This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2168) Outbreak and Pandemic Preparedness and Management (1492) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1361) Infectious Diseases (non-STD/STI) (1254) Chatbots and Conversational Agents (203) Symptom Checkers (19)

Published on in Vol 22 , No 10 (2020) :October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21299, first published .
Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study

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

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

Authors of this article:

Nicolas Munsch 1 Author Orcid Image ;   Alistair Martin 1 Author Orcid Image ;   Stefanie Gruarin 2 Author Orcid Image ;   Jama Nateqi 2, 3 Author Orcid Image ;   Isselmou Abdarahmane 1 Author Orcid Image ;   Rafael Weingartner-Ortner 1, 2 Author Orcid Image ;   Bernhard Knapp 1 Author Orcid Image
Article Authors Cited by (32) Tweetations (13) Metrics

Journals

  1. Siddique S, Chow J. Machine Learning in Healthcare Communication. Encyclopedia 2021;1(1):220 View
  2. Kahnbach L, Lehr D, Brandenburger J, Mallwitz T, Jent S, Hannibal S, Funk B, Janneck M. Quality and Adoption of COVID-19 Tracing Apps and Recommendations for Development: Systematic Interdisciplinary Review of European Apps. Journal of Medical Internet Research 2021;23(6):e27989 View
  3. Munsch N, Martin A, Gruarin S, Nateqi J, Abdarahmane I, Weingartner-Ortner R, Knapp B. Authors’ Reply to: Screening Tools: Their Intended Audiences and Purposes. Comment on “Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study”. Journal of Medical Internet Research 2021;23(5):e26543 View
  4. Millen E, Gilsdorf A, Fenech M, Gilbert S. Screening Tools: Their Intended Audiences and Purposes. Comment on “Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study”. Journal of Medical Internet Research 2021;23(5):e26148 View
  5. Umair M, Cheema M, Cheema O, Li H, Lu H. Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT. Sensors 2021;21(11):3838 View
  6. Deng L, Chen L, Yang T, Liu M, Li S, Jiang T. Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study. Journal of Medical Internet Research 2021;23(6):e26892 View
  7. Morales H, Guedes M, Silva J, Massuda A. COVID-19 in Brazil—Preliminary Analysis of Response Supported by Artificial Intelligence in Municipalities. Frontiers in Digital Health 2021;3 View
  8. Snitz K, Honigstein D, Weissgross R, Ravia A, Mishor E, Perl O, Karagach S, Medhanie A, Harel N, Shushan S, Roth Y, Iravani B, Arshamian A, Ernst G, Okamoto M, Poo C, Bonacchi N, Mainen Z, Monteleone E, Dinnella C, Spinelli S, Mariño-Sánchez F, Ferdenzi C, Smeets M, Touhara K, Bensafi M, Hummel T, Lundström J, Sobel N. An olfactory self-test effectively screens for COVID-19. Communications Medicine 2022;2(1) View
  9. Munsch N, Gruarin S, Nateqi J, Lutz T, Binder M, Aberle J, Martin A, Knapp B. Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna. Wiener klinische Wochenschrift 2022;134(9-10):344 View
  10. Kadirvelu B, Burcea G, Quint J, Costelloe C, Faisal A. Variation in global COVID-19 symptoms by geography and by chronic disease: A global survey using the COVID-19 Symptom Mapper. eClinicalMedicine 2022;45:101317 View
  11. Wilson L, Marasoiu M. The Development and Use of Chatbots in Public Health: Scoping Review. JMIR Human Factors 2022;9(4):e35882 View
  12. Liu A, Odisho A, Brown III W, Gonzales R, Neinstein A, Judson T. Patient Experience and Feedback After Using an Electronic Health Record–Integrated COVID-19 Symptom Checker: Survey Study. JMIR Human Factors 2022;9(3):e40064 View
  13. Laukka E, Kujala S, Gluschkoff K, Kanste O, Hörhammer I, Heponiemi T. Leaders’ support for using online symptom checkers in Finnish primary care: Survey study. Health Informatics Journal 2021;27(4):146045822110522 View
  14. Chiu H, Hwang C, Chen S, Shih F, Han H, King C, Gilbert J, Fang C, Oyang Y. Machine learning for emerging infectious disease field responses. Scientific Reports 2022;12(1) View
  15. Hennemann S, Kuhn S, Witthöft M, Jungmann S. Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients. JMIR Mental Health 2022;9(1):e32832 View
  16. Amiri P, Karahanna E. Chatbot use cases in the Covid-19 public health response. Journal of the American Medical Informatics Association 2022;29(5):1000 View
  17. Röbbelen A, Schmieding M, Kopka M, Balzer F, Feufel M. Interactive Versus Static Decision Support Tools for COVID-19: Randomized Controlled Trial. JMIR Public Health and Surveillance 2022;8(4):e33733 View
  18. Doğan P, Güney Varal İ, Ararat A, Güler Kazancı E. Evaluation of Red Blood Cell Distribution Width-Platelet Ratio as an Early Predictor of Late-Onset Sepsis in Preterm Infants. Güncel Pediatri 2022;20(1):27 View
  19. Po (Harvey) Chin Y, Song W, Islam M, Bates D, Zhou L, Chuan (Jack) Li Y. Sequential coupling of dry and wet COVID-19 screening to reduce the number of quarantined individuals. Computer Methods and Programs in Biomedicine 2022;218:106715 View
  20. Zobel M, Knapp B, Nateqi J, Martin A, V. E. S. Correlating global trends in COVID-19 cases with online symptom checker self-assessments. PLOS ONE 2023;18(2):e0281709 View
  21. Michel J, Kilb T, Mettler A, Müller M, Hautz W, Hautz S, Sauter T. The Utility of an Online Forward Triage Tool During the SARS-CoV-2 Pandemic: Health Care Provider and Health Authority Perspectives. Frontiers in Public Health 2022;10 View
  22. de Campos Filho A, Vasconcelos Cursino J, do Nascimento J, de Souza R, da Silva Lima Roque G, de Souza Cavalcanti A. Content and Usability Validation of an Intelligent Virtual Conversation Assistant Used for Virtual Triage During the COVID-19 Pandemic in Brazil. CIN: Computers, Informatics, Nursing 2022;40(11):779 View
  23. Judson T, Pierce L, Tutman A, Mourad M, Neinstein A, Shuler G, Gonzales R, Odisho A. Utilization patterns and efficiency gains from use of a fully EHR-integrated COVID-19 self-triage and self-scheduling tool: a retrospective analysis. Journal of the American Medical Informatics Association 2022;29(12):2066 View
  24. Megahed N, Abdel-Kader R. Smart Cities after COVID-19: Building a conceptual framework through a multidisciplinary perspective. Scientific African 2022;17:e01374 View
  25. Liu V, Kaila M, Koskela T. User initiated symptom assessment with an electronic symptom checker. Study protocol for mixed-methods validation. (Preprint). JMIR Research Protocols 2022 View
  26. Hogeveen S, Donaghy-Hughes M, Nova A, Saari M, Sinn C, Northwood M, Heckman G, Geffen L, Hirdes J. The interRAI COVID-19 vulnerability screener: Results of a health surveillance initiative for vulnerable adults in the community during the COVID-19 pandemic. Archives of Gerontology and Geriatrics 2023;113:105056 View
  27. Lin S, Nateqi J, Weingartner-Ortner R, Gruarin S, Marling H, Pilgram V, Lagler F, Aigner E, Martin A. An artificial intelligence-based approach for identifying rare disease patients using retrospective electronic health records applied for Pompe disease. Frontiers in Neurology 2023;14 View

Books/Policy Documents

  1. Etienne L, Faux F, Roecker O. Artificial Intelligence in Medicine. View
  2. Singh A, Virdi K, Choudhary A. Impact of AI and Data Science in Response to Coronavirus Pandemic. View
  3. Aftab H, Gautam V, Hawkins R, Alexander R, Habli I. Wireless Mobile Communication and Healthcare. View
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