Establishing Evidence-Based Statistical Quality Control Practices

Abstract

AbstractObjectivesTo establish an objective, scientific, evidence-based process for planning statistical quality control (SQC) procedures based on quality required for a test, precision and bias observed for a measurement procedure, probabilities of error detection and false rejection for different control rules and numbers of control measurements, and frequency of QC events (or run size) to minimize patient risk.MethodsA Sigma-Metric Run Size Nomogram and Power Function Graphs have been used to guide the selection of control rules, numbers of control measurements, and frequency of QC events (or patient run size).ResultsA tabular summary is provided by a Sigma-Metric Run Size Matrix, with a graphical summary of Westgard Sigma Rules with Run Sizes.ConclusionMedical laboratories can plan evidence-based SQC practices using simple tools that relate the Sigma-Metric of a testing process to the control rules, number of control measurements, and run size (or frequency of QC events).