ArticleJMIR human factors2022
Determinants of Laypersons' Trust in Medical Decision Aids: Randomized Controlled Trial.
Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Evaluating digital triage symptom checker with historical triage-related adverse events.Scandinavian journal of primary health care · 2026Article
- The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study.PLOS digital health · 2026Article
- Disclaimers and Referral Patterns for Medical Advice Across Urgency Levels: Large Language Model Evaluation Study.Journal of medical Internet research · 2026Article
- How to Evaluate the Accuracy of Symptom Checkers and Diagnostic Decision Support Systems: Symptom Checker Accuracy Reporting Framework (SCARF).JMIR human factors · 2026Article
- Evaluating adult digital health literacy, 2020-2025: A systematic review.PLOS digital health · 2025Article
- Understanding the Impact of AI Doctors' Information Quality on Patients' Intentions to Adopt AI for Independent Diagnosis: Scenario-Based Experimental Study.Journal of medical Internet research · 2025Article
- Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists.Journal of medical Internet research · 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
- The RepVig framework for designing use-case specific representative vignettes and evaluating triage accuracy of laypeople and symptom assessment applications.Scientific reports · 2024Article
- Unveiling Usage Patterns and Explaining Usage of Symptom Checker Apps: Explorative Longitudinal Mixed Methods Study.Journal of medical Internet research · 2024Article
- Only the anxious ones? Identifying characteristics of symptom checker app users: a cross-sectional survey.BMC medical informatics and decision making · 2024Article
- Assessment of a Digital Symptom Checker Tool's Accuracy in Suggesting Reproductive Health Conditions: Clinical Vignettes Study.JMIR mHealth and uHealth · 2023Article
- User-Initiated Symptom Assessment With an Electronic Symptom Checker: Protocol for a Mixed Methods Validation Study.JMIR research protocols · 2023Article
- Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study.Journal of medical Internet research · 2023Article
- How Virtual Triage Can Improve Patient Experience and Satisfaction: A Narrative Review and Look Forward.Telemedicine reports · 2023Review
- The Triage Capability of Laypersons: Retrospective Exploratory Analysis.JMIR formative research · 2022Article
- Promoting trust and intention to adopt health information generated by ChatGPT among healthcare customers: An empirical study.Digital healthArticle
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Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSymptom checker apps are patient-facing decision support systems aimed at providing advice to laypersons on whether, where, and how to seek health care (disposition advice). Such advice can improve laypersons' self-assessment and ultimately improve medical outcomes. Past research has mainly focused on the accuracy of symptom checker apps' suggestions. To support decision-making, such apps need to provide not only accurate but also trustworthy advice. To date, only few studies have addressed the question of the extent to which laypersons trust symptom checker app advice or the factors that moderate their trust. Studies on general decision support systems have shown that framing automated systems (anthropomorphic or emphasizing expertise), for example, by using icons symbolizing artificial intelligence (AI), affects users' trust.
objectiveThis study aims to identify the factors influencing laypersons' trust in the advice provided by symptom checker apps. Primarily, we investigated whether designs using anthropomorphic framing or framing the app as an AI increases users' trust compared with no such framing.
methodsThrough a web-based survey, we recruited 494 US residents with no professional medical training. The participants had to first appraise the urgency of a fictitious patient description (case vignette). Subsequently, a decision aid (mock symptom checker app) provided disposition advice contradicting the participants' appraisal, and they had to subsequently reappraise the vignette. Participants were randomized into 3 groups: 2 experimental groups using visual framing (anthropomorphic, 160/494, 32.4%, vs AI, 161/494, 32.6%) and a neutral group without such framing (173/494, 35%).
resultsMost participants (384/494, 77.7%) followed the decision aid's advice, regardless of its urgency level. Neither anthropomorphic framing (odds ratio 1.120, 95% CI 0.664-1.897) nor framing as AI (odds ratio 0.942, 95% CI 0.565-1.570) increased behavioral or subjective trust (P=.99) compared with the no-frame condition. Even participants who were extremely certain in their own decisions (ie, 100% certain) commonly changed it in favor of the symptom checker's advice (19/34, 56%). Propensity to trust and eHealth literacy were associated with increased subjective trust in the symptom checker (propensity to trust b=0.25; eHealth literacy b=0.2), whereas sociodemographic variables showed no such link with either subjective or behavioral trust.
conclusionsContrary to our expectation, neither the anthropomorphic framing nor the emphasis on AI increased trust in symptom checker advice compared with that of a neutral control condition. However, independent of the interface, most participants trusted the mock app's advice, even when they were very certain of their own assessment. Thus, the question arises as to whether laypersons use such symptom checkers as substitutes rather than as aids in their own decision-making. With trust in symptom checkers already high at baseline, the benefit of symptom checkers depends on interface designs that enable users to adequately calibrate their trust levels during usage.
trial registrationDeutsches Register Klinischer Studien DRKS00028561; https://tinyurl.com/rv4utcfb (retrospectively registered).
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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 OpenQuestion graph.