Literature DB >> 30032130

Beyond Dr. Google: the evidence on consumer-facing digital tools for diagnosis.

Michael L Millenson1,2, Jessica L Baldwin3,4, Lorri Zipperer5, Hardeep Singh3,4.   

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.

Entities:  

Keywords:  consumerism; crowdsourcing; diagnostic error; digital health; evidence-based medicine; health apps; health information technology; mHealth; patient engagement

Mesh:

Year:  2018        PMID: 30032130     DOI: 10.1515/dx-2018-0009

Source DB:  PubMed          Journal:  Diagnosis (Berl)        ISSN: 2194-802X


  14 in total

1.  Design and testing of a mobile health application rating tool.

Authors:  David M Levine; Zoe Co; Lisa P Newmark; Alissa R Groisser; A Jay Holmgren; Jennifer S Haas; David W Bates
Journal:  NPJ Digit Med       Date:  2020-05-21

2.  A Roadmap to Advance Patient Safety in Ambulatory Care.

Authors:  Hardeep Singh; Pascale Carayon
Journal:  JAMA       Date:  2020-12-22       Impact factor: 56.272

3.  Reducing anticholinergic medication exposure among older adults using consumer technology: Protocol for a randomized clinical trial.

Authors:  Ephrem Abebe; Noll L Campbell; Daniel O Clark; Wanzhu Tu; Jordan R Hill; Addison B Harrington; Gracen O'Neal; Kimberly S Trowbridge; Christian Vallejo; Ziyi Yang; Na Bo; Alexxus Knight; Khalid A Alamer; Allie Carter; Robin Valenzuela; Philip Adeoye; Malaz A Boustani; Richard J Holden
Journal:  Res Social Adm Pharm       Date:  2020-10-22

4.  Design and testing of a mobile health application rating tool.

Authors:  David M Levine; Zoe Co; Lisa P Newmark; Alissa R Groisser; A Jay Holmgren; Jennifer S Haas; David W Bates
Journal:  NPJ Digit Med       Date:  2020-05-21

5.  The Digital Marshmallow Test (DMT) Diagnostic and Monitoring Mobile Health App for Impulsive Behavior: Development and Validation Study.

Authors:  Michael Sobolev; Rachel Vitale; Hongyi Wen; James Kizer; Robert Leeman; J P Pollak; Amit Baumel; Nehal P Vadhan; Deborah Estrin; Frederick Muench
Journal:  JMIR Mhealth Uhealth       Date:  2021-01-22       Impact factor: 4.773

6.  A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis.

Authors:  Adam Baker; Yura Perov; Katherine Middleton; Janie Baxter; Daniel Mullarkey; Davinder Sangar; Mobasher Butt; Arnold DoRosario; Saurabh Johri
Journal:  Front Artif Intell       Date:  2020-11-30

7.  Goldilocks and the Three Bears: A Just-Right Hybrid Model to Synthesize the Growing Landscape of Publicly Available Health-Related Mobile Apps.

Authors:  Nancy Lau; Alison O'Daffer; Joyce Yi-Frazier; Abby R Rosenberg
Journal:  J Med Internet Res       Date:  2021-06-07       Impact factor: 5.428

8.  Patient Perspectives on the Usefulness of an Artificial Intelligence-Assisted Symptom Checker: Cross-Sectional Survey Study.

Authors:  Ashley N D Meyer; Traber D Giardina; Christiane Spitzmueller; Umber Shahid; Taylor M T Scott; Hardeep Singh
Journal:  J Med Internet Res       Date:  2020-01-30       Impact factor: 5.428

9.  Assessment of Mobile Health Apps Using Built-In Smartphone Sensors for Diagnosis and Treatment: Systematic Survey of Apps Listed in International Curated Health App Libraries.

Authors:  Clarence Baxter; Julie-Anne Carroll; Brendan Keogh; Corneel Vandelanotte
Journal:  JMIR Mhealth Uhealth       Date:  2020-02-03       Impact factor: 4.773

Review 10.  What is the clinical value of mHealth for patients?

Authors:  Simon P Rowland; J Edward Fitzgerald; Thomas Holme; John Powell; Alison McGregor
Journal:  NPJ Digit Med       Date:  2020-01-13
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