QUALITY MANAGEMENT
considered during initial risk assessments, evaluating QC failure risk is about making that impact measurable over time. This includes tracking the frequency of QC rule violations, determining whether those failures reflect random variation or meaningful shifts, and identifying which failures are likely to have consequences for patients. The combination of these factors – how often errors occur and how harmful they could be – forms the basis of an ongoing risk profile for each assay or testing system. Tools such as risk matrices, severity
of harm scales, and risk priority scoring continue to play a role here, but the focus shifts from theoretical risk to real-world data. When a QC failure does occur, the response must be both systematic and proportionate. Not all failures require the same level of intervention; the urgency and depth of response should reflect the residual risk to the patient. For example, a violation involving a low-risk, stable assay may warrant only brief verification, while a similar event involving a high-stakes marker with narrow clinical decision limits may demand full investigation and root cause analysis.
Risk-based QC is not achieved just
through planning, but also the ability to detect when controls are no longer effective and to act decisively when that
Argument
Calibration and maintenance
Pooling data
Risk-based viewpoint
Details
Devices are calibrated and maintained identically, and thus, their performance should be statistically comparable.
Leads to more precise estimates of mean and SD, enhancing the stability and robustness of control limits.
Focus should be on minimising the overall probability of undetected error, aligning with MaxE(nuf) principles.
Table 3. Rationale in favour of common mean and SD.
happens. This includes not only correcting the immediate problem, but analysing trends to identify underlying issues that might recur. A single isolated QC event may not carry significant risk, but repeated patterns of failure – particularly if they go unexplored – can quickly erode the reliability of the entire testing process. Evaluating QC failure risk, then, is not the end of the risk management cycle, but a critical feedback loop. It enables laboratories to refine their controls, reassess their assumptions, and ensure that their strategies remain aligned with both analytical performance and clinical safety.
Risk-based QC frequency using the Parvin MaxE(nuf) model In traditional laboratory practice, the frequency of IQC checks is often fixed
– commonly once per day or once per shift – regardless of the test’s risk profile or analytical performance. While straightforward, this approach does not account for test-specific variability, failure risk, or the potential clinical consequences of reporting erroneous results. The Parvin MaxE(nuf) model, developed by Curtis Parvin and embedded in the logic behind statistical QC planning, offers a quantitative, risk-based method for optimising QC frequency, particularly well-aligned with the expectations of ISO 15189:2022, CLSI EP23-A, and ISO 22367:2020.
n Understanding the MaxE(nuf) model MaxE(nuf), short for Maximum Expected Number of Unacceptable Final patient results, quantifies the risk of reporting clinically erroneous patient results that
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