In a contemporary clinical laboratory it is very common to have to assess the agreement between two quantitative methods of measurement. The correct statistical approach to assess this degree of agreement is not obvious. Correlation and regression studies are frequently proposed. However, correlation studies the relationship between one variable and another, not the differences, and it is not recommended as a method for assessing the comparability between methods. In 1983 Altman and Bland (B&A) proposed an alternative analysis, based on the quantification of the agreement between two quantitative measurements by studying the mean difference and constructing limits of agreement. The B&A plot analysis is a simple way to evaluate a bias between the mean differences, and to estimate an agreement interval, within which 95% of the differences of the second method, compared to the first one, fall. Data can be analyzed both as unit differences plot and as percentage differences plot. The B&A plot method only defines the intervals of agreements, it does not say whether those limits are acceptable or not. Acceptable limits must be defined a priori, based on clinical necessity, biological considerations or other goals. The aim of this article is to provide guidance on the use and interpretation of Bland Altman analysis in method comparison studies.

Laboratory diagnostics (i.e., the total testing process) develops conventionally through a virtual loop, originally referred to as “the brain to brain cycle” by George Lundberg. Throughout this complex cycle, there is an inherent possibility that a mistake might occur. According to reliable data, preanalytical errors still account for nearly 60%–70% of all problems occurring in laboratory diagnostics, most of them attributable to mishandling procedures during collection, handling, preparing or storing the specimens. Although most of these would be “intercepted” before inappropriate reactions are taken, in nearly one fifth of the cases they can produce inappropriate investigations and unjustifiable increase in costs, while generating inappropriate clinical decisions and causing some unfortunate circumstances. Several steps have already been undertaken to increase awareness and establish a governance of this frequently overlooked aspect of the total testing process. Standardization and monitoring preanalytical variables is of foremost importance and is associated with the most efficient and well-organized laboratories, resulting in reduced operational costs and increased revenues. As such, this article is aimed at providing readers with significant updates on the total quality management of the preanalytical phase to endeavour further improvement for patient safety throughout this phase of the total testing process.