A cross-sectional study on the evaluation of laboratory analytical quality performance in a tertiary medical college in Eastern India
Background: Six Sigma is a powerful management tool that can be used in the laboratory to assess the quality of performance in the analytical phase. Aims and Objectives: This study aims to calculate the sigma metrics of 17 biochemistry test analytes over a period of 1 year from January to Decembe...
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| Format: | Article |
| Language: | English |
| Published: |
Manipal College of Medical Sciences, Pokhara
2025-07-01
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| Series: | Asian Journal of Medical Sciences |
| Subjects: | |
| Online Access: | https://ajmsjournal.info/index.php/AJMS/article/view/4564 |
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| Summary: | Background: Six Sigma is a powerful management tool that can be used in the laboratory to assess the quality of performance in the analytical phase.
Aims and Objectives: This study aims to calculate the sigma metrics of 17 biochemistry test analytes over a period of 1 year from January to December 2024. The quality goal index (QGI) is measured for analytes with Sigma metric value <3. The total analytical error observed is compared with the established total allowable error (TEa) guidelines. The root cause analysis, followed by corrective action, will be taken for parameters with poor performance. An appropriate quality control (QC) strategy will be planned for these routine biochemistry tests for quality improvement.
Materials and Methods: The daily internal quality control (IQC) and the monthly External quality assessment scheme samples were run for biochemistry analytes. The mean, standard deviation, coefficient of variation, Bias%, Total error observed (TEobs), and Sigma metric were calculated for each IQC level of each analyte. The QGI was measured for analytes with a Sigma value of <3.
Results: The Sigma value was highest for direct bilirubin and the lowest for phosphorus. The QGI for analytes with Sigma <3 revealed the underlying cause as inaccuracy. The TEobs was higher than the TEa for albumin, creatinine, phosphorus, and total protein. The root cause analysis demonstrated various reasons for the observed inaccuracy. A QC strategy was designed for the laboratory, based on the guidelines proposed by Westgard and Cooper.
Conclusion: This study has demonstrated that Sigma metrics can be used to assess laboratory quality, analyze the cause of low performance, and also design a QC strategy for optimum and better laboratory performance. |
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| ISSN: | 2467-9100 2091-0576 |