Diagnostic error patterns in case reports: a hierarchical cluster analysis.

Authors

Toshinori Nishizawa, Yukinori Harada, Taro Shimizu

Published in

Diagnosis (Berlin, Germany). Nov 10, 2026. Epub Nov 10, 2026.

Abstract

Diagnostic errors arise through complex interactions between diagnostic process failures, contextual factors, and cognitive pitfalls. However, most previous studies examined these factors individually, providing limited insights into how diagnostic breakdowns co-occur in clinically challenging cases. We aimed to identify recurrent diagnostic failure modes using a hierarchical cluster analysis of published case reports of diagnostic errors.
We performed a secondary analysis of previously published systematic reviews of diagnostic-error case reports. Diagnostic process failures were coded using the diagnostic error evaluation and research (DEER) taxonomy. Hierarchical clustering using Ward's method and the Euclidean distance was applied to identify the patterns of co-occurring DEER failure points. The Caliński-Harabasz pseudo-F index was used to evaluate cluster structures. Reliable Diagnosis Challenges and Generic Diagnostic Pitfalls frameworks were used to characterize the contextual and cognitive contributors within clusters.
A total of 563 cases of diagnostic errors were analyzed. Cluster analysis revealed two main categories of failure modes: (1) test ordering and diagnostic prioritization failure, in which mimicking presentations were more common, and (2) testing interpretation and diagnostic synthesis failure. The latter category was further divided into three clinically relevant subtypes: laboratory/radiological reading failure, test interpretation failure, and diagnostic synthesis failure despite preserved information acquisition.
This study identified exploratory patterns of co-occurring diagnostic process failures in published case reports. These patterns require prospective validation in independent multicenter clinical datasets before they can be used to guide diagnostic safety interventions.

PMID:
43002468
Bibliographic data and abstract were imported from PubMed on 10 Nov 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.