Startseite Association between referral letters and diagnostic errors: a single-center, cross-sectional study in general internal medicine in Japan
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Association between referral letters and diagnostic errors: a single-center, cross-sectional study in general internal medicine in Japan

  • Sakura Kamiya , Toshinori Nishizawa EMAIL logo , Hiroki Ozawa , Yukinori Harada , Takashi Watari , Taro Shimizu , Madoka Sakurai , Yuya Suzuki , Gautam A. Deshpande und Hiroko Arioka
Veröffentlicht/Copyright: 22. August 2025
Diagnosis
Aus der Zeitschrift Diagnosis

Abstract

Objectives

Referral documentation may either contribute to diagnostic excellence or play a role in diagnostic errors (DEs), but its exact impact remains unclear. This study investigates the association between referral documentation and DEs among patients initially evaluated by another hospital or department and subsequently referred to the general internal medicine (GIM) outpatient clinic of an acute care tertiary hospital in Japan.

Methods

This cross-sectional study analyzed outpatients who visited the GIM outpatient clinic between April 1, 2017 and March 31, 2023. Patients initially evaluated at another medical facility or department, who then visited the GIM outpatient clinic, and were subsequently readmitted unexpectedly within 14 days after GIM outpatient clinic visit were included. DEs were identified using the Revised Safer Dx Instrument. Errors were analyzed using the Diagnostic Error Evaluation and Research (DEER) taxonomy. Logistic regression analysis was performed to assess the relationship between referral letters and DEs.

Results

Of 80 patients, 29 (36.3 %) experienced DEs. Referral letters were present for 52 (65.0 %) patients. The proportion of DEs was lower in the referred patients compared to non-referred patients (25.0 vs. 57.1 %; p-value=0.004). After adjusting for age, sex, race, multimorbidity, type of previous physicians, and post-graduate year of the GIM physician, the presence of a referral letter was associated with a substantially likelihood of DEs (OR=0.20, 95 % CI: 0.06–0.62, p-value=0.005).

Conclusions

The presence of a referral letter facilitates accurate diagnoses while markedly reducing DEs. Healthcare systems should consider promoting the proper use of referral systems.


Corresponding author: Toshinori Nishizawa, MD, Department of General Internal Medicine, St Luke’s International Hospital, Tokyo, Japan; Medical Quality Management Office, QI Center, St Luke’s International Hospital, Tokyo, Japan; and Department of Public Health, Institute of Science Tokyo, Tokyo, Japan, E-mail:

Acknowledgments

We sincerely thank Hardeep Singh, MD, MPH, Professor Medicine-Health Srvcs Research, Baylor College of Medicine, Houston, TX US, for his advice based on the results of our research.

  1. Research ethics: The protocol of this study was approved by the Institutional Ethics Committees at St. Luke’s International Hospital (23-J020).

  2. Informed consent: Informed consent was not required because this was a retrospective observational study. However, we allowed patients to opt out of this study.

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

  4. Use of Large Language Models, AI and Machine Learning Tools: We used Chat GPT for proofreading this manuscript. All revisions were carefully reviewed and verified by the authors.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

References

1. Malik, MA, Motta-Calderon, D, Piniella, N, Garber, A, Konieczny, K, Lam, A, et al.. A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts. Diagnosis (Berl) 2022;9:446–57. https://doi.org/10.1515/dx-2022-0032.Suche in Google Scholar PubMed PubMed Central

2. Singh, H, Schiff, GD, Graber, ML, Onakpoya, I, Thompson, MJ. The global burden of diagnostic errors in primary care. BMJ Qual Saf 2017;26:484–94. https://doi.org/10.1136/bmjqs-2016-005401.Suche in Google Scholar PubMed PubMed Central

3. Singh, H, Meyer, AN, Thomas, EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf 2014;23:727–31. https://doi.org/10.1136/bmjqs-2013-002627.Suche in Google Scholar PubMed PubMed Central

4. Tobin-Schnittger, P, O’Doherty, J, O’Connor, R, O’Regan, A. Improving quality of referral letters from primary to secondary care: a literature review and discussion paper. Prim Health Care Res Dev 2018;19:211–22. https://doi.org/10.1017/s1463423617000755.Suche in Google Scholar

5. O’Donnell, CA. Variation in GP referral rates: what can we learn from the literature? Fam Pract 2000;17:462–71. https://doi.org/10.1093/fampra/17.6.462.Suche in Google Scholar PubMed

6. Rosano, A, Loha, CA, Falvo, R, Van Der Zee, J, Ricciardi, W, Guasticchi, G, et al.. The relationship between avoidable hospitalization and accessibility to primary care: a systematic review. Eur J Publ Health 2013;23:356–60. https://doi.org/10.1093/eurpub/cks053.Suche in Google Scholar PubMed

7. Eva, KW, Cunnington, JPW. The difficulty with experience: does practice increase susceptibility to premature closure? J Continuing Educ Health Prof 2006;26:192–8. https://doi.org/10.1002/chp.69.Suche in Google Scholar PubMed

8. Croskerry, P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med 2002;9:1184–204. https://doi.org/10.1197/aemj.9.11.1184.Suche in Google Scholar

9. Amano, M, Harada, Y, Shimizu, T. Impact of disclosing a working diagnosis during simulated patient handoff presentation in the emergency department: correctness matters. Diagnosis 2024;12:61–7. https://doi.org/10.1515/dx-2024-0121.Suche in Google Scholar PubMed

10. Staal, J, Speelman, M, Brand, R, Alsma, J, Zwaan, L. Does a suggested diagnosis in a general practitioners’ referral question impact diagnostic reasoning: an experimental study. BMC Med Educ 2022;22. https://doi.org/10.1186/s12909-022-03325-7.Suche in Google Scholar PubMed PubMed Central

11. Meyer, FML, Filipovic, MG, Balestra, GM, Tisljar, K, Sellmann, T, Marsch, S. Diagnostic errors induced by a wrong a priori diagnosis: a prospective randomized simulator-based trial. J Clin Med 2021;10:826. https://doi.org/10.3390/jcm10040826.Suche in Google Scholar PubMed PubMed Central

12. 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;0.10.1515/dx-2024-0041Suche in Google Scholar PubMed

13. 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 Qual Saf 2024;33:386–94. https://doi.org/10.1136/bmjqs-2022-015436.Suche in Google Scholar PubMed

14. Toth, F. Integration vs separation in the provision of health care: 24 OECD countries compared. Health Econ Pol Law 2020;15:160–72. https://doi.org/10.1017/s1744133118000476.Suche in Google Scholar

15. Kaneko, M, Motomura, K, Mori, H, Ohta, R, Matsuzawa, H, Shimabukuro, A, et al.. Gatekeeping function of primary care physicians under Japan’s free-access system: a prospective open cohort study involving 14 isolated islands. Fam Pract 2019;36:452–9. https://doi.org/10.1093/fampra/cmy084.Suche in Google Scholar PubMed PubMed Central

16. Toyabe, S-I, Kouhei, A. Referral from secondary care and to aftercare in a tertiary care university hospital in Japan. BMC Health Serv Res 2006;6. https://doi.org/10.1186/1472-6963-6-11.Suche in Google Scholar PubMed PubMed Central

17. Aaronson, E, Jansson, P, Wittbold, K, Flavin, S, Borczuk, P. Unscheduled return visits to the emergency department with ICU admission: a trigger tool for diagnostic error. Am J Emerg Med 2020;38:1584–7. https://doi.org/10.1016/j.ajem.2019.158430.Suche in Google Scholar PubMed

18. Singh, H, Giardina, TD, Forjuoh, SN, Reis, MD, Kosmach, S, Khan, MM, et al.. Electronic health record-based surveillance of diagnostic errors in primary care. BMJ Qual Saf 2012;21:93–100. https://doi.org/10.1136/bmjqs-2011-000304.Suche in Google Scholar PubMed PubMed Central

19. Singh, H, Khanna, A, Spitzmueller, C, Meyer, AND. Recommendations for using the revised safer Dx instrument to help measure and improve diagnostic safety. Diagnosis 2019;6:315–23. https://doi.org/10.1515/dx-2019-0012.Suche in Google Scholar PubMed

20. Forster, AJ, O’Rourke, K, Shojania, KG, van Walraven, C. Combining ratings from multiple physician reviewers helped to overcome the uncertainty associated with adverse event classification. J Clin Epidemiol 2007;60:892–901. https://doi.org/10.1016/j.jclinepi.2006.11.019.Suche in Google Scholar PubMed

21. Forster, AJ, Taljaard, M, Bennett, C, van Walraven, C. Reliability of the peer-review process for adverse event rating. PLoS One 2012;7:e41239. https://doi.org/10.1371/journal.pone.0041239.Suche in Google Scholar PubMed PubMed Central

22. 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

23. Griffin, JA, Carr, K, Bersani, K, Piniella, N, Motta-Calderon, D, Malik, M, et al.. Analyzing diagnostic errors in the acute setting: a process-driven approach. Diagnosis (Berl) 2021;9:77–88. https://doi.org/10.1515/dx-2021-0033.Suche in Google Scholar PubMed PubMed Central

24. Hooftman, J, Dijkstra, AC, Suurmeijer, I, van der Bij, A, Paap, E, Zwaan, L. Common contributing factors of diagnostic error: a retrospective analysis of 109 serious adverse event reports from Dutch hospitals. BMJ Qual Saf 2024;33:642–51. https://doi.org/10.1136/bmjqs-2022-015876.Suche in Google Scholar PubMed PubMed Central

25. Charlson, ME, Pompei, P, Ales, KL, Mackenzie, CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis 1987;40:373–83.10.1016/0021-9681(87)90171-8Suche in Google Scholar PubMed

26. Gwet, K. Handbook of inter-rater reliability: the definitive guide to measuring the extent of agreement among raters. Fort Wayne, IN: Advanced Analytics, LLC; 2012.Suche in Google Scholar

27. Iida, K, Watari, T, Watanuki, S. The Japanese universal health insurance system in the context of diagnostic equity. Diagnosis 2024;11:335–6. https://doi.org/10.1515/dx-2024-0047.Suche in Google Scholar PubMed

28. Chao, S, Lotfi, J, Lin, B, Shaw, J, Jhandi, S, Mahoney, M, et al.. Diagnostic journeys: characterization of patients and diagnostic outcomes from an academic second opinion clinic. Diagnosis 2022;9:340–7. https://doi.org/10.1515/dx-2022-0029.Suche in Google Scholar PubMed


Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/dx-2024-0197).


Received: 2024-12-12
Accepted: 2025-07-30
Published Online: 2025-08-22

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 9.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/dx-2024-0197/pdf
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