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Çevlik T, Haklar G. Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data. J Med Biochem 2024; 43:43-49. [PMID: 38496028 PMCID: PMC10943459 DOI: 10.5937/jomb0-43052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/16/2023] [Indexed: 03/19/2024] Open
Abstract
Background Six Sigma is a popular quality management system that enables continuous monitoring and improvement of analytical performance in the clinical laboratory. We aimed to calculate sigma metrics and quality goal index (QGI) for 17 biochemical analytes and compare the use of bias from internal quality control (IQC) and external quality assurance (EQA) data in the calculation of sigma metrics. Methods This retrospective study was conducted in Marmara University Pendik E&R Hospital Biochemistry Laboratory. Sigma metrics calculation was performed as (TEa-bias)/CV). CV was calculated from IQC data from June 2018 - February 2019. EQA bias was calculated as the mean of % deviation from the peer group means in the last seven surveys, and IQC bias was calculated as (laboratory control result mean-manufacturer control mean)/ manufacturer control mean) x100. In parameters where sigma metrics were <5; QGI=bias/1.5 CV) score of <0.8 indicated imprecision, >1.2 pointed inaccuracy, and 0.8-1.2 showed both imprecision and inaccuracy. Results Creatine kinase (both levels), iron and magnesium (pathologic levels) showed an ideal performance with ≥6 sigma level for both bias determinations. Eight of the 17 parameters had different sigma levels when we compared sigma values calculated from EQA and IQC derived bias% while the rest were grouped at the same levels. Conclusions Sigma metrics is a good quality tool to assess a laboratory's analytical performance and facilitate the comparison of the assay performances in the same manner across multiple systems. However, we might need to design a tight internal quality control protocol for analytes showing poor assay performance.
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Affiliation(s)
- Tülay Çevlik
- Marmara University Pendik E&R Hospital, Biochemistry Laboratory, Istanbul, Turkey
| | - Goncagül Haklar
- Marmara University Pendik E&R Hospital, Biochemistry Laboratory, Istanbul, Turkey
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Panda CR, Kumari S, Mangaraj M, Nayak S. The Evaluation of the Quality Performance of Biochemical Analytes in Clinical Biochemistry Laboratory Using Six Sigma Matrices. Cureus 2023; 15:e51386. [PMID: 38292960 PMCID: PMC10826247 DOI: 10.7759/cureus.51386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/31/2023] [Indexed: 02/01/2024] Open
Abstract
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.
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Affiliation(s)
- Chhabi R Panda
- Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, IND
| | - Suchitra Kumari
- Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, IND
| | | | - Saurav Nayak
- Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, IND
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Gadde R, HM V. Analysis of biochemical analytes using six sigma metrics with two analyzers at an Indian lab setting. Bioinformation 2023; 19:1043-1050. [PMID: 38046510 PMCID: PMC10692979 DOI: 10.6026/973206300191043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/05/2023] Open
Abstract
A zero defects goal was implemented in the clinical laboratory settings using a six-sigma model. Daily Internal Quality Control (IQC) and external quality control data from April-September 2023 was extracted to calculate the sigma metrics of 21 biochemical analytes based on Total Error Allowable (TEa), % bias and co-efficient of variation percent (CV%). A retrospective comparative study was conducted in the department of Clinical Biochemistry at Kanva Diagnostic Services Pvt. Ltd, Bengaluru, India. The analytical performance of the 21 biochemical analytes was tested on Cobas 6000 and C311 analyzers. Quality Goal Index (QGI) and root cause analysis was calculated to infer the reason for the deviation of six sigma. Method decision charts were plotted to show the comparison of the problem analytes on both the analyzers. On Cobas 6000 at level 1 IQC, out of 21 analytes, 10 analytes showed σ>6 and 10 analytes showed σ 3-6 and on C311, 15 analytes which showed σ>6 and 6 analytes that showed σ 3-6. On Cobas 6000 at level 2 IQC, out of 21 analytes, 12 analytes showed σ>6 and 8 analytes showed σ 3-6 and on C311 17 analytes showed σ>6 and 4 analytes showed σ 3-6. Creatinine failed to meet minimal sigma performance at both levels of IQC on Cobas 6000.
