Mohammad Farhadi Langeroudi - Rasad Pathobiology & Genetic Lab
Reza Mohammadi - Rasad Pathobiology & Genetic Lab
Seyed Mohammad Hassan Hashemi Madani - Rasad Pathobiology & Genetic Lab
Alireza Fallah - Rasad Pathobiology & Genetic Lab

Clinical laboratories have a long history using statistical internal quality control (IQC) as a tool for detecting analytical error before reporting patient results. Despite this long period, IQC has not matured into a well-developed practice. Internal quality control is a system which must differentiate unallowable error of measuring method or signal from stable inherent error of the method or noise, so this system must be able to detect unallowable error and preventing of false rejection of measuring run. Quality is often described as doing the right thing right . The quality of IQC depends on doing the right IQC right. In addition to selecting appropriate control materials and setting control limits correctly, for correct performance of an IQC system, it is needs using correct number of control measurements and applying the correct statistical control rules for interpreting results, which are determined according to the quality required for the test and the observed performance of the method. Choosing inappropriate number of control measurements and applying incorrect statistical control rules could result in decreasing error detection and increasing false rejection. Today, method performance is determined by sigma metrics, according to allowable total error of the test along with bias and imprecision of the method. When method performance is six sigma, applying 12s rule, and even 13s rule, is not correct and results in increased false rejection. In contrast, when these rules are not applied for a method with 3 sigma performance, decreasing error detection occurs.

6 Sigma, Quality Control