The History of Statistical Quality Control in Clinical Chemistry and Haematology (1950 – 2010)
Petros Karkalousos1, Angelos Evangelopoulos
Int. J. Bio. Lab. Sci  2015  4:1-11 Abstract】 PDF

Statistical quality control in clinical chemistry and haematology has a tradition of almost 60 years. The most important landmarks were the adoption of control charts by Levey and Jennings (1950), the use of different control levels by Henry and Segalove (1952), the preparation of specific control samples by Freier and Rausch (1958) and the invention of control rules by Westgard et al (1981). Other methods attempted to utilize patient samples; these include the invention of the average of normals by Hoffmann and Waid (1965), the moving average in haematology by Bull (1973), the delta check by Nosanchuk and Gottmann (1974), the use of the anion gap by Witte (1975) and the use of retained whole blood samples in haematology by Cembrowski (1988). The selection of the appropriate method is aided by the use of the power functions (Westgard et al, 1979), the Operational Process Specifications charts (Westgard et al, 1994), and the Six Sigma method (Westgard, 2001). All these tools compare the performance of an analytical method to relevant quality goals.For use as quality goals, either the analytical goals (USA) or the biological variances (Europe) are usually selected. The use of biological variances was introduced by Fraser (1969) and achieved widespread application since Ricós et al collected variance values for a large number of analytes (1999). Along with internal quality assessment, statistical methods for external quality assessment were also developed. Important landmarks were the first interlaboratory quality control procedure by William Sunderman in the USA (1949), the invention of the Youden diagram (1959), and the implementation of specific quality control rules in the external quality assessment schemes by Cembrowski (1997). The introduction of uncertainty in 1993 changed the way of the estimation of values’dispersion.In conclusion, modern laboratories have a large variety of quality control methods to choose from. The choice of quality control goals and the abilities of their computer system will guide them to the appropriate methods.
Key words: Average of Normals, Control Rules, Statistical Quality Control, Power functions, Operational Process Specifications charts

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