**3. Salient features**

Westgard in 1975 (**Figure 3**). A year after, the use of anion gap equation for automated blood gas and electrolyte analyzers quality control was advocated [16] by David Witte and co-workers. "A multi-rule Shewhart Chart for quality control in clinical chemistry" was published during the 1980s by Westgard, marking a major breakthrough in quality control for laboratories. The simple rules explaining implementation of the Levy-Jennings chart were given in this chapter. The initial international quality standard for operations in a clinical laboratory was also established during the 1980s. During the 1990s, the theoretical and practical application of biological variances as analytical targets in clinical chemistry [18, 19] were worked upon by Fraser and his co-workers, distinguishable among them being Eugene Harris, the American clinical chemist, who was instrumental in contributing to the formulation of the theory of biological variances through his expertise of statistics and informatics [20]. Another notable contribution is that of Carmen Ricos [21–23] and her group of Spanish researchers (majority), who were responsible for collecting data on quality specifications and biological variances a number of

The "OPSpecs charts" [24] concept was proposed by Westgard in 1994. Non-analytical errors, that is, errors that occur before or after analysis, were also discussed extensively during the 1990s. Configuration of laboratory information systems (LISs) led to the prevention of

**Figure 3.** Professor James O. Westgard is President of Westgard QC, Inc., a small business providing education and training for laboratory quality management. He is an Emeritus Professor in the Department of Pathology and Laboratory Medicine at the University of Wisconsin Medical School [reference: https://www.labqualityconfab.com/speakers/james-

post-analytical errors and some types of pre-analytical errors.

biochemical parameters.

4 Quality Control in Laboratory

o-westgard] [17].

The QC should proceed through three parts, mainly:

	- (I) The cause of the error should be found out.
	- (II) Action should be taken to correct the error.
	- (III) The patients' data should be re-analyzed.

Multirule procedure: this includes decision criteria to determine if an analytic run is in control; it is used to detect random and systemic error over time and is developed by Westgard and Groth [26].

Proficiency testing, internal quality control, laboratory inspections, clinical utilization and quality assurance monitoring play an important role as indicators of analytic performance. Management of quality consists of quality design, quality control and quality improvement [26] (**Figure 4**).

**Figure 4.** Relation between various aspects of quality control [26].

Use of automated analyzers in clinical laboratories: nowadays, almost every laboratory uses automated analyzers. The reason is that they are more reliable, can process more samples at a time, and are time saving and also cost saving in the long run. Most companies provide the quality control material along with the quality control guide. This has made it easier for laboratories to assess quality of the various types of parameters.

[11] Ductra F. Monitoring the quality of blood cell counts with replicate determinations on routine samples. American Journal of Clinical Pathology. 1966;**46**(2):286-288

Introductory Chapter: History and Scope of Quality Control in Laboratories

http://dx.doi.org/10.5772/intechopen.74593

7

[12] Hoffmann R, Waid M. The "average of normals" method of quality control. American

[13] Bull B. Α study of various estimators for derivation of quality control procedures from erythrocyte indices. American Journal of Clinical Pathology. 1974;**61**:473-481

[14] Aronson T, de Verder C-H, Groth T. Factors influencing the quality of analytical methods—A systems analysis with use of computer simulation. Clinical Chemistry.

[15] Nosanchuk J, Gottmann A. Cums and delta checks. American Journal of Clinical

[16] Witte D, Rodgers J, Barrett D. The anion gap: Its use in quality control. Clinical Chemistry.

[18] Fraser C, Harris E. Generation and application of data on biological variation in clinical

[19] Harris E, Kanofsky P, Shakarji G, Cotlove E. Biological and analytic components of variation in long-term studies of serum constituents in normal subjects. Clinical Chemistry.

[20] Ricós C, Alvarez V, Cava F, García-Lario JV, Hernández A, Jiménez CV, Minchinela J, Perich C, Simón M. Current databases on biological variation: pros, cons and progress.

[21] Westgard J, Quam E, Barry P. QC Selection grids for planning QC procedures. Clinical

[23] Westgard J, Burnet R, Bowers G. Quality management science in clinical chemistry: A dynamic framework for continuous improvement of quality. Clinical Chemistry.

[24] Lippi G. Governance of preanalytical variability and error detection. JMB. 2008;**25**(3):

[25] Westgard JO, Groth T. Power functions for statistical control rules. Clinical Chemistry.

[26] Bishop ML, Fody EP, Schoeff LE. Clinical Chemistry: Principles, Techniques, and Correlations. 7th ed. Lippincott Williams and Wilkins. 2013. ISBN-10: 1451118694,

Scandinavian Journal of Clinical and Laboratory Investigation. 1999;**59**:491-500

chemistry. Critical Reviews in Clinical Laboratory Sciences. 1969;**27**:409-437

[17] https://www.labqualityconfab.com/speakers/james-o-westgard]

[22] http://www.jano.es/noticia-carmen-ricos-premiada-con-el-18844

Journal of Clinical Pathology. 1965;**43**:134-141

1974;**20**:738-748

1976;**22**:643-646

1970;**16**(12):1022-1027

1990;**36**:1712-1716

1979;**25**:863-869

ISBN-13: 978-1451118698

337-338

Laboratory Science. 1990;**3**:271-278

Pathology. 1974;**62**:707-712
