Establishing Evidence-Based Statistical Quality Control Practices.

Area of Science:

• Clinical Chemistry
• Laboratory Medicine
• Quality Management

Background:

• Statistical quality control (SQC) is crucial for reliable laboratory testing.
• Current SQC planning often lacks an objective, evidence-based framework.
• Minimizing patient risk requires optimizing SQC parameters.

Purpose of the Study:

• To develop an objective, scientific, evidence-based process for planning SQC procedures.
• To minimize patient risk by integrating test quality, measurement performance, and error probabilities.
• To provide practical tools for laboratory quality management.

Main Methods:

• Utilized Sigma-Metric Run Size Nomogram and Power Function Graphs.
• Guided selection of control rules, number of control measurements, and QC event frequency.
• Developed a Sigma-Metric Run Size Matrix and graphical summary of Westgard Sigma Rules.

Main Results:

• A Sigma-Metric Run Size Matrix provides tabular summaries.
• Graphical summaries of Westgard Sigma Rules with Run Sizes are available.
• Tools effectively link Sigma-Metric to control rules, measurements, and run size.

Conclusions:

• Medical laboratories can implement evidence-based SQC practices.
• Simple tools facilitate planning by relating Sigma-Metric to control parameters.
• Optimized SQC planning enhances laboratory testing quality and patient safety.