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Affiliation(s)
- Ranjeeta Gadde
- Kanva Diagnostic Services Private Ltd, #744, 11th Block, 2nd Stage, Marilingappa Extension, Nagarbhavi, Bengaluru - 560072, Karnataka, India
| | - Venkatappa HM
- Kanva Diagnostic Services Private Ltd, #744, 11th Block, 2nd Stage, Marilingappa Extension, Nagarbhavi, Bengaluru - 560072, Karnataka, India
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Liu Q, Bian G, Chen X, Han J, Chen Y, Wang M, Yang F. Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk-based statistical quality control strategy for these assays: A multicenter study. J Clin Lab Anal 2021; 35:e24059. [PMID: 34652033 PMCID: PMC8605169 DOI: 10.1002/jcla.24059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/15/2021] [Accepted: 10/04/2021] [Indexed: 11/13/2022] Open
Abstract
Background The six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk‐based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed. Methods Internal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk‐based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI). Results Sigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk‐based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation. Conclusions In multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk‐based SQC strategy development and improvement measure implementation.
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Affiliation(s)
- Qian Liu
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, China
| | - Guangrong Bian
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, China
| | - Xinkuan Chen
- Department of Laboratory Medicine, Xuzhou Medical University Affiliated Hospital of Lianyungang, Lianyungang, China
| | - Jingjing Han
- Department of Laboratory Medicine, Wuxi Branch of Ruijin Hospital, Wuxi, China
| | - Ying Chen
- Department of Laboratory Medicine, Nantong Hospital of Traditional Chinese Medicine, Nantong, China
| | - Menglin Wang
- Department of Laboratory Medicine, Suqian First Hospital, Suqian, China
| | - Fumeng Yang
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, China
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Luo Y, Yan X, Xiao Q, Long Y, Pu J, Li Q, Cai Y, Chen Y, Zhang H, Chen C, Ou S. Application of Sigma metrics in the quality control strategies of immunology and protein analytes. J Clin Lab Anal 2021; 35:e24041. [PMID: 34606652 PMCID: PMC8605144 DOI: 10.1002/jcla.24041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 12/19/2022] Open
Abstract
Background Six Sigma (6σ) is an efficient laboratory management method. We aimed to analyze the performance of immunology and protein analytes in terms of Six Sigma. Methods Assays were evaluated for these 10 immunology and protein analytes: Immunoglobulin G (IgG), Immunoglobulin A (IgA), Immunoglobulin M (IgM), Complement 3 (C3), Complement 4 (C4), Prealbumin (PA), Rheumatoid factor (RF), Anti streptolysin O (ASO), C‐reactive protein (CRP), and Cystatin C (Cys C). The Sigma values were evaluated based on bias, four different allowable total error (TEa) and coefficient of variation (CV) at QC materials levels 1 and 2 in 2020. Sigma Method Decision Charts were established. Improvement measures of analytes with poor performance were recommended according to the quality goal index (QGI), and appropriate quality control rules were given according to the Sigma values. Results While using the TEaNCCL, 90% analytes had a world‐class performance with σ>6, Cys C showed marginal performance with σ<4. While using minimum, desirable, and optimal biological variation of TEa, only three (IgG, IgM, and CRP), one (CRP), and one (CRP) analytes reached 6σ level, respectively. Based on σNCCL that is calculated from TEaNCCL, Sigma Method Decision Charts were constructed. For Cys C, five multi‐rules (13s/22s/R4s/41s/6X, N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (13s, N = 2, R = 1, Batch length: 1000). Cys C need to improve precision (QGI = 0.12). Conclusions The laboratories should choose appropriate TEa goals and make judicious use of Sigma metrics as a quality improvement tool.
