Improving quality control for in‐clinic hematology analyzers: Common myths and opportunities

A robust quality management system for automated hematology an alyzers is crucial for generating high- quality results and instilling con fidence in analyzer function. A comprehensive quality management system comprises multiple aspects. Two major aspects are quality assurance (QA) and quality control (QC). QA is a framework and strategy for ensuring quality across the entire process, from blood draw through reported results. QA thus encompasses preanalytical to postanalytical variables related to result generation. QC, on the other hand, is focused exclusively on the analytical portion of quality management to ensure the function and performance of the analyzer itself. QC facilitates identification of analyzer problems so they can be addressed and fixed in a timely fashion. Recommendations for QC, including QC materials (QCMs), frequency of QC, and interpre tation of QC results, are specific for the analyzer. The ideal QCM would (1) be able to assess the function and per formance of all aspects of the hematology analyzer, (2) have a long shelf life, (3) be easy for the user to run and interpret, and (4) instill confidence in results for all species tested. To understand these re -quirements, it is imperative to understand the workflow of an au tomated hematology analyzer. Although the specifics vary among analyzers, the workflow follows the same general pattern (Table 1). The recommended QCM and the algorithms to interpret the QC results vary among analyzers to differences in chemistry and detection methods. In to basic analyzer function, QC for species- specific differ ences in performance, bias