Publications

Introduction: Internal and external quality controls (QCs) used in the laboratory are effective in detecting analytical errors. However, they cannot quantify the number of errors. Six sigma can be used to objectively evaluate the performance of analytical methods. Hence, we have evaluated the analytical performance of 19 parameters using six sigma methodology. Materials and methods: Quality control data were collected over a period of 6 months—from January to June 2016—and sigma metric was calculated. Parameters showing sigma metrics of ≤3 were further analyzed between July and September 2016 by applying the suggested rules\ from Unity Real Time (URT) software.
Results: Gamma-glutamyl transferase (GGT) Level (L) 2 showed the highest value of sigma (13.22). Total bilirubin was found to have the highest sigma values at both control levels (7.15 and 9.49 at L1 and L2, respectively). Sigma value of ≥4 was observed across all control levels for anti-TPO, CK-MB, potassium, PSA, and TSH. L1 of alpha feto protein (AFP) and L2 of Troponin I had sigma value of ≤3. We have obtained sigma value of ≤3 for all levels of remaining analytes. Among these, L1 of AFP showed a significant improvement in sigma after the application of suggested rules (2.5 to 9.3).Conclusion: The sigma value for a test is a good indication of its process capability because it considers both bias and imprecision. Unfortunately, most clinical laboratory tests are below six sigma processes. It is imperative to implement appropriate QC strategies for the judicious use of quality control. Keywords: Quality control, Root cause analysis, Six sigma, Total error, Westgard rules. Indian Journal of Medical Biochemistry (2020): 10.5005/jp-journals-10054-0131