The data of diagnostic error: big, large and small

Diagnostic error research has mostly focused on methods to detect, characterise and analyse lapses in the diagnostic process by using incident reports, malpractice claims, autopsies and electronic trigger tools. The associated literature shows how frequently important diagnostic errors occur1 and examines cognitive2 and system-based3 causes of these errors. Relatively absent from this portfolio of research have been large-scale approaches for measuring institutional diagnostic performance, either for benchmarking purposes or for driving improvement efforts. 

In this issue of BMJQS, Liberman and Newman-Toker introduce Symptom–Disease Pair Analysis of Diagnostic Error (SPADE) as a new approach to identify diagnostic errors by analysing large patient data  sets (tens of thousands of patient encounters housed in electronic medical records or administrative databases).4 The SPADE methodology starts with a symptom that is misdiagnosed at an appreciable rate such as chest pain or dizziness. It then looks for...

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