Driving Analytical Excellence in Biochemistry: A Four-Month Six Sigma Performance Assessment

Background: Ensuring the accuracy of biochemical tests in clinical labs is crucial for diagnosis. Six Sigma evaluates test result deviation from targets; higher values indicate fewer errors. Some tests perform poorly with sigma values below 4. This study uses Six Sigma, the Quality Goal Index (QGI), and Root Cause Analysis (RCA) to identify and improve performance issues.Methods: Data were collected daily for IQC at two levels and monthly for external assessments over four months. CV, bias, and Total Allowable Error were used to calculate sigma values for 16 biochemical analytes. QGI analysis identified discrepancies, and RCA uncovered reasons for poor results.Results: Urea, protein, phosphate, high-density lipoprotein, alanine aminotransferase, and iron had sigma values ≤4.0. Glucose, creatinine, albumin, uric acid, and triglyceride in Level 1, and albumin, triglyceride, and alanine aminotransferase in Level 2, showed sigma values of 4–5. Level 2 glucose, creatinine, uric acid, and cholesterol were in the 5–6 sigma range. Ten analytes across Levels 1 and 2 had sigma values ≥6. For σ < 4 analytes, QGI showed inaccuracy. IQC reconstitution, storage temperature, and air bubbles affected performance.Conclusion: Six Sigma methodology improves laboratory testing quality. Regular monitoring and addressing root causes lead to more accurate results and better patient care.