The application of sigma metrics in the laboratory to assess quality control processes in South Africa

Analysis of hematology quality control using six sigma metrics

INTRODUCTION

Clinical laboratories serve a critical role in increasing the efficiency of patient care. Choosing the right test, getting trustworthy results and appropriate interpretation are of utmost importance in improving the patient's well-being. Quality management strategies should be applied in routine patient care because laboratory errors have a major impact on the quality of patient care. In sigma metrics, errors identified are quantified as percentage errors or defects per million (DPM). It aims at improving the quality control (QC) process by forming an appropriate strategy.

AIM AND OBJECTIVES

To analyze the internal quality control (IQC) of hematology analytes using the sigma metrics method and to devise the frequency of IQC by the results of six sigma metric analysis.

MATERIALS AND METHODS

This study was conducted in a tertiary care center of western India. Internal quality control (IQC) data sets of five analytes- Red Blood Cell count (RBC), Hemoglobin (Hb), Hematocrit (Hct), White blood cell count (WBC), and Platelet count (PLT) were analyzed retrospectively of six months using Beckman Coulter DXH 800 hematology analyzers.

RESULTS

The observed sigma value was >6 for Hb, TLC, and PLT, indicating excellent results and requiring no modification in IQC. The Sigma value was between 3 and 4 for RBC and Hct suggested the need for improvement in quality control (QC) processes. No analytes showed a Sigma value of <3.

CONCLUSION

Sigma metrics provide a quantitative framework that helps to assess analytic methodologies and can serve as an important self-assessment tool for quality assurance in the clinical laboratory.

The Evaluation of the Quality Performance of Biochemical Analytes in Clinical Biochemistry Laboratory Using Six Sigma Matrices

Introduction This study was conducted to assess the analytical performance of biochemical tests using Six Sigma methodology and to assess the underlying causes of unsatisfied performance of analytes with a sigma value of less than 4 using quality goal index (QGI) and root cause analysis (RCA). Methodology Daily data for internal quality control (IQC) for both level 1 (L1) and level 2 (L2) and monthly data for external quality assessment for a period of six months were recorded. The coefficient of variation (CV), bias, and total allowable error (TEa) were calculated to analyze the sigma (σ) values for 19 biochemical analytes. Quality goal index (QGI) analysis was done to analyze impressions and inaccuracies in analyte performance having a sigma value of less than 4. Root cause analysis (RCA) was done to evaluate the possible causes that can improve quality performance. Results Creatinine and high-density lipoprotein (HDL) had sigma metrics of ≤2.0, and chloride, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) had sigma values between 2 and 3. Glucose, total protein (TP), phosphate (Phos), and potassium had sigma values between 4 and 5 in level 1 QC. Sigma grading for level 2 quality control (QC) also gave similar results. For analytes with σ < 4, QGI analysis exposed inaccuracy or imprecision issues and identified errors such as the reconstitution of IQC, storage temperature, and air bubbles while processing the QC, being common causes of poor performance. Conclusion Six Sigma approach is helpful for quality assurance and identifying areas for improvement. Assessing Six Sigma metrics should be a routine practice to decide the frequency of QC run and to detect errors in analysis.