Journal Information
Journal ID (publisher-id): BM
Journal ID (nlm-ta): Biochem Med (Zagreb)
Title: Biochemia Medica
Abbreviated Title: Biochem. Med. (Zagreb)
ISSN (print): 1330-0962
ISSN (electronic): 1846-7482
Publisher: Croatian Society of Medical Biochemistry and Laboratory Medicine
Article Information
Copyright statement: Croatian Society of Medical Biochemistry and Laboratory Medicine.
Copyright: 2017, Croatian Society of Medical Biochemistry
License (open-access):
This is an Open Access article distributed under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Date received: 31 January 2018
Date accepted: 03 April 2018
Publication date (print and electronic): 15 June 2018
Volume: 28
Issue: 2
Electronic Location Identifier: 020503
Publisher ID: bm-28-2-020503
DOI: 10.11613/BM.2018.020503
Sigma metrics in laboratory medicine revisited: We are on the right road with the wrong map
Reliable procedures are needed to quantify the performance of instruments and methods in order to increase the quality in clinical laboratories. The Sigma metrics serves that purpose, and in the present study, the current methods for the calculation of the Sigma metrics are critically evaluated. Although the conventional model based on permissible (or allowable) total error is widely used, it has been shown to be flawed. An alternative method is proposed based on the within-subject biological variation. This model is conceptually similar to the model used in industry to quantify measurement performance, based on the concept of the number of distinct categories and consistent with the Six Sigma methodology. The quality of data produced in clinical laboratories is expected, however, to be higher than the quality of industrial products. It is concluded that this model is consistent with Six Sigma theory, original Sigma metrics equation and with the nature of patients’ samples. Therefore, it can be used easily to calculate the performance of measurement methods and instruments used in clinical laboratories.
Keywords: biological variation; Six Sigma; Sigma metrics; total error; number of distinct categories