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Affiliation(s)
- Yanfen Luo
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Xingxing Yan
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Qian Xiao
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yifei Long
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Jieying Pu
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Qiwei Li
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yimei Cai
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yushun Chen
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Hongyuan Zhang
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Cha Chen
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Songbang Ou
- Reproductive center, Department of Obstetrics and Gynecology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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Liu Q, Zhu W, Bian G, Liang W, Zhao C, Yang F. Application of the sigma metrics to evaluate the analytical performance of cystatin C and design a quality control strategy. Ann Clin Biochem 2021; 58:203-210. [PMID: 33393354 DOI: 10.1177/0004563220988032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Sigma metrics are commonly used to evaluate laboratory management. In this study, we aimed to evaluate the analytical performance of cystatin C using sigma metrics and to develop an individualized quality control scheme for cystatin C concentrations. METHODS Bias was calculated based on the samples used for the external quality assessment. The coefficient of variation was calculated using six months of internal quality control measurements at two levels, and desirable specification derived from biological variation was used as the quality goal. The sigma value for cystatin C was calculated using the above data. The internal quality control scheme and improvement measures were formulated according to the Westgard sigma standards for batch size and quality goal index. RESULTS The sigma values for cystatin C, for quality control levels 1 and 2, were 3.04 and 4.95, respectively. The 13s/22s/R4s/41s/6x multirules (n = 6, R = 1), with a batch size of 45 patient samples, were selected as the internal quality control schemes for cystatin C. With different concentrations of cystatin C, the power function graph showed a probability for error detection of 94% and 100% and a probability for false rejection of 4% and 2%, respectively. According to the quality goal index of cystatin C, its precision needs to be improved. CONCLUSIONS With a 'desirable' biological variation of 6.50%, the Westgard rule 13s/22s/R4s/41s/6x (n = 6, R = 1, batch size of 45) with high efficacy for determining the detection error is recommended for individualized quality control schemes of cystatin C.
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Affiliation(s)
- Qian Liu
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Wenjun Zhu
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Guangrong Bian
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Wei Liang
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Changxin Zhao
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Fumeng Yang
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
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Liu Y, Cao Y, Liu X, Wu L, Cai W. Evaluation of the analytical performance of endocrine analytes using sigma metrics. J Clin Lab Anal 2020; 35:e23581. [PMID: 32951270 PMCID: PMC7843286 DOI: 10.1002/jcla.23581] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/24/2020] [Accepted: 08/29/2020] [Indexed: 11/11/2022] Open
Abstract
Background (a) To evaluate the clinical performance of endocrine analytes using the sigma metrics (σ) model. (b) To redesign quality control strategies for performance improvement. Methods The sigma values of the analytes were initially evaluated based on the allowable total error (TEa), bias, and coefficient of variation (CV) at QC materials level 1 and 2 in March 2018. And then, the normalized QC performance decision charts, personalized QC rules, quality goal index (QGI) analysis, and root causes analysis (RCA) were performed based on the sigma values of the analytes. Finally, the sigma values were re‐evaluated in September 2018 after a series of targeted corrective actions. Results Based on the initial sigma values, two analytes (FT3 and TSH) with σ > 6, only needed one QC rule (13S) with N2 and R500 for QC management. On the other hand, seven analytes (FT4, TT4, CROT, E2, PRL, TESTO, and INS) with σ < 4 at one QC material level or both needed multiple rules (13S/22S/R4S/41S/10X) with N6 and R10‐500 depending on different sigma values for QC management. Subsequently, detailed and comprehensive RCA and timely corrective actions were performed on all the analytes base on the QGI analysis. Compared with the initial sigma values, the re‐evaluated sigma metrics of all the analytes increased significantly. Conclusions It was demonstrated that the combination of sigma metrics, QGI analysis, and RCA provided a useful evaluation system for the analytical performance of endocrine analytes.
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Affiliation(s)
- Yanming Liu
- Department of Laboratory Medicine, YueBei People's Hospital, Shaoguan, China.,Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Institute of Aging Research, Guangdong Medical University, Dongguan, China
| | - Yue Cao
- Department of Medical Technology, Medical College of Shaoguan University, Shaoguan, China
| | - Xijun Liu
- Department of Laboratory Medicine, YueBei People's Hospital, Shaoguan, China
| | - Liangyin Wu
- Department of Laboratory Medicine, YueBei People's Hospital, Shaoguan, China
| | - Wencan Cai
- Department of Laboratory Medicine, YueBei People's Hospital, Shaoguan, China
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Abstract
OBJECTIVE: Six Sigma is one of the most popular quality management system tools employed for process improvement. The Six Sigma methods are usually applied when the outcome of the process can be measured. This study was done to assess the performance of individual biochemical parameters on a Sigma Scale by calculating the sigma metrics for individual parameters and to follow the Westgard guidelines for appropriate Westgard rules and levels of internal quality control (IQC) that needs to be processed to improve target analyte performance based on the sigma metrics. MATERIALS AND METHODS: This is a retrospective study, and data required for the study were extracted between July 2015 and June 2016 from a Secondary Care Government Hospital, Chennai. The data obtained for the study are IQC - coefficient of variation percentage and External Quality Assurance Scheme (EQAS) - Bias% for 16 biochemical parameters. RESULTS: For the level 1 IQC, four analytes (alkaline phosphatase, magnesium, triglyceride, and high-density lipoprotein-cholesterol) showed an ideal performance of ≥6 sigma level, five analytes (urea, total bilirubin, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level and for level 2 IQCs, same four analytes of level 1 showed a performance of ≥6 sigma level, and four analytes (urea, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level. For all analytes <6 sigma level, the quality goal index (QGI) was <0.8 indicating the area requiring improvement to be imprecision except cholesterol whose QGI >1.2 indicated inaccuracy. CONCLUSION: This study shows that sigma metrics is a good quality tool to assess the analytical performance of a clinical chemistry laboratory. Thus, sigma metric analysis provides a benchmark for the laboratory to design a protocol for IQC, address poor assay performance, and assess the efficiency of existing laboratory processes.
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Affiliation(s)
- B Vinodh Kumar
- Department of Biochemistry, ESIC Medical College Hospital and PGIMSR, Chennai, Tamil Nadu, India
| | - Thuthi Mohan
- Department of Biochemistry, ESIC Medical College Hospital and PGIMSR, Chennai, Tamil Nadu, India
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Zhou B, Wu Y, He H, Li C, Tan L, Cao Y. Practical application of Six Sigma management in analytical biochemistry processes in clinical settings. J Clin Lab Anal 2019; 34:e23126. [PMID: 31774217 PMCID: PMC6977137 DOI: 10.1002/jcla.23126] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 10/31/2019] [Accepted: 11/08/2019] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Six Sigma methodology with a zero-defect goal has long been applied in commercial settings and was utilized in this study to assure/improve the quality of various analytes. METHODS Daily internal quality control (QC) and external quality assessment data were collected and analyzed by calculating the sigma (σ) values for 19 analytes based on the coefficient of variation, bias, and total error allowable. Standardized QC sigma charts were established with these parameters. Quality goal index (QGI) analysis and root cause analysis (RCA) were used to discover potential problems for the analytes. RESULTS Five analytes with σ ≥ 6 achieved world-class performance, and only the Westgard rule (13s ) with one control measurement at two QC material levels (N2) per QC event and a run size of 1000 patient samples between QC events (R1000) was needed for QC. In contrast, more control rules (22s /R4s /41s ) along with high N values and low R values were needed for quality assurance for five analytes with 4 ≤ σ < 6. However, the sigma levels of nine analytes were σ < 4 at one or more QC levels, and a more rigorous QC procedure (13s /22s /R4s /41s /8x with N4 and R45) was implemented. The combination of QGI analysis and RCA further revealed inaccuracy or imprecision problems for these analytes with σ < 4 and discovered five aspects of potential causes considered for quality improvement. CONCLUSIONS Six Sigma methodology is an effective tool for evaluating the performance of biochemical analytes and is conducive to quality assurance and improvement.
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Affiliation(s)
- Bingfei Zhou
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.,Research Office of Clinical Laboratory, Clinical Translational Medicine Research Institute of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Yi Wu
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Hanlin He
- Department of Medical laboratory of Hunan Normal University School of Medicine, Changsha, China
| | - Cunyan Li
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.,Research Office of Clinical Laboratory, Clinical Translational Medicine Research Institute of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Liming Tan
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Youde Cao
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.,Research Office of Clinical Laboratory, Clinical Translational Medicine Research Institute of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
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