Useful measures and models for analytical quality management in medical laboratories

Abstract

AbstractThe 2014 Milan Conference “Defining analytical performance goals 15 years after the Stockholm Conference” initiated a new discussion of issues concerning goals for precision, trueness or bias, total analytical error (TAE), and measurement uncertainty (MU). Goal-setting models are critical for analytical quality management, along with error models, quality-assessment models, quality-planning models, as well as comprehensive models for quality management systems. There are also critical underlying issues, such as an emphasis on MU to the possible exclusion of TAE and a corresponding preference for separate precision and bias goals instead of a combined total error goal. This opinion recommends careful consideration of the differences in the concepts of accuracy and traceability and the appropriateness of different measures, particularly TAE as a measure of accuracy and MU as a measure of traceability. TAE is essential to manage quality within a medical laboratory and MU and trueness are essential to achieve comparability of results across laboratories. With this perspective, laboratory scientists can better understand the many measures and models needed for analytical quality management and assess their usefulness for practical applications in medical laboratories.