Related Experiment Video
Updated: Oct 20, 2025

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
Published on: March 22, 2022
Impact of COVID-19 on the pre and post analytical clinical laboratory testing processes- A performance evaluation
Sibtain Ahmed1, Farhat Jahan2, Muhammad Umer Naeem Effendi1
1Department of Pathology and Laboratory Medicine, Aga Khan University, Stadium Road, P.O. Box 3500, Karachi, 74800, Pakistan.
Background:
The aim of this study was to determine the impact of COVID-19 pandemic on the total testing process using six sigma metrics based on a comparison of pre and during pandemic periods.
Material & Methods:
The study duration was over 12 months, 6 months before and 6 months after the COVID-19 onset in Pakistan in March 2020 after the recognition of the first case, using quality indicators (QIs). QIs were chosen from a model of QIs recommended by the CAP. Analysis was performed using Six Sigma calculators and QIs were expressed in percentage (%) and Sigma value were computed. Three levels of performance quality i.e. 25th, 50th, and 75th percentile were derived, being best, common and worst performance respectively. Between-group differences were tested using the Mann-Whitney's U test.
Results:
The median defect percentages of these QIs ranged from 0% to 0.27% for the pre-pandemic period and 0% to 0.13% for the during pandemic period. Meanwhile, sigma values of the majority of the QIs were all above 4.0σ during the pre and the pandemic times. For the pre-analytical phase, sigma scores declined for 1 QI, improved for 3 QIs and remained same for 2. In the post analytical phase, no change in sigma metrics was noted for critical values notification. Considerable increase in defect percentage of inappropriate turnaround times was noted.
Conclusion:
The emergency preparedness proved to be fruitful as depicted by exceptional performance on the sigma metrics for most Qis both prior to and during the pandemic. The pre-analytical and the post analytical phases, being the most error sensitive requires strict vigilance.
Related Concept Videos
Introduction to Statistical Process Control
Quality Control
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Contaminants and Errors
Another key consideration is determining the appropriate number of samples required to...
Steps in Outbreak Investigation
Data Validation
Key parameters for method validation include:
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...

