Redefining quality targets: a first-time application of an innovative graphic tool in hematology using six sigma

Hematology laboratories routinely face analytical challenges despite automation and standardized workflows. While Six Sigma metrics are increasingly applied in clinical chemistry, their use in hematology remains limited due to variability in Total Allowable Error (TEa) standards and lack of integrated assessment tools. This study aims to evaluate analytical performance in hematology by combining sigma metrics with an innovative graphic decision tool to guide quality control (QC) planning. A retrospective study over was conducted in a tertiary hematology lab. Internal Quality Control (IQC) and External Quality Assurance Scheme (EQAS) data for five analytes-hemoglobin, WBC, RBC, hematocrit, and platelet count-were analyzed using the Six Sigma model. TEa values were selected using a hierarchical approach based on the 2014 Milan Consensus, prioritizing biological variation, CLIA, and RCPA guidelines. A novel graphic tool was used to visualize performance zones and inform QC strategies. Sigma metrics varied across parameters and TEa sources. Hemoglobin demonstrated excellent performance (σ > 6), while hematocrit and platelet count showed sigma <3 under strict TEa. Graphic tool stratification revealed actionable insights; application of TEa optimization reclassified low-performing tests, significantly improving QC efficiency. Subsequent QGI calculations identified the predominant source of error. This study introduces a first-time application of a graphic tool in hematology for sigma visualization and QC planning. The dual-framework approach enhances diagnostic accuracy and resource utilization, offering a practical, scalable model for laboratories seeking personalized, risk-based quality management.