Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study

The 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....

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Vydáno v:Journal of medical Internet research Ročník 24; číslo 8; s. e36322
Hlavní autoři: Arellano Carmona, Kimberly, Chittamuru, Deepti, Kravitz, Richard L, Ramondt, Steven, Ramírez, A Susana
Médium: Journal Article
Jazyk:angličtina
Vydáno: Toronto Journal of Medical Internet Research 19.08.2022
Gunther Eysenbach MD MPH, Associate Professor
JMIR Publications
Témata:
Accuracy
Adults
Analysis
Anxiety
Artificial intelligence
Black white differences
Black white relations
Comprehension
Confidence
Consumer health information
COVID-19
Diagnostic tests
Emergency medical care
Empowerment
Ethnic differences
Ethnic groups
Health care
Health care industry
Health information
Health insurance
Health services
Health services utilization
Hispanic Americans
Infectious diseases
Information
Information needs
Information seeking behavior
Information systems
Intelligence
Intelligibility
Internet
Internet access
Interviews
Latin American cultural groups
Medical advice systems
Medical diagnosis
Medical personnel
Medical treatment
Original Paper
Pain
Patients
Physicians
Polls & surveys
Primary care
Racial differences
Rankings
Self-medication
Site visits
Symptoms
Triage
Web sites
Women
United States > US
ISSN:1438-8871, 1439-4456, 1438-8871
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Shrnutí:The 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. We 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. Cross-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. Buoy 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.
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ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/36322