TakingAdultData CollectionFamily HealthFemaleHumansInternetMaleMiddle AgedSurveys and Questionnaireschatbotsconversational agentscrowdsourcingdigital health toolsengagementevaluationfamily health historymixed methodsreport usefulnessusability

Identifiers

PMID39348188
PMCPMC11474129

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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 Socratic graph.