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Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): a conceptual framework and methodological approach for unearthing misdiagnosis-related harms using big data
  1. Ava L Liberman1,
  2. David E Newman-Toker2,3
  1. 1Department of Neurology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA
  2. 2Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  3. 3Departments of Epidemiology and Health Policy and Management, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
  1. Correspondence to Dr David E Newman-Toker, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; toker{at}jhu.edu

Abstract

Background The 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.

Methods Key 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).

Results We 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.

Conclusion SPADE-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.

  • diagnostic errors
  • patient harm
  • outcome measures/methods
  • process measures/methods
  • public health informatics/methods
  • epidemiology/diagnosis

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Footnotes

  • Funding National Institute on Deafness and Communication Disorders (grant #U01 DC013778) and the Armstrong Institute Center for Diagnostic Excellence.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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