Sigma Metrics: A Valuable Tool for Evaluating the Performance of Internal Quality Control in Laboratory

Abstract Background Six Sigma is a widely accepted quality management system that provides an objective assessment of analytical methods and instrumentation. Six Sigma scale typically runs from 0 to 6, with sigma value above 6 being considered adequate and 3 sigma being considered the minimal acceptable performance for a process. Methodology Sigma metrics of 10 biochemistry parameters, namely glucose, triglycerides, high-density lipoprotein (HDL), albumin, direct bilirubin, alanine transaminase, aspartate transaminase, urea nitrogen, creatinine and uric acid, and hematology parameters such as hemoglobin (Hb), total leucocyte count (TLC), packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and platelet were calculated by analyzing internal quality control (IQC) data of 3 months (June–August 2019). Results Sigma value was found to be > 6 for triglyceride, HDL, Hb, TLC, and MCH, signifying excellent results and no further modification with respect to IQC. Sigma value was between 3 and 6 for glucose, albumin, creatinine, uric acid, PCV, and MCHC, implying the requirement of improvement in quality control (QC) processes. Sigma value of < 3 was seen in AST, ALT, direct bilirubin, urea nitrogen, platelet, and MCV, signifying suboptimal performance. Discussion Six Sigma provides a more quantitative framework for evaluating process performance with evidence for process improvement and describes how many sigmas fit within the tolerance limits.Thus, for parameters with sigma value < 3, duplicate testing of the sample along with three QCs three times a day may be used along with stringent Westgard rules for rejecting a run. Conclusion Sigma metrics help assess analytical methodologies and augment laboratory performance.

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Implementation of quality management system in health care of the Lipetsk region by optimizing internal quality control and safety of medical activities. Results and future prospects

Vestnik Roszdravnadzora ◽

10.35576/2070-7940-2019-2019-6-60-65 ◽

2019 ◽

Vol 2019 (6) ◽

pp. 60-65

Author(s):

Юрий Шуршуков ◽

Yuriy Shurshukov ◽

Игорь Иванов ◽

Igor' Ivanov ◽

Любовь Агафонова ◽

...

Keyword(s):

Health Care ◽

Quality Control ◽

Quality Management ◽

Management System ◽

Quality Management System ◽

Basic Element ◽

Internal Quality Control ◽

Healthcare Sector ◽

Internal Quality ◽

Future Prospects

The article describes the experience of implementing a quality management system in healthcare of the Lipetsk region using the example of the “Lipetsk Regional Clinical Hospital”. The approaches to the organization of internal quality control and safety of medical activities as a basic element of a quality management system are shown. There are presented primary results of the audits. The prospects for the further implementation of the quality management system in the healthcare sector are identified.

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The Evaluation of Analytical Performance of Immunoassay Tests by Using Six-Sigma Method

Journal of Medical Biochemistry ◽

10.1515/jomb-2017-0026 ◽

2017 ◽

Vol 36 (4) ◽

pp. 301-308 ◽

Cited By ~ 5

Author(s):

Rukiye Nar ◽

Dilek Iren Emekli

Keyword(s):

Quality Control ◽

Six Sigma ◽

Clinical Chemistry ◽

Quality Measurement ◽

Analytical Performance ◽

Internal Quality Control ◽

Internal Quality ◽

Variation Database ◽

The Mean ◽

Six Sigma Methodology

SummaryBackground: The Six-Sigma Methodology is a quality measurement method in order to evaluate the performance of the laboratory. In the present study, it is aimed to evaluate the analytical performance of our laboratory by using the internal quality control data of immunoassay tests and by calculating process sigma values. Methods: Biological variation database (BVD) are used for Total Allowable Error (TEa). Sigma values were determined from coefficient of variation (CV) and bias resulting from Internal Quality Control (IQC) results for 3 subsequent months. If the sigma values are ≥6, between 3 and 6, and <3, they are classified as »world-class«, »good« or »un - acceptable«, respectively. Results: A sigma value >6 was found for TPSA and TSH for the both levels of IQC for 3 months. When the sigma values were analyzed by calculating the mean of 3 months, folate, LH, PRL, TPSA, TSH and vitamin B12 were found >6. The mean sigma values of CA125, CA15-3, CA19-9, CEA, cortisol, ferritin, FSH, FT3, PTH and testosteron were >3 for 3-months. However, AFP, CA125 and FT4 produced sigma values <3 for varied months. Conclusion: When the analytical performance was evaluated according to Six-Sigma levels, it was generally found as good. It is possible to determine the test with high error probability by evaluating the fine sigma levels and the tests that must be quarded by a stringent quality control regime. In clinical chemistry laboratories, an appropriate quality control scheduling should be done for each test by using Six-Sigma Methodology.

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Quality improvement in clinical biochemistry laboratory using six sigma metrics and quality goal index

10.17727/jmsr.2021/9-16 ◽

2021 ◽

Vol 9 (2) ◽

pp. 101-107

Author(s):

Maheshwari A ◽

Sadariya B ◽

Javia HN ◽

Sharma D

Keyword(s):

Quality Control ◽

Quality Assurance ◽

Six Sigma ◽

Quality Management System ◽

Clinical Biochemistry ◽

Process Efficiency ◽

Internal Quality ◽

Level Ii ◽

Level I ◽

Clinical Biochemistry Laboratory

Introduction: One of the most popular quality management system tackle employed for process perfection is six sigma. When the process outcome is measurable, six sigma can be used to assess the quality. Aim: Present study was conducted with the objective to apply six sigma matrices and quality goal index for the assessment of quality assurance in a clinical biochemistry laboratory. Materials and methods: Present study is a retrospective study. Internal and external quality control data were analyzed retrospectively during July 2020 to December 2020. Descriptive statistics like laboratory mean ± standard deviation; bias and coefficient of variation (CV) were calculated for the parameters glucose, urea, creatinine, ALT (SGPT), AST (SGOT), cholesterol, triglyceride and HDL. Sigma value was calculated for both level I & level II of internal quality control (IQC). Results: Satisfactory sigma values (≥3) were elicited for blood glucose, cholesterol, triglyceride, HDL, urea and creatinine, while ALT (SGPT) and AST (SGOT) performed poorly (<3) on the sigma scale. The quality goal index (QGI) ratio was found (> 1.2) for only 2 parameters SGPT and SGOT (with sigma value <3) for both levels 1 and 2, indicating inaccuracy. Conclusion: Results of present study focuses on meticulous appraisal and execution of quality measures to improve sigma standards of all the analytical processes. Even though six sigma provides benefits over former approaches to quality assurance, it also opens newer challenges for laboratory practitioners. Therefore, sigma metric analysis provides a point of reference to design a protocol for IQC for the laboratory; address poor assess performance, and assess the existing laboratory process efficiency.

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