Managing quality vs. measuring uncertainty in the medical laboratory

Abstract

ISO 15189's particular requirements for quality management in medical laboratories provide guidance for (a) relating performance specifications to the intended use of a test or examination procedure, (b) designing internal quality control (IQC) procedures to verify the attainment of the intended quality of test results, as well as (c) determining the uncertainty or results, where relevant and possible. This guidance has particular implications for analytical quality management, specifically for validating method performance relative to quality goals or requirements (intended use), designing statistical quality control procedures on the basis of the quality required for a test and the precision and bias observed for a method, and characterizing the quality achieved in practice by calculating measurement uncertainty. There already exists an error framework that provides practical tools and guidance for managing analytical quality, along with an existing concept of total error that can be used to characterize the quality of laboratory tests, thus there is considerable concern and debate on the merits and usefulness of measurement uncertainty. This paper argues that total error provides a practical top-down estimate of measurement uncertainty in the laboratory, and that the ISO/GUM model should be primarily directed to and applied by manufacturers.