Beyond Dr. Google: the evidence on consumer-facing digital tools for diagnosis
Abstract
Over a third of adults go online to diagnose their health condition. Direct-to-consumer (DTC), interactive, diagnostic apps with information personalization capabilities beyond those of static search engines are rapidly proliferating. While these apps promise faster, more convenient and more accurate information to improve diagnosis, little is known about the state of the evidence on their performance or the methods used to evaluate them. We conducted a scoping review of the peer-reviewed and gray literature for the period January 1, 2014–June 30, 2017. We found that the largest category of evaluations involved symptom checkers that applied algorithms to user-answered questions, followed by sensor-driven apps that applied algorithms to smartphone photos, with a handful of evaluations examining crowdsourcing. The most common clinical areas evaluated were dermatology and general diagnostic and triage advice for a range of conditions. Evaluations were highly variable in methodology and conclusions, with about half describing app characteristics and half examining actual performance. Apps were found to vary widely in functionality, accuracy, safety and effectiveness, although the usefulness of this evidence was limited by a frequent failure to provide results by named individual app. Overall, the current evidence base on DTC, interactive diagnostic apps is sparse in scope, uneven in the information provided and inconclusive with respect to safety and effectiveness, with no studies of clinical risks and benefits involving real-world consumer use. Given that DTC diagnostic apps are rapidly evolving, rigorous and standardized evaluations are essential to inform decisions by clinicians, patients, policymakers and other stakeholders.
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Diagnostic symptom checker apps targeted at specific symptoms such as hand or knee pain frequently deliver inaccurate advice.
“Diagnostic symptom checker apps targeted at specific symptoms such as hand or knee pain have been shown to frequently deliver inaccurate advice.”
From What is the clinical value of mHealth for patients? (2020) · cites this paper as [3]
Interactive symptom checkers are the most frequently investigated category of diagnostic app.
“a recent systematic review demonstrated that interactive symptom checkers are the most frequently investigated category of diagnostic app.”
From What is the clinical value of mHealth for patients? (2020) · cites this paper as [3]
Symptom checker apps are often risk averse and may lead to unnecessary consultations in the non-emergency setting.
“Published evidence suggests that symptom checker apps are often risk averse and may lead to unnecessary consultations in the non-emergency setting.”
From What is the clinical value of mHealth for patients? (2020) · cites this paper as [3]
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- Authors
- Millenson, M. L., Baldwin, J. L., Zipperer, L., & Singh, H.
- Affiliations
- Northwestern University (US)
- Michael E. DeBakey VA Medical Center (US)
- Zipperer Project Management , Albuquerque, NM , USA
- Corresponding author
- Millenson, Michael L.
- Venue
- Diagnosis
- Publisher
- Walter de Gruyter GmbH
- Published
- 2018-07-23
- Volume
- 5
- Issue
- 3
- Pages
- 95-105
- ISSN
- 2194-8011 (ISSN-L)
- 2194-802X
- OpenAlex
- W2884680014
- Cited by
- 91
- References
- 47
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 12 · 2023: 18 · 2022: 13 · 2021: 10 · 2020: 13 · 2019: 3 · 2018: 2
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diagnosis (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Agency for Healthcare Research and Quality (R01HS022087)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- self-diagnosis, symptom checkers, crowdsourcing, diagnostic accuracy, safety and effectiveness, dermatology, consumer health information
- MeSH terms
- Direct-to-Consumer Advertising, Internet Access, Algorithms, Diagnostic Errors, Female, Humans, Male, Triage, Evidence-Based Medicine, Consumer Health Information, Crowdsourcing, Mobile Applications
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