Diagnostic Error Assessment in Healthcare Systems
Summary
Diagnostic error constitutes a significant threat to patient safety and quality of care, arising when a diagnosis is delayed, missed or incorrect. Assessment of diagnostic error involves identifying instances of misdiagnosis through retrospective record review, prospective surveillance using electronic health records and novel computational tools, and structured feedback mechanisms. Traditional approaches rely on manual chart audits, root-cause analyses and voluntary reporting systems, which are time-consuming and may underestimate true incidence. Advances in information technology have enabled automated detection of potential diagnostic divergence and supported real-time oversight, while consensus-based instruments standardise error measurement across settings. Emerging frameworks also incorporate patient perspectives and interprofessional collaboration to enrich case identification and highlight system-wide contributory factors. An accurate and reproducible assessment strategy is essential for benchmarking performance, informing targeted interventions and ultimately reducing preventable harm on a global scale.
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Diagnostic Error Assessment in Healthcare Systems publication trend
The graph below shows the total number of articles in diagnostic error assessment in healthcare systems across all publications each year (not limited to Nature Index journals).
Technical terms
Diagnostic error: A delayed, missed or incorrect diagnosis that may lead to patient harm.
Electronic health record (EHR): A digital platform that stores comprehensive patient data for clinical care and review.
Machine learning: Computational methods that enable systems to learn patterns from data to make predictions or classifications.
Natural language processing (NLP): Techniques in artificial intelligence for analysing and interpreting human language in text form.
Safer Dx Instrument: A standardised questionnaire-based tool for detecting diagnostic error through retrospective record review.
References
- The challenges in defining and measuring diagnostic error. Diagnosis (2015).
- Automating detection of diagnostic error of infectious diseases using machine learning. PLOS Digital Health (2024).
- Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study. Journal of Medical Internet Research (2024).
- Accuracy of the Safer Dx Instrument to Identify Diagnostic Errors in Primary Care. Journal of General Internal Medicine (2016).
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