Search

Background: Quality control (QC) is an important part of clinical laboratory management which evaluates and maintains the performance of the clinical laboratory. Six Sigma is a continuous quality improvement process which is widely applied in the health care industry mainly in clinical laboratories. Aims and Objectives: The study aimed to evaluate the performance of biochemical analytes in a clinical laboratory using Six Sigma metrics and to implement quality improvement interventions. Sigma value was analyzed using bias, coefficient of variation (CV), and total allowable error (TEa) for each analyte. The quality goal index ratio was calculated to identify specific types of errors to introduce targeted quality interventions. Materials and Methods: This quasi-experimental study was carried out at the Government Institute of Medical Sciences, Greater Noida, Uttar Pradesh. Internal QC (IQC) and external QC data were collected during the study period and analyzed. The CV, Bias, and TEa were used to calculate the sigma (s) values for 20 biochemical analytes. Results: On retrospective data analysis, triglycerides-Level 1 (L1) and Level 2 (L2), and direct bilirubin-L2 showed a world-class performance (above 6s) while 14 parameters were placed below average (<3s) on sigma metrics. Prospective data analysis was performed following quality improvement intervention. Amylase, direct bilirubin, triglycerides and alanine transferase performed at world-class sigma metric for both the control levels (L1 and L2) whereas, Albumin and aspartate transferase showed above six sigma values for L2 control only. Five analytes out of 20 performed below average (<3s) on sigma metrics, for which further continuous intervention will be carried out. Conclusion: Six Sigma metrics is a continuous improvement and monitoring program that should be implemented to achieve world-class quality performance. [ABSTRACT FROM AUTHOR]

Background: The application of the Six Sigma (σ) metric in biochemical laboratories is a powerful tool for reducing the occurrence of errors and prioritizing important improvements in laboratory quality control. The National Accreditation Board for Testing and Calibration Laboratories (NABL) is an accreditation body with an accreditation system established in accordance with ISO/IEC 17011, providing specialty- or scope-based certification based on the conformance of quality indices to medical laboratories. In this context, a study has been designed that considers the quality guidelines set by NABL as well as the sigma metric rule in the assessment of analytical performance. Aims and Objectives: The aims of this study were to identify the gaps and need for strategy modification for quality improvement by assessing the performance of two NABL-accredited medical testing laboratories on a Sigma metric scale. Materials and Methods: A retrospective analytical study was conducted over 6 months (January–June 2021). Internal quality control (IQC) and EQAS data were obtained from third-party QC providers (Bio-Rad, India) and analyzed by calculating sigma (σ) values based on the coefficient of variation, bias, and total error allowable in two NABL-accredited medical testing laboratories. To identify potential problems for analytes with poor sigma values, a quality goal index (QGI) analysis was performed. Results: By analyzing the sigma values obtained by both NABL-accredited laboratories, we can see that laboratory 2 performed better than laboratory 1. After calculating the QGI, there was a problem of inaccuracy and imprecision in laboratory 1, and laboratory 2 had QGI values that indicated only imprecision. Conclusion: Diagnostic laboratories should incorporate Six Sigma metrics to identify gaps in their performance to ensure better quality control and patient safety.

Background: Clinical laboratories errors can impose serious adverse effects on patient management. Clinical laboratories can evaluate their analytical performance by effective quality management tool i.e. Six Sigma metrics. Objectives: To evaluate the analytical performance of routine chemistry parameters by Six Sigma approach. Methods: Cross sectional, study carried out from July 2022 till February 2024 in the Chemical Pathology section of University of Lahore Diagnostic Lab and Research Center, Lahore. Analytical performance of nineteen routine chemistry parameters were evaluated by using the external and internal quality control material. Only those parameters were selected who enrolled in External Quality Assurance Scheme. Inclusion criteria for IQC following West gard rule +/- 2 SD while for EQAS results follow +/- 2 Z score. Sample size for 19 parameters analyzed in EQAS from July 2022 till December 2023 was 342. 2700 IQC samples were analyzed from August 2023 till February 2024. All control values were entered in Microsoft Excel 2013 calculated manually by conventional equations. Results: Most of our laboratory parameters showed satisfactory analytical performance in between 3-6 when evaluated by six sigma metrics. Conclusion: Control frequency for parameters showed score > 6 can be reduce for saving laboratory resources whereas parameters with sigma score around 3 needs more vigilant monitoring. Health care and laboratory outcome need high quality results to improve patient health. Six sigma tool allows laboratory to identify the right method, right rule, run controls at right frequency to enable the right patient outcome. [ABSTRACT FROM AUTHOR]

Background: The application of the Six Sigma (σ) metric in biochemical laboratories is a powerful tool for reducing the occurrence of errors and prioritizing important improvements in laboratory quality control. The National Accreditation Board for Testing and Calibration Laboratories (NABL) is an accreditation body with an accreditation system established in accordance with ISO/IEC 17011, providing specialty- or scope-based certification based on the conformance of quality indices to medical laboratories. In this context, a study has been designed that considers the quality guidelines set by NABL as well as the sigma metric rule in the assessment of analytical performance. Aims and Objectives: The aims of this study were to identify the gaps and need for strategy modification for quality improvement by assessing the performance of two NABL-accredited medical testing laboratories on a Sigma metric scale. Materials and Methods