Examining atypical presentations associated with a higher risk of diagnostic errors using a new classification model

Area of Science:

• Medical diagnosis
• Clinical presentation analysis
• Diagnostic error research

Background:

• Diagnostic errors pose significant patient safety risks.
• Atypical presentations are often associated with increased diagnostic challenges.
• Existing classification models may not fully capture the nuances of diagnostic errors.

Purpose of the Study:

• To apply a novel classification model to clinical presentations.
• To identify a subtype of atypical presentations associated with higher diagnostic error risk.
• To analyze diagnostic error data collected using disease-agnostic methods.

Main Methods:

• Classified diagnostic error cases from a systematic review using a new model.
• Defined four patterns of atypical presentations linked to higher diagnostic error risk.
• Characterized the atypical group using Diagnostic Error Evaluation and Research (DEER) and Reliable Diagnosis Challenges (RDC) taxonomy codes.

Main Results:

• Included 382 cases; 9.4% were classified as atypical presentations.
• The atypical group showed higher frequencies of RDC codes: 'Atypical presentations', 'Findings masking/mimicking another diagnosis', 'Equivocal results/interpretation', and 'Recognizing misdiagnosis occurrence'.
• No significant differences in DEER taxonomy code distribution were observed between typical and atypical groups.

Conclusions:

• A new model successfully detected atypical presentations associated with increased diagnostic error risk.
• Atypical presentations present distinct diagnostic challenges compared to typical presentations.
• The model supports future research to reduce diagnostic errors in patients with high-risk atypical presentations.