Abstract
Sigma metrics have become a useful tool for all parts of the quality control (QC) design process. Through the allowable total error model of laboratory testing, analytical assay performance can be judged on the Six Sigma scale. This not only allows benchmarking the performance of methods and instruments on a universal scale, it allows laboratories to easily visualize performance, optimize the QC rules and numbers of control measurements they implement, and now even schedule the frequency of running those controls.
Highlights
We have emerged from a period of debate over the fundamental approach to analytical quality in laboratory testing
When the 2014 Strategic Conference on Quality Specifications convened in Milan, one of the provocative assignments was to determine whether total error should be improved, or even if it should continue to exist [1]
The report notes the weaknesses of the current total error (TE) approach, as well as shortcomings of the biological variation database and the imperfect calculation of allowable total error based on biological variation data
Summary
We have emerged from a period of debate over the fundamental approach to analytical quality in laboratory testing. To understand the analytical method performance on the Sigma metrics scale, we do not have an easy comparative value against which we judge the test result. If we can squeeze six standard deviations of our analytical method distribution within that TEa, in the absence of any bias, we will achieve that “Six Sigma” goal and expect to generate fewer than four clinical defective results.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
