Diagnostic errors in older patients: a secondary analysis of case reports
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Kotaro Kunitomo
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Yukinori Harada
Abstract
Objectives
Diagnostic errors are a significant source of patient harm and occur more frequently in older adults due to comorbidities, symptom ambiguity, and communication barriers. However, how these errors differ between older and younger patients remains unclear. The aim of this study was to examine the characteristics of diagnostic errors in older patients using published case reports.
Methods
We performed a secondary analysis of 534 case reports from a systematic review. Cases were divided into older (≥65 years, n=115) and younger (<65 years, n=419) groups. Data were extracted and coded using the diagnostic error evaluation and research (DEER), reliable diagnosis challenges (RDC), and generic diagnostic pitfalls (GDP) frameworks.
Results
Older patients had significantly more DEER codes per case than younger patients (2.5 vs. 2.0; p=0.01). Key DEER codes were more frequent in older adults, including “Physical examination: Failure in weighing” (7.8 vs. 2.9 %), “Assessment: Failure/delay in considering the diagnosis” (74.8 vs. 64.0 %), and “Assessment: Failure/delay to recognise/weigh urgency” (7.8 vs. 2.9 %). In RDC, “Diagnosis of complications” was also more common in older patients (11.3 vs. 5.3 %). No significant differences were found in GDP coding.
Conclusions
Diagnostic errors involving failure to consider the correct diagnosis, recognize urgency, and identify complications were more common in older patients. Understanding these mechanisms is essential to develop diagnostic strategies specific to older patients.
Acknowledgments
We would like to thank Editage (www.editage.jp) for English language editing.
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Research ethics: The local Institutional Review Board deemed the study exempt from review.
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Informed consent: Not applicable.
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Author contributions: K.K. and Y.H had full access to all of the data in the study, and they take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: K.K., Y.H. and T.S. Acquisition, analysis, or interpretation of data: K.K., Y.H. and T.S. Drafting of the manuscript: K.K., Y.H. and T.S. Critical review of the manuscript for important intellectual content: T.W., T.H. and T.S. Statistical analysis: K.K. and Y.H. Obtained funding: None. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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