QUALITY MANAGEMENT
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.
At the heart of risk-based QC planning is the principle that the frequency, type,
At the heart of risk-based QC planning is the principle that the frequency, type, and complexity of QC should reflect the likelihood of failures and potential impact of errors.
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and complexity of QC should reflect the likelihood of failures and potential impact of errors. Instead of applying blanket rules, laboratories assess the specific risks associated with each test method. This means identifying where failures could occur, understanding how likely
they are, and evaluating the severity of potential clinical consequences if errors go undetected. In doing so, the focus shifts from compliance for its own sake to preventing patient harm. CLSI EP23-A promotes a structured
approach that begins with mapping the entire testing process – pre-analytical, analytical, and post-analytical – so that laboratories can systematically identify points of vulnerability – this again sounds familiar from previous articles. Laboratories can tailor their QC plans based on a combination of internal data, clinical priorities, and manufacturer recommendations. For instance, high- risk tests or those with narrow clinical decision limits may warrant more frequent QC or stricter rules, while highly stable, low-risk assays may require fewer interventions without compromising
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