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 instances within the data set where a patient with that symptom has two coded encounters in a short time frame. Misdiagnosis-related harm is inferred when there is a prespecified change in diagnosis over time. An example is a patient with acute dizziness (the symptom) who is discharged with a diagnosis of positional vertigo at an initial emergency department (ED) encounter and 1 week later returns to the ED and is diagnosed with an acute stroke (the disease).

The Symptom–Disease Pair at the core of the SPADE methodology refers to the presenting symptom (eg, dizziness) and the correct, but initially overlooked, diagnosis (eg, stroke). The validity of any symptom–diagnosis pair as a marker of diagnostic error is established using two approaches. A ‘look forward’ approach begins by identifying all patients who presented with dizziness and were discharged with a diagnosis of benign positional vertigo. It then looks at how often these patients present again within …