Startseite Medizin Diagnostic errors in uncommon conditions: a systematic review of case reports of diagnostic errors
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Diagnostic errors in uncommon conditions: a systematic review of case reports of diagnostic errors

  • Yukinori Harada EMAIL logo , Takashi Watari , Hiroyuki Nagano , Tomoharu Suzuki , Kotaro Kunitomo , Taiju Miyagami ORCID logo , Tetsuro Aita , Kosuke Ishizuka ORCID logo , Mika Maebashi , Taku Harada , Tetsu Sakamoto , Shusaku Tomiyama und Taro Shimizu
Veröffentlicht/Copyright: 10. August 2023
Diagnosis
Aus der Zeitschrift Diagnosis Band 10 Heft 4

Abstract

Objectives

To assess the usefulness of case reports as sources for research on diagnostic errors in uncommon diseases and atypical presentations.

Content

We reviewed 563 case reports of diagnostic error. The commonality of the final diagnoses was classified based on the description in the articles, Orphanet, or epidemiological data on available references; the typicality of presentation was classified based on the description in the articles and the judgment of the physician researchers. Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC), and Generic Diagnostic Pitfalls (GDP) taxonomies were used to assess the factors contributing to diagnostic errors.

Summary and outlook

Excluding three cases in that commonality could not be classified, 560 cases were classified into four categories: typical presentations of common diseases (60, 10.7 %), atypical presentations of common diseases (35, 6.2 %), typical presentations of uncommon diseases (276, 49.3 %), and atypical presentations of uncommon diseases (189, 33.8 %). The most important DEER taxonomy was “Failure/delay in considering the diagnosis” among the four categories, whereas the most important RDC and GDP taxonomies varied with the categories. Case reports can be a useful data source for research on the diagnostic errors of uncommon diseases with or without atypical presentations.


Corresponding author: Yukinori Harada, MD, PhD, Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga-Gun, Tochigi 321-0293, Japan, Phone: +81 282 86 1111, E-mail:

Acknowledgments

This study was conducted as an academic work by the Japan Diagnostic Excellence (JDX) team of the Japanese Society of Hospital General Medicine. We thank Professor Gordon D. Schiff for his intellectual support in the interpretation of DEER, RDC, and GDP.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

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

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

  5. Research funding: None declared.

  6. Data availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

1. Yang, D, Fineberg, HV, Cosby, K. Diagnostic excellence. JAMA 2021;326:1905–6. https://doi.org/10.1001/jama.2021.19493.Suche in Google Scholar PubMed

2. Committee on Diagnostic Error in Health Care, Board on Health Care Services, Institute of Medicine, The National Academies of Sciences, Engineering, and Medicine. Improving diagnosis in health care. Balogh, EP, Miller, BT, Ball, JR, editors. Washington, DC: National Academies Press (US); 2015.Suche in Google Scholar

3. Measure Dx: a resource to identify, analyze, and learn from diagnostic safety events. Content last reviewed April 2023. Rockville, MD: Agency for Healthcare Research and Quality. Available from: https://www.ahrq.gov/patient-safety/settings/multiple/measure-dx.html.Suche in Google Scholar

4. Kwan, JL, Singh, H. General internists in pursuit of diagnostic excellence in primary care: a #ProudtobeGIM thread that unites us all. J Gen Intern Med 2018;33:395–6. https://doi.org/10.1007/s11606-018-4343-8.Suche in Google Scholar PubMed PubMed Central

5. Singh, H, Giardina, TD, Meyer, AN, Forjuoh, SN, Reis, MD, Thomas, EJ. Types and origins of diagnostic errors in primary care settings. JAMA Intern Med 2013;173:418–25. https://doi.org/10.1001/jamainternmed.2013.2777.Suche in Google Scholar PubMed PubMed Central

6. Newman-Toker, DE, Wang, Z, Zhu, Y, Nassery, N, Saber Tehrani, AS, Schaffer, AC, et al.. Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: toward a national incidence estimate using the “big three”. Diagnosis 2020;8:67–84. https://doi.org/10.1515/dx-2019-0104.Suche in Google Scholar PubMed

7. Kostopoulou, O, Delaney, BC, Munro, CW. Diagnostic difficulty and error in primary care–a systematic review. Fam Pract 2008;25:400–13. https://doi.org/10.1093/fampra/cmn071.Suche in Google Scholar PubMed

8. Newman-Toker, DE, Peterson, SM, Badihian, S, Hassoon, A, Nassery, N, Parizadeh, D, et al.. Diagnostic errors in the emergency department: a systematic review. Comparative effectiveness review No. 258. (prepared by the Johns Hopkins University Evidence-based Practice Center under Contract No. 75Q80120D00003.) AHRQ Publication No. 22(23)-EHC043. Rockville, MD: Agency for Healthcare Research and Quality; 2022. Available from: https://effectivehealthcare.ahrq.gov/products/diagnostic-errors-emergency/research.Suche in Google Scholar

9. Matulis, JC, Kok, SN, Dankbar, EC, Majka, AJ. A survey of outpatient Internal Medicine clinician perceptions of diagnostic error. Diagnosis 2020;7:107–14. https://doi.org/10.1515/dx-2019-0070.Suche in Google Scholar PubMed

10. Goyder, CR, Jones, CH, Heneghan, CJ, Thompson, MJ. Missed opportunities for diagnosis: lessons learned from diagnostic errors in primary care. Br J Gen Pract 2015;65:e838–44. https://doi.org/10.3399/bjgp15x687889.Suche in Google Scholar PubMed PubMed Central

11. Cassel, C, Fulmer, T. Achieving diagnostic excellence for older patients. JAMA 2022;327:919–20. https://doi.org/10.1001/jama.2022.1813.Suche in Google Scholar PubMed

12. Schiff, GD, Hasan, O, Kim, S, Abrams, R, Cosby, K, Lambert, BL, et al.. Diagnostic error in medicine: analysis of 583 physician-reported errors. Arch Intern Med 2009;169:1881–7. https://doi.org/10.1001/archinternmed.2009.333.Suche in Google Scholar PubMed

13. Schiff, GD. Finding and fixing diagnosis errors: can triggers help? BMJ Qual Saf 2012;21:89–92. https://doi.org/10.1136/bmjqs-2011-000590.Suche in Google Scholar PubMed

14. Schiff, GD, Volodarskaya, M, Ruan, E, Lim, A, Wright, A, Singh, H, et al.. Characteristics of disease-specific and generic diagnostic pitfalls: a qualitative study. JAMA Netw Open 2022;5:e2144531. https://doi.org/10.1001/jamanetworkopen.2021.44531.Suche in Google Scholar PubMed PubMed Central

15. OECD Health Statistics 2022 – OECD. Available from: https://www.oecd.org/health/health-data.htm.Suche in Google Scholar

16. International Classification of Primary Care – 3rd Revision. Available from: https://icpc-3.info/.Suche in Google Scholar

17. International Classification of Diseases 11th Revision. Available from: https://icd.who.int/en.Suche in Google Scholar

18. Orphanet: an online database of rare diseases and orphan drugs. https://www.orpha.net/consor/cgi-bin/index.php [Accessed 16 Mar 2023].Suche in Google Scholar

19. UpToDate. https://www.uptodate.com/contents/search. Registration and login required [Accessed 16 Mar 2023].Suche in Google Scholar

20. DynaMed. Ipswich (MA): EBSCO information services; 1995. https://www.dynamed.com/. Registration and login required [Accessed 16 Mar 2023].Suche in Google Scholar

21. Harada, T, Miyagami, T, Watari, T, Hiyoshi, T, Kunitomo, K, Tsuji, T, et al.. Analysis of diagnostic error cases among Japanese residents using diagnosis error evaluation and research taxonomy. J Gen Fam Med 2021;22:96–9. https://doi.org/10.1002/jgf2.388.Suche in Google Scholar PubMed PubMed Central

22. Singh, H, Thomas, EJ, Khan, MM, Petersen, LA. Identifying diagnostic errors in primary care using an electronic screening algorithm. Arch Intern Med 2007;167:302–8. https://doi.org/10.1001/archinte.167.3.302.Suche in Google Scholar PubMed

23. Schroeder, RM, Stunkel, L, Gowder, MTA, Kendall, E, Wilson, B, Nagia, L, et al.. Misdiagnosis of third nerve palsy. J Neuro Ophthalmol 2022;42:121–5. https://doi.org/10.1097/wno.0000000000001010.Suche in Google Scholar PubMed PubMed Central

24. Zhu, Y, Fan, Q, Cheng, L, Chen, B. Diagnostic errors in initial misdiagnosis of foreign body aspiration in children: a retrospective observational study in a tertiary care hospital in China. Front Pediatr 2021;9:694211. https://doi.org/10.3389/fped.2021.694211.Suche in Google Scholar PubMed PubMed Central

25. Stunkel, L, Sharma, RA, Mackay, DD, Wilson, B, Van Stavern, GP, Newman, NJ, et al.. Patient harm due to diagnostic error of neuro-ophthalmologic conditions. Ophthalmology 2021;128:1356–62. https://doi.org/10.1016/j.ophtha.2021.03.008.Suche in Google Scholar PubMed

26. Ely, JW, Kaldjian, LC, D’Alessandro, DM. Diagnostic errors in primary care: lessons learned. J Am Board Fam Med 2012;25:87–97. https://doi.org/10.3122/jabfm.2012.01.110174.Suche in Google Scholar PubMed

27. Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis 2017;4:239–40. https://doi.org/10.1515/dx-2017-0005.Suche in Google Scholar PubMed

28. Harada, T, Miyagami, T, Kunitomo, K, Shimizu, T. Clinical decision support systems for diagnosis in primary care: a scoping review. Int J Environ Res Publ Health 2021;18:8435. https://doi.org/10.3390/ijerph18168435.Suche in Google Scholar PubMed PubMed Central

29. Harada, T, Shimizu, T, Kaji, Y, Suyama, Y, Matsumoto, T, Kosaka, C, et al.. A perspective from a case conference on comparing the diagnostic process: human diagnostic thinking vs. artificial intelligence (AI) decision support tools. Int J Environ Res Publ Health 2020;17:6110. https://doi.org/10.3390/ijerph17176110.Suche in Google Scholar PubMed PubMed Central


Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/dx-2023-0030).


Received: 2023-03-16
Accepted: 2023-06-21
Published Online: 2023-08-10

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Reviews
  3. Diagnostic errors in uncommon conditions: a systematic review of case reports of diagnostic errors
  4. Routine blood test markers for predicting liver disease post HBV infection: precision pathology and pattern recognition
  5. Opinion Papers
  6. The challenge of clinical reasoning in chronic multimorbidity: time and interactions in the Health Issues Network model
  7. The first diagnostic excellence conference in Japan
  8. Clouds across the new dawn for clinical, diagnostic and biological data: accelerating the development, delivery and uptake of personalized medicine
  9. Original Articles
  10. Towards diagnostic excellence on academic ward teams: building a conceptual model of team dynamics in the diagnostic process
  11. Error codes at autopsy to study potential biases in diagnostic error
  12. Multicenter evaluation of a method to identify delayed diagnosis of diabetic ketoacidosis and sepsis in administrative data
  13. Detection of fake papers in the era of artificial intelligence
  14. Is language an issue? Accuracy of the German computerized diagnostic decision support system ISABEL and cross-validation with the English counterpart
  15. The feasibility of a mystery case curriculum to enhance diagnostic reasoning skills among medical students: a process evaluation
  16. Internal medicine intern performance on the gastrointestinal physical exam
  17. Scaling up a diagnostic pause at the ICU-to-ward transition: an exploration of barriers and facilitators to implementation of the ICU-PAUSE handoff tool
  18. Learned cautions regarding antibody testing in mast cell activation syndrome
  19. Diagnostic properties of natriuretic peptides and opportunities for personalized thresholds for detecting heart failure in primary care
  20. Incomplete filling of spray-dried K2EDTA evacuated blood tubes: impact on measuring routine hematological parameters on Sysmex XN-10
  21. Letters to the Editor
  22. The diagnostic accuracy of AI-based predatory journal detectors: an analogy to diagnosis
  23. Explainable AI for gut microbiome-based diagnostics: colorectal cancer as a case study
  24. Restless X syndrome: a new diagnostic family of nocturnal, restless, abnormal sensations of various body parts
  25. Erratum
  26. Retraction of: Establishing a stable platform for the measurement of blood endotoxin levels in the dialysis population
Heruntergeladen am 7.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/dx-2023-0030/html
Button zum nach oben scrollen