Measurement uncertainty in laboratory medicine: a comprehensive review of frameworks, analytical performance...

Measurement uncertainty is a fundamental component of laboratory quality assurance and a key requirement of International Organization for Standardization (ISO) 15189. Despite its recognized importance, routine implementation of measurement uncertainty in clinical laboratories remains inconsistent due to methodologic complexity and lack of harmonized approaches. We sought to summarize current methodologic frameworks, analytical performance specifications (APS), and practical strategies for estimation and implementation of measurement uncertainty in medical laboratories.

A structured literature review was conducted covering the period 2015 to 2025 using databases such as PubMed and Scopus, along with resources from the European Federation of Clinical Chemistry and Laboratory Medicine. The review focused on international standards (ISO 15189, Clinical and Laboratory Standards Institute), biological variation-based APS (including the Milan Consensus), patient-based real-time quality control (QC), and emerging artificial intelligence and machine learning applications.

The findings demonstrate a convergence between traditional total allowable error concepts and contemporary measurement uncertainty frameworks, with biological variation serving as a key foundation for APS. Both bottom-up approaches based on uncertainty budgeting and top-down approaches using routine internal QC and external quality assessment data are widely applied. Six Sigma metrics provide a robust tool for prioritizing analytical performance improvement. Integration of patient-based real-time QC with artificial intelligence/machine learning techniques, including anomaly detection and deep learning, enhances the detection of analytical drift and sporadic errors beyond conventional QC systems.

Measurement uncertainty should be integrated within laboratory quality management systems rather than treated as an isolated requirement. Future directions include harmonization of measurement uncertainty estimation practices, adaptation to emerging analytical technologies, and improving clinician understanding and interpretation of uncertainty to enhance clinical decision-making.