Literature DB >> 33328258

How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs.

Stephen Gilbert1, Alicia Mehl2, Adel Baluch2, Caoimhe Cawley2, Jean Challiner2, Hamish Fraser3, Elizabeth Millen2, Maryam Montazeri2, Jan Multmeier2, Fiona Pick2, Claudia Richter2, Ewelina Türk2, Shubhanan Upadhyay2, Vishaal Virani2, Nicola Vona2, Paul Wicks2, Claire Novorol2.   

Abstract

OBJECTIVES: To compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps.
DESIGN: Vignettes study.
SETTING: 200 primary care vignettes. INTERVENTION/COMPARATOR: For eight apps and seven general practitioners (GPs): breadth of coverage and condition-suggestion and urgency advice accuracy measured against the vignettes' gold-standard. PRIMARY OUTCOME MEASURES: (1) Proportion of conditions 'covered' by an app, that is, not excluded because the user was too young/old or pregnant, or not modelled; (2) proportion of vignettes with the correct primary diagnosis among the top 3 conditions suggested; (3) proportion of 'safe' urgency advice (ie, at gold standard level, more conservative, or no more than one level less conservative).
RESULTS: Condition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%; WebMD: 93.0%. Top-3 suggestion accuracy was GPs (average): 82.1%±5.2%; Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps excluded certain user demographics or conditions and their performance was generally greater with the exclusion of corresponding vignettes. For safe urgency advice, tested GPs had an average of 97.0%±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 SD of the GPs-Ada: 97.0%; Babylon: 95.1%; Symptomate: 97.8%. One app had a safety performance within 2 SDs of GPs-Your.MD: 92.6%. Three apps had a safety performance outside 2 SDs of GPs-Buoy: 80.0% (p<0.001); K Health: 81.3% (p<0.001); Mediktor: 87.3% (p=1.3×10-3).
CONCLUSIONS: The utility of digital symptom assessment apps relies on coverage, accuracy and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  health informatics; information technology; primary care; world wide web technology

Year:  2020        PMID: 33328258     DOI: 10.1136/bmjopen-2020-040269

Source DB:  PubMed          Journal:  BMJ Open        ISSN: 2044-6055            Impact factor:   2.692


  24 in total

1.  Triage Errors in Primary and Pre-Primary Care.

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2.  Ethical, Legal, and Social Implications of Symptom Checker Apps in Primary Health Care (CHECK.APP): Protocol for an Interdisciplinary Mixed Methods Study.

Authors:  Anna-Jasmin Wetzel; Roland Koch; Christine Preiser; Regina Müller; Malte Klemmt; Robert Ranisch; Hans-Jörg Ehni; Urban Wiesing; Monika A Rieger; Tanja Henking; Stefanie Joos
Journal:  JMIR Res Protoc       Date:  2022-05-16

3.  Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study.

Authors:  Marcel Schmude; Nahya Salim; Hila Azadzoy; Mustafa Bane; Elizabeth Millen; Lisa O'Donnell; Philipp Bode; Ewelina Türk; Ria Vaidya; Stephen Gilbert
Journal:  JMIR Res Protoc       Date:  2022-06-07

4.  Rheumatic?-A Digital Diagnostic Decision Support Tool for Individuals Suspecting Rheumatic Diseases: A Multicenter Pilot Validation Study.

Authors:  Rachel Knevel; Johannes Knitza; Aase Hensvold; Alexandra Circiumaru; Tor Bruce; Sebastian Evans; Tjardo Maarseveen; Marc Maurits; Liesbeth Beaart-van de Voorde; David Simon; Arnd Kleyer; Martina Johannesson; Georg Schett; Tom Huizinga; Sofia Svanteson; Alexandra Lindfors; Lars Klareskog; Anca Catrina
Journal:  Front Med (Lausanne)       Date:  2022-04-25

5.  Study protocol for a prospective, double-blinded, observational study investigating the diagnostic accuracy of an app-based diagnostic health care application in an emergency room setting: the eRadaR trial.

Authors:  S Fatima Faqar-Uz-Zaman; Natalie Filmann; Dora Mahkovic; Michael von Wagner; Charlotte Detemble; Ulf Kippke; Ursula Marschall; Luxia Anantharajah; Philipp Baumartz; Paula Sobotta; Wolf O Bechstein; Andreas A Schnitzbauer
Journal:  BMJ Open       Date:  2021-01-08       Impact factor: 2.692

6.  Accuracy, patient-perceived usability, and acceptance of two symptom checkers (Ada and Rheport) in rheumatology: interim results from a randomized controlled crossover trial.

Authors:  Axel J Hueber; Martin Welcker; Johannes Knitza; Jacob Mohn; Christina Bergmann; Eleni Kampylafka; Melanie Hagen; Daniela Bohr; Harriet Morf; Elizabeth Araujo; Matthias Englbrecht; David Simon; Arnd Kleyer; Timo Meinderink; Wolfgang Vorbrüggen; Cay Benedikt von der Decken; Stefan Kleinert; Andreas Ramming; Jörg H W Distler; Nicolas Vuillerme; Achim Fricker; Peter Bartz-Bazzanella; Georg Schett
Journal:  Arthritis Res Ther       Date:  2021-04-13       Impact factor: 5.156

7.  Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study.

Authors:  Yukinori Harada; Shinichi Katsukura; Ren Kawamura; Taro Shimizu
Journal:  Int J Environ Res Public Health       Date:  2021-02-21       Impact factor: 3.390

8.  Young Adults' Perspectives on the Use of Symptom Checkers for Self-Triage and Self-Diagnosis: Qualitative Study.

Authors:  Stephanie Aboueid; Samantha Meyer; James R Wallace; Shreya Mahajan; Ashok Chaurasia
Journal:  JMIR Public Health Surveill       Date:  2021-01-06

9.  Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study.

Authors:  Caoimhe Cawley; François Bergey; Alicia Mehl; Ashlee Finckh; Andreas Gilsdorf
Journal:  JMIR Public Health Surveill       Date:  2021-11-04

10.  Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients.

Authors:  Severin Hennemann; Sebastian Kuhn; Michael Witthöft; Stefanie M Jungmann
Journal:  JMIR Ment Health       Date:  2022-01-31
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