Literature DB >> 30973602

Accuracy of a Popular Online Symptom Checker for Ophthalmic Diagnoses.

Carl Shen1, Michael Nguyen1, Alexander Gregor2, Gloria Isaza1, Anne Beattie1.   

Abstract

Importance: Because more patients are presenting with self-guided research of symptoms, it is important to assess the capabilities and limitations of these available health information tools. Objective: To determine the accuracy of the most popular online symptom checker for ophthalmic diagnoses. Design, Setting, and Participants: In a cross-sectional study, 42 validated clinical vignettes of ophthalmic symptoms were generated and distilled to their core presenting symptoms. Cases were entered into WebMD symptom checker by both medically trained and nonmedically trained personnel blinded to the diagnosis. Output from the symptom checker, including the number of symptoms, ranking and list of diagnoses, and triage urgency were recorded. The study was conducted on October 13, 2017. Analysis was performed between October 15, 2017, and April 30, 2018. Main Outcomes and Measures: Accuracy of the top 3 diagnoses generated by the online symptom checker.
Results: The mean (SD) number of symptoms entered was 3.6 (1.6) (range, 1-8). The median (SD) number of diagnoses generated by the symptom checker was 26.8 (21.8) (range, 1-99). The primary diagnosis by the symptom checker was correct in 11 of 42 (26%; 95% CI, 12%-40%) cases. The correct diagnosis was included in the online symptom checker's top 3 diagnoses in 16 of 42 (38%; 95% CI, 25%-56%) cases. The correct diagnosis was not included in the symptom checker's list in 18 of 42 (43%; 95% CI, 32%-63%) cases. Triage urgency based on the top diagnosis was appropriate in 7 of 18 (39%; 95% CI, 14%-64%) emergent cases and 21 of 24 (88%; 95% CI, 73%-100%) nonemergent cases. Interuser variability for the correct diagnosis being in the top 3 listed was at least moderate (Cohen κ = 0.74; 95% CI, 0.54-0.95). Conclusions and Relevance: The most popular online symptom checker may arrive at the correct clinical diagnosis for ophthalmic conditions, but a substantial proportion of diagnoses may not be captured. These findings suggest that further research to reflect the real-life application of internet diagnostic resources is required.

Entities:  

Mesh:

Year:  2019        PMID: 30973602      PMCID: PMC6567837          DOI: 10.1001/jamaophthalmol.2019.0571

Source DB:  PubMed          Journal:  JAMA Ophthalmol        ISSN: 2168-6165            Impact factor:   7.389


  8 in total

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

Authors:  Hai Nguyen; Andras Meczner; Krista Burslam-Dawe; Benedict Hayhoe
Journal:  J Med Internet Res       Date:  2022-06-24       Impact factor: 7.076

2.  Artificial Intelligence-Based Differential Diagnosis: Development and Validation of a Probabilistic Model to Address Lack of Large-Scale Clinical Datasets.

Authors:  Shahrukh Chishti; Karan Raj Jaggi; Anuj Saini; Gaurav Agarwal; Ashish Ranjan
Journal:  J Med Internet Res       Date:  2020-04-28       Impact factor: 5.428

3.  Artificial intelligence method to classify ophthalmic emergency severity based on symptoms: a validation study.

Authors:  Hyunmin Ahn
Journal:  BMJ Open       Date:  2020-07-05       Impact factor: 2.692

Review 4.  A brief overview of animal symptom checkers.

Authors:  Jelle Stans
Journal:  Open Vet J       Date:  2020-01-15

5.  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

6.  Safety of Triage Self-assessment Using a Symptom Assessment App for Walk-in Patients in the Emergency Care Setting: Observational Prospective Cross-sectional Study.

Authors:  Fabienne Cotte; Tobias Mueller; Stephen Gilbert; Bibiana Blümke; Jan Multmeier; Martin Christian Hirsch; Paul Wicks; Joseph Wolanski; Darja Tutschkow; Carmen Schade Brittinger; Lars Timmermann; Andreas Jerrentrup
Journal:  JMIR Mhealth Uhealth       Date:  2022-03-28       Impact factor: 4.773

7.  Cost and Effort Considerations for the Development of Intervention Studies Using Mobile Health Platforms: Pragmatic Case Study.

Authors:  Dan Thorpe; John Fouyaxis; Jessica M Lipschitz; Amy Nielson; Wenhao Li; Susan A Murphy; Niranjan Bidargaddi
Journal:  JMIR Form Res       Date:  2022-03-31

Review 8.  The diagnostic and triage accuracy of digital and online symptom checker tools: a systematic review.

Authors:  William Wallace; Calvin Chan; Swathikan Chidambaram; Lydia Hanna; Fahad Mujtaba Iqbal; Amish Acharya; Pasha Normahani; Hutan Ashrafian; Sheraz R Markar; Viknesh Sounderajah; Ara Darzi
Journal:  NPJ Digit Med       Date:  2022-08-17
  8 in total

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