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Risk-based quality control: planning, defining, linking and evaluating

By moving away from fixed quality control schedules and embracing a framework rooted in clinical risk and analytical performance, laboratories can tailor their quality systems to reflect real-world challenges. Here, Stephen MacDonald introduces the role of analytical performance specifications, Sigma metrics, and the MaxE(nuf) model.

Having discussed in previous articles the frameworks and tools available for risk-based practice, we now move onto another specific application of this approach – in internal quality control (IQC). Risk-based quality control shifts the focus to a clinical context, away from the purely analytical focus that has long been used. This article introduces how laboratories can approach quality control while accounting for patient risk; although it is a large and complex topic that is still being updated, probably as we read this!

Risk-based approach to QC planning 

The traditional approach to quality control (QC) in medical laboratories has long relied on fixed, routine schedules – running QC once per shift, or per day – regardless of the analytical performance of the test or the clinical context in which results are used. While this approach provides a baseline level of assurance, it falls short in recognising that not all assays carry equal clinical risk, and not all analysers perform with the same consistency. The shift towards a risk-based QC model offers a more rational and patient-centred alternative. 

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