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

Machine Learning (1465) Decision Support for Health Professionals (1185) Electronic Health Records (1053) Artificial Intelligence (1371) Clinical Information and Decision Making (1407) Safety and Error Prevention in Health (202)

Published on in Vol 10, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35225, first published .
Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History–Taking System With a Differential Diagnosis Generator: Retrospective Observational Study

Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History–Taking System With a Differential Diagnosis Generator: Retrospective Observational Study

Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History–Taking System With a Differential Diagnosis Generator: Retrospective Observational Study

Authors of this article:

Ren Kawamura1 Author Orcid Image ;   Yukinori Harada1, 2 Author Orcid Image ;   Shu Sugimoto2 Author Orcid Image ;   Yuichiro Nagase2 Author Orcid Image ;   Shinichi Katsukura1 Author Orcid Image ;   Taro Shimizu1 Author Orcid Image
Article Authors Cited by (11) Tweetations (2) Metrics

Journals

  1. Sanaeifar A, Eslami S, Ahadi M, Kahani M, Vakili Arki H. DxGenerator: An Improved Differential Diagnosis Generator for Primary Care Based on MetaMap and Semantic Reasoning. Methods of Information in Medicine 2022;61(05/06):174 View
  2. Harada Y, Otaka Y, Katsukura S, Shimizu T. Effect of contextual factors on the prevalence of diagnostic errors among patients managed by physicians of the same specialty: a single-centre retrospective observational study. BMJ Quality & Safety 2024;33(6):386 View
  3. Harada Y, Tomiyama S, Sakamoto T, Sugimoto S, Kawamura R, Yokose M, Hayashi A, Shimizu T. Effects of Combinational Use of Additional Differential Diagnostic Generators on the Diagnostic Accuracy of the Differential Diagnosis List Developed by an Artificial Intelligence–Driven Automated History–Taking System: Pilot Cross-Sectional Study. JMIR Formative Research 2023;7:e49034 View
  4. Küper A, Lodde G, Livingstone E, Schadendorf D, Krämer N. Mitigating cognitive bias with clinical decision support systems: an experimental study. Journal of Decision Systems 2024;33(3):439 View
  5. Hirosawa T, Kawamura R, Harada Y, Mizuta K, Tokumasu K, Kaji Y, Suzuki T, Shimizu T. ChatGPT-Generated Differential Diagnosis Lists for Complex Case–Derived Clinical Vignettes: Diagnostic Accuracy Evaluation. JMIR Medical Informatics 2023;11:e48808 View
  6. Harada Y, Otaka Y, Katsukura S, Shimizu T. Prevalence of atypical presentations among outpatients and associations with diagnostic error. Diagnosis 2024;11(1):40 View
  7. Zhakhina G, Tapinova K, Kanabekova P, Kainazarov T. Pre-consultation history taking systems and their impact on modern practices: Advantages and limitations. Journal of Clinical Medicine of Kazakhstan 2023;20(6):26 View
  8. Harada Y, Sakamoto T, Sugimoto S, Shimizu T. Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study. JMIR Formative Research 2024;8:e53985 View
  9. Otaka Y, Harada Y, Katsukura S, Shimizu T. Diagnostic errors and characteristics of patients seen at a general internal medicine outpatient clinic with a referral for diagnosis. Diagnosis 2024;11(4):400 View
  10. Siira E, Tyskbo D, Nygren J. Healthcare leaders’ experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study. BMC Primary Care 2024;25(1) View
  11. Sakamoto T, Harada Y, Shimizu T. Facilitating trust calibration in artificial-intelligence-driven differential diagnoses list for physicians’ diagnostic accuracy: A quasi-experimental study (Preprint). JMIR Formative Research 2024 View