Literature DB >> 30993374

[From symptom to diagnosis-symptom checkers re-evaluated : Are symptom checkers finally sufficient and accurate to use? An update from the ENT perspective].

J Nateqi1, S Lin2, H Krobath2, S Gruarin2, T Lutz2, T Dvorak2, A Gruschina2, R Ortner2.   

Abstract

BACKGROUND: Every seventh diagnosis is a misdiagnosis. Each year, 1.5 million lives could be saved worldwide with the correct diagnosis. Physicians have to consider over 20,000 diseases. A study from Harvard University published in 2015 tested 19 symptom checkers and found them to be insufficient, with only 29-71% accuracy in diagnosis.
OBJECTIVE: The current study investigates the diagnostic accuracy of new symptom checkers from an ENT perspective.
MATERIALS AND METHODS: The authors update the abovenamed diagnostic accuracy comparison by (1) including the five new symptom checkers Symptoma, Ada, FindZebra, Mediktor, and Babylon; and (2) normalizing results of the previously tested symptom checkers as to reflect each diagnostic accuracy based on the same set of patient vignettes. The winner is then compared to the two symptom checkers with the most scientific evidence, namely Isabel and FindZebra, on the basis of an ENT-specific test with patient vignettes sourced from the British Medical Journal.
RESULTS: Most of the new symptom checkers demonstrated diagnostic accuracy rates within the previously established range, with the exception of Symptoma, which scored the right diagnosis in 82.2% of cases at the top of the list (+38% points), and in 100% of cases in the top 3 (+29% points) and the top 10 (+16% points), thus raising the bar in this field. The cross-validation with ENT cases resulted in a diagnostic accuracy of 64.3 vs. 21.4 vs. 26.2% (top 1), 92.9 vs. 40.5 vs. 42.9% (top 3), and 100 vs. 61.9 vs. 54.8% (top 10) for Symptoma vs. Isabel vs. FindZebra, respectively.
CONCLUSIONS: Symptoma is the first and only viable solution in this market. Large-scale studies should be conducted to further validate these results as well as to assess the actual practical performance of the symptom checkers and their ability to diagnose rare diseases.

Entities:  

Keywords:  Diagnosis, computer-assisted; Diagnostic errors; Information seeking behavior; Quality of health care; Self care

Mesh:

Year:  2019        PMID: 30993374     DOI: 10.1007/s00106-019-0666-y

Source DB:  PubMed          Journal:  HNO        ISSN: 0017-6192            Impact factor:   1.284


  6 in total

1.  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

2.  Study protocol for a pilot prospective, observational study investigating the condition suggestion and urgency advice accuracy of a symptom assessment app in sub-Saharan Africa: the AFYA-'Health' Study.

Authors:  Elizabeth Millen; Nahya Salim; Hila Azadzoy; Mustafa Miraji Bane; Lisa O'Donnell; Marcel Schmude; Philipp Bode; Ewelina Tuerk; Ria Vaidya; Stephen Henry Gilbert
Journal:  BMJ Open       Date:  2022-04-11       Impact factor: 2.692

3.  Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna.

Authors:  Nicolas Munsch; Stefanie Gruarin; Jama Nateqi; Thomas Lutz; Michael Binder; Judith H Aberle; Alistair Martin; Bernhard Knapp
Journal:  Wien Klin Wochenschr       Date:  2022-04-13       Impact factor: 2.275

4.  An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot.

Authors:  Alistair Martin; Jama Nateqi; Stefanie Gruarin; Nicolas Munsch; Isselmou Abdarahmane; Marc Zobel; Bernhard Knapp
Journal:  Sci Rep       Date:  2020-11-04       Impact factor: 4.379

5.  Syndromic Surveillance Insights from a Symptom Assessment App Before and During COVID-19 Measures in Germany and the United Kingdom: Results From Repeated Cross-Sectional Analyses.

Authors:  Alicia Mehl; Francois Bergey; Caoimhe Cawley; Andreas Gilsdorf
Journal:  JMIR Mhealth Uhealth       Date:  2020-10-09       Impact factor: 4.773

6.  Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study.

Authors:  Nicolas Munsch; Alistair Martin; Stefanie Gruarin; Jama Nateqi; Isselmou Abdarahmane; Rafael Weingartner-Ortner; Bernhard Knapp
Journal:  J Med Internet Res       Date:  2020-10-06       Impact factor: 5.428

  6 in total

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