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The Big Three diagnostic errors through reflections of Japanese internists

  • Kotaro Kunitomo , Ashwin Gupta , Taku Harada and Takashi Watari EMAIL logo
Published/Copyright: March 20, 2024
Diagnosis
From the journal Diagnosis Volume 11 Issue 3

Abstract

Objectives

To analyze the Big Three diagnostic errors (malignant neoplasms, cardiovascular diseases, and infectious diseases) through internists’ self-reflection on their most memorable diagnostic errors.

Methods

This secondary analysis study, based on a web-based cross-sectional survey, recruited participants from January 21 to 31, 2019. The participants were asked to recall the most memorable diagnostic error cases in which they were primarily involved. We gathered data on internists’ demographics, time to error recognition, and error location. Factors causing diagnostic errors included environmental conditions, information processing, and cognitive bias. Participants scored the significance of each contributing factor on a Likert scale (0, unimportant; 10, extremely important).

Results

The Big Three comprised 54.1 % (n=372) of the 687 cases reviewed. The median physician age was 51.5 years (interquartile range, 42–58 years); 65.6 % of physicians worked in hospital settings. Delayed diagnoses were the most common among malignancies (n=64, 46 %). Diagnostic errors related to malignancy were frequent in general outpatient settings on weekdays and in the mornings and were not identified for several months following the event. Environmental factors often contributed to cardiovascular disease-related errors, which were typically identified within days in emergency departments, during night shifts, and on holidays. Information gathering and interpretation significantly impacted infectious disease diagnoses.

Conclusions

The Big Three accounted for the majority of cases recalled by Japanese internists. The most relevant contributing factors were different for each of the three categories. Addressing these errors may require a unique approach based on the disease associations.

Keywords: Big Three; cardiovascular diseases; diagnostic errors; infectious diseases; malignancies; cognitive bias
Corresponding author: Takashi Watari, MD, MHQS, PhD, Medicine Service, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, 48105, USA; Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, 48105, USA; Department of General Medicine, Nerima Hikarigaoka Hospital, Nerima-ku, Tokyo, 179-0072, Japan; and General Medicine Center, Shimane University Hospital, 89-1, Enya-cho, Izumo shi, Shimane, 693-8501, Japan, Phone: +81 853 20 2005, Fax: +81 853 20 2375, E-mail:

Funding source: Japan Society for the Promotion of Science

Award Identifier / Grant number: 20H03913

Acknowledgments

We sincerely thank the Nikkei Medical Online editorial team for their help with data collection and Dr. Yu Amano for his assistance with data cleaning. We also thank Dr. Yasuharu Tokuda for his advice on this study.

  1. Ethical approval: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as revised in 2013), and has been approved by the authors’ Institutional Review Board (the Medical Research Ethics Committee of Shimane University School of Medicine) (Approval No.: 20181017).

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: All authors accept responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: Authors state no conflict of interest.

  5. Research funding: This work was supported by the National Academic Research Grant Fund (JSPS KA-KENHI grant number, 20H03913). The sponsor of the study had no role in study design, data collection, analysis, or manuscript preparation.

  6. Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author, T. W., of the Shimane General Medicine Center, e-mail: upon reasonable request.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/dx-2023-0131).

Received: 2023-09-30
Accepted: 2024-02-27
Published Online: 2024-03-20

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/dx-2023-0131
Keywords for this article
Big Three; cardiovascular diseases; diagnostic errors; infectious diseases; malignancies; cognitive bias

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. The growing threat of hijacked journals
  4. Review
  5. Effects of SNAPPS in clinical reasoning teaching: a systematic review with meta-analysis of randomized controlled trials
  6. Mini Review
  7. Diagnostic value of D-dimer in differentiating multisystem inflammatory syndrome in Children (MIS-C) from Kawasaki disease: systematic literature review and meta-analysis
  8. Opinion Papers
  9. Masquerade of authority: hijacked journals are gaining more credibility than original ones
  10. FRAMED: a framework facilitating insight problem solving
  11. Algorithms in medical decision-making and in everyday life: what’s the difference?
  12. Original Articles
  13. Computerized diagnostic decision support systems – a comparative performance study of Isabel Pro vs. ChatGPT4
  14. Comparative analysis of diagnostic accuracy in endodontic assessments: dental students vs. artificial intelligence
  15. Assessing the Revised Safer Dx Instrument® in the understanding of ambulatory system design changes for type 1 diabetes and autism spectrum disorder in pediatrics
  16. The Big Three diagnostic errors through reflections of Japanese internists
  17. SASAN: ground truth for the effective segmentation and classification of skin cancer using biopsy images
  18. Computable phenotype for diagnostic error: developing the data schema for application of symptom-disease pair analysis of diagnostic error (SPADE)
  19. Development of a disease-based hospital-level diagnostic intensity index
  20. HbA1c and fasting plasma glucose levels are equally related to incident cardiovascular risk in a high CVD risk population without known diabetes
  21. Short Communications
  22. Can ChatGPT-4 evaluate whether a differential diagnosis list contains the correct diagnosis as accurately as a physician?
  23. Analysis of thicknesses of blood collection needle by scanning electron microscopy reveals wide heterogeneity
  24. Letters to the Editor
  25. For any disease a human can imagine, ChatGPT can generate a fake report
  26. The dilemma of epilepsy diagnosis in Pakistan
  27. The Japanese universal health insurance system in the context of diagnostic equity
  28. Case Report – Lessons in Clinical Reasoning
  29. Lessons in clinical reasoning – pitfalls, myths, and pearls: a case of tarsal tunnel syndrome caused by an intraneural ganglion cyst
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