ArticleBMJ quality & safety2018
Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): a conceptual framework and methodological approach for unearthing misdiagnosis-related harms using big data.
Article in BMJ quality & safety, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed.
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- Harbingers of sepsis misdiagnosis among pediatric emergency department patients.Diagnosis (Berlin, Germany) · 2025Article
- Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025Review
- Diagnostic inequalities relating to physical healthcare among people with mental health conditions: a systematic review.EClinicalMedicine · 2025Review
- Implementation of Electronic Triggers to Identify Diagnostic Errors in Emergency Departments.JAMA internal medicine · 2025Article
- Machine Learning to Enhance Electronic Detection of Diagnostic Errors.JAMA network open · 2024Article
- Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions.PLOS digital health · 2024Article
- Computable phenotype for diagnostic error: developing the data schema for application of symptom-disease pair analysis of diagnostic error (SPADE).Diagnosis (Berlin, Germany) · 2024Article
- Automating detection of diagnostic error of infectious diseases using machine learning.PLOS digital health · 2024Article
- Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study.JMIR formative research · 2024Article
- Review
- A taxonomy of early diagnosis research to guide study design and funding prioritisation.British journal of cancer · 2023Review
- Misdiagnosis of Posterior Reversible Encephalopathy Syndrome and Reversible Cerebral Vasoconstriction Syndrome in the Emergency Department.Journal of the American Heart Association · 2023Article
- Article
- Optimizing measurement of misdiagnosis-related harms using symptom-disease pair analysis of diagnostic error (SPADE): comparison groups to maximize SPADE validity.Diagnosis (Berlin, Germany) · 2023Article
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Abstract
backgroundThe public health burden associated with diagnostic errors is likely enormous, with some estimates suggesting millions of individuals are harmed each year in the USA, and presumably many more worldwide. According to the US National Academy of Medicine, improving diagnosis in healthcare is now considered 'a moral, professional, and public health imperative.' Unfortunately, well-established, valid and readily available operational measures of diagnostic performance and misdiagnosis-related harms are lacking, hampering progress. Existing methods often rely on judging errors through labour-intensive human reviews of medical records that are constrained by poor clinical documentation, low reliability and hindsight bias.
methodsKey gaps in operational measurement might be filled via thoughtful statistical analysis of existing large clinical, billing, administrative claims or similar data sets. In this manuscript, we describe a method to quantify and monitor diagnostic errors using an approach we call 'Symptom-Disease Pair Analysis of Diagnostic Error' (SPADE).
resultsWe first offer a conceptual framework for establishing valid symptom-disease pairs illustrated using the well-known diagnostic error dyad of dizziness-stroke. We then describe analytical methods for both look-back (case-control) and look-forward (cohort) measures of diagnostic error and misdiagnosis-related harms using 'big data'. After discussing the strengths and limitations of the SPADE approach by comparing it to other strategies for detecting diagnostic errors, we identify the sources of validity and reliability that undergird our approach.
conclusionSPADE-derived metrics could eventually be used for operational diagnostic performance dashboards and national benchmarking. This approach has the potential to transform diagnostic quality and safety across a broad range of clinical problems and settings.
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