ArticleJournal of medical Internet research2022
Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Authentication status and AI triage concordance among care seekers in a US health system.npj health systems · 2026Article
- Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study.JMIR biomedical engineering · 2026Article
- Metacognition and diagnostic decision-making: short "blips" of knowledge and the consequences of overconfidence.Cognitive research: principles and implications · 2026Article
- Evaluating the accuracy of ChatGPT model versions for giving care-seeking advice.Communications medicine · 2026Article
- Evidence for Digital Health Tools Designed to Support the Triage of Musculoskeletal Conditions in Primary, Urgent, and Emergency Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Attrition and representativeness in development and validation of online symptom checkers-a case study on theFrontiers in artificial intelligence · 2026Article
- Why do I use generative artificial intelligence (GenAI) to seek health information? A perceptual perspective of GenAI users.Frontiers in public health · 2026Article
- Patient Navigation Behavior in a Symptom-Based Self-Triage Mobile App for Direct-to-Consumer Urgent Care: Retrospective Observational Study.Journal of medical Internet research · 2025Observational
- Factors Influencing the Use of Online Symptom Checkers in the United Kingdom: Cross-Sectional Study.JMIR formative research · 2025Article
- Preventing, identifying, and managing sepsis in the community: research and clinical priorities.The Lancet. Primary care · 2025Article
- Accuracy of online symptom assessment applications, large language models, and laypeople for self-triage decisions.NPJ digital medicine · 2025Article
- Exploring Laypersons' Experiences With a Mobile Symptom Checker App as an Interface Between eHealth Literacy, Health Literacy, and Health-Related Behavior: Qualitative Interview Study.JMIR formative research · 2025Article
- Technology-supported self-triage decision making.npj health systems · 2025Article
- Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross-Sectional Investigation.Cancer medicine · 2025Article
- The RepVig framework for designing use-case specific representative vignettes and evaluating triage accuracy of laypeople and symptom assessment applications.Scientific reports · 2024Article
- Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms.BMC infectious diseases · 2024Article
- Triage Accuracy and the Safety of User-Initiated Symptom Assessment With an Electronic Symptom Checker in a Real-Life Setting: Instrument Validation Study.JMIR human factors · 2024Article
- Only the anxious ones? Identifying characteristics of symptom checker app users: a cross-sectional survey.BMC medical informatics and decision making · 2024Article
- Statistical refinement of patient-centered case vignettes for digital health research.Frontiers in digital health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe ever-growing amount of health information available on the web is increasing the demand for tools providing personalized and actionable health information. Such tools include symptom checkers that provide users with a potential diagnosis after responding to a set of probes about their symptoms. Although the potential for their utility is great, little is known about such tools' actual use and effects.
objectiveWe aimed to understand who uses a web-based artificial intelligence-powered symptom checker and its purposes, how they evaluate the experience of the web-based interview and quality of the information, what they intend to do with the recommendation, and predictors of future use.
methodsCross-sectional survey of web-based health information seekers following the completion of a symptom checker visit (N=2437). Measures of comprehensibility, confidence, usefulness, health-related anxiety, empowerment, and intention to use in the future were assessed. ANOVAs and the Wilcoxon rank sum test examined mean outcome differences in racial, ethnic, and sex groups. The relationship between perceptions of the symptom checker and intention to follow recommended actions was assessed using multilevel logistic regression.
resultsBuoy users were well-educated (1384/1704, 81.22% college or higher), primarily White (1227/1693, 72.47%), and female (2069/2437, 84.89%). Most had insurance (1449/1630, 88.89%), a regular health care provider (1307/1709, 76.48%), and reported good health (1000/1703, 58.72%). Three types of symptoms-pain (855/2437, 35.08%), gynecological issues (293/2437, 12.02%), and masses or lumps (204/2437, 8.37%)-accounted for almost half (1352/2437, 55.48%) of site visits. Buoy's top three primary recommendations split across less-serious triage categories: primary care physician in 2 weeks (754/2141, 35.22%), self-treatment (452/2141, 21.11%), and primary care in 1 to 2 days (373/2141, 17.42%). Common diagnoses were musculoskeletal (303/2437, 12.43%), gynecological (304/2437, 12.47%) and skin conditions (297/2437, 12.19%), and infectious diseases (300/2437, 12.31%). Users generally reported high confidence in Buoy, found it useful and easy to understand, and said that Buoy made them feel less anxious and more empowered to seek medical help. Users for whom Buoy recommended "Waiting/Watching" or "Self-Treatment" had strongest intentions to comply, whereas those advised to seek primary care had weaker intentions. Compared with White users, Latino and Black users had significantly more confidence in Buoy (P<.05), and the former also found it significantly more useful (P<.05). Latino (odds ratio 1.96, 95% CI 1.22-3.25) and Black (odds ratio 2.37, 95% CI 1.57-3.66) users also had stronger intentions to discuss recommendations with a provider than White users.
conclusionsResults demonstrate the potential utility of a web-based health information tool to empower people to seek care and reduce health-related anxiety. However, despite encouraging results suggesting the tool may fulfill unmet health information needs among women and Black and Latino adults, analyses of the user base illustrate persistent second-level digital divide effects.
Indexed as
Identifiers
What OpenQuestion holds
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.