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Diagnostic performance dashboards: tracking diagnostic errors using big data
  1. Ketan K Mane1,
  2. Kevin B Rubenstein1,
  3. Najlla Nassery2,
  4. Adam L Sharp3,
  5. Ejaz A Shamim4,
  6. Navdeep S Sangha5,
  7. Ahmed Hassoon6,
  8. Mehdi Fanai7,
  9. Zheyu Wang8,
  10. David E Newman-Toker7
  1. 1 Mid-Atlantic Permanente Research Institute, Kaiser Permanente, Mid-Atlantic States, Rockville, Maryland, USA
  2. 2 Department of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  3. 3 Department of Emergency Medicine, Kaiser Permanente, Southern California, Los Angeles, California, USA
  4. 4 Department of Neurology, Kaiser Permanente, Mid-Atlantic States, Rockville, Maryland, USA
  5. 5 Department of Neurology, Kaiser Permanente, Southern California, Los Angeles, California, USA
  6. 6 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
  7. 7 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  8. 8 Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  1. Correspondence to Dr David E Newman-Toker, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA; toker{at}jhu.edu

https://doi.org/10.1136/bmjqs-2018-007945

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    In their 2015 report, Improving Diagnosis in Health Care, the National Academy of Medicine asserted that most individuals ‘will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences’1 and ‘improving the diagnostic process is not only possible, but it also represents a moral, professional, and public health imperative.’1 A key barrier to eliminating diagnostic errors is the lack of operational measures to track diagnostic performance.2 3

    Novel approaches using ‘big data’ to identify statistically meaningful patterns offer unique opportunities to operationalise measurement of diagnostic errors and misdiagnosis-related harms.4 In a study of ~190 000 US inpatient stroke admissions, the authors found missed opportunities to diagnose stroke early were often linked to clinical presentations with dizziness or vertigo.5 A graphical temporal profile analysis of treat-and-release emergency department (ED) visits showed an exponential increase in visit frequency in the days before stroke admission, establishing these as likely misdiagnoses.5

    We operationalised this approach by constructing a diagnostic performance dashboard to monitor diagnostic quality and safety. Kaiser Permanente-Mid Atlantic Permanente Medical Group (KP) and the Johns Hopkins University School of Medicine (JHM) partnered to build a learning ecosystem using visual analytics tools. Visual analytics combines expert knowledge with machine computational power for smart data exploration.6 Visual representations allow users to see the big picture and visually explore relevant data. Interactive data discovery supports ‘slice-and-dice’ operations with data drill-through capabilities, enabling exploratory data mining, hypothesis testing and decision making.

    Leveraging the exploratory data …

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    Footnotes

    • Contributors All authors have made substantial contributions to the conception or design of the work, or the acquisition, analysis or interpretation of data; drafting the work or revising it critically for important intellectual content and have approved the final work.

    • Funding This study was funded by Johns Hopkins - Kaiser Permanente Collaborative Pilot Program.

    • Competing interests None declared.

    • Patient consent Not required.

    • Ethics approval Mid-Atlantic Permanente Research Institute, Johns Hopkins University School of Medicine.

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

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