Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study
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 se...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-08-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2022/8/e36322 |
| _version_ | 1797734761862529024 |
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| author | Kimberly Arellano Carmona Deepti Chittamuru Richard L Kravitz Steven Ramondt A Susana Ramírez |
| author_facet | Kimberly Arellano Carmona Deepti Chittamuru Richard L Kravitz Steven Ramondt A Susana Ramírez |
| author_sort | Kimberly Arellano Carmona |
| collection | DOAJ |
| description |
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. |
| first_indexed | 2024-03-12T12:49:08Z |
| format | Article |
| id | doaj.art-0661b800bd8a4b48b8b3dbd57f00149b |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:49:08Z |
| publishDate | 2022-08-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-0661b800bd8a4b48b8b3dbd57f00149b2023-08-28T22:55:19ZengJMIR PublicationsJournal of Medical Internet Research1438-88712022-08-01248e3632210.2196/36322Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire StudyKimberly Arellano Carmonahttps://orcid.org/0000-0002-6393-4121Deepti Chittamuruhttps://orcid.org/0000-0003-2411-9658Richard L Kravitzhttps://orcid.org/0000-0001-5575-529XSteven Ramondthttps://orcid.org/0000-0001-6794-6894A Susana Ramírezhttps://orcid.org/0000-0002-9095-5358 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.https://www.jmir.org/2022/8/e36322 |
| spellingShingle | Kimberly Arellano Carmona Deepti Chittamuru Richard L Kravitz Steven Ramondt A Susana Ramírez Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study Journal of Medical Internet Research |
| title | Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study |
| title_full | Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study |
| title_fullStr | Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study |
| title_full_unstemmed | Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study |
| title_short | Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study |
| title_sort | health information seeking from an intelligent web based symptom checker cross sectional questionnaire study |
| url | https://www.jmir.org/2022/8/e36322 |
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