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1. Introduction
1.1 The automated analyzers in clinical laboratories
Nowadays, the overwhelming majority of laboratory results in clinical laboratories is being generated by automated analyzers. Modern automated analyzers are highly sophisticated instruments which can produce a tremendous number of laboratory results in a very short time. This is achieved thanks to the integration of technologies from three different scientific fields: analytical chemistry, computer science and robotics. The combination of these technologies substitutes a huge number of glassware equipment and tedious, repetitive laboratory work. As a matter of fact, the laboratory routine work has diminished significantly. Today laboratory personnel’s duties have been shifted from manual work to the maintenance of the equipment, internal and external quality control, instrument calibration and data management of the generated results.
1.2 Statistical Quality control in industrial production
Quality control is an ancient procedure. For centuries manufacturers checked the quality of their products trying to find early any defect. At that time, manufacturers checked every product, one by one, without exception. Today, in industrial business, monitoring the quality of the each product is unattainable due to the large-scale production of different goods. Modern quality control is used to check the quality of a minimum number of samples from the total production. The procedure is called statistical quality control (SQC) or statistical process control (SPC). SQC is faster and more efficient than single checking.
The most general definition of SQC is: “SQC is process that minimizes the variability of a procedure” although it would be wiser to define SQC as “The process that focuses on revealing any deviations from well defined standards”.
1.3 SQC in clinical laboratories’ automated analyzers
SQC can be used in every automated production, like the laboratory determinations which are performed by biomedical analyzers. Unlike the industrial business where all products are similar, the laboratory determinations are totally different because of the huge biological differences among human beings. As a result, SQC can be done only for the equipment and the analytical methods and rarely for each laboratory result. SQC of automated analyzers uses as samples not the patients’ results but the results of some special samples, the control samples.
The aim of this chapter is the introduction to the statistical quality control for automated analyzers in biomedical sciences such as haematology and biochemistry. The most important relevant laboratory SQC methods will be described.

Am J Clin Pathol 2006;125:343-354

To assess the analytic quality of laboratory testing in the United States, we obtained proficiency testing survey results from several national programs that comply with Clinical Laboratory Improvement Amendments (CLIA) regulations. We studied regulated tests (cholesterol, glucose, calcium, fibrinogen, and prothrombin time) and nonregulated tests (international normalized ratio [INR], glycohemoglobin, and prostate-specific antigen [PSA]). Quality was assessed on the σ scale with a benchmark for minimum process performance of 3 σ and a goal for world-class quality of 6 σ. Based on the CLIA criteria for acceptable performance in proficiency testing (allowable total errors [TEa]), the national quality of cholesterol testing (TEa = 10%) estimated σ values as 2.9 to 3.0; glucose (TEa = 10%), 2.9 to 3.3; calcium (TEa= 1.0 mg/dL),2.8 to 3.0; prothrombin time (TEa= 15%), 1.8; INR(TEa=20%), 2.4 to 3.5; fibrinogen (TEa= 20%), 1.8 to3.2; glycohemoglobin (TEa= 10%), 1.9 to 2.6; and PSA (TEa =10%), 1.2 to 1.8. The analytic quality of laboratory tests requires improvement in measurement performance and more intensive quality control monitoring than the CLIA minimum of 2 levels per day

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What is Quality Control?
Quality Control in the clinical laboratory is a system designed to increase the probability that each result rep o rted by the laboratory is valid and can be used with confidence by the physician making a diagnostic or therapeutic decision. Quality control (QC)procedures function by detecting analytical errors; ideally any error large enough to invalidate the medical usefulness of laboratory results should be detected. In practice, many QC procedures operate by submitting controls (sample materials well characterized by previous testing) to the laboratory testing process, and comparing the results of current testing to an expected range of values derived from previous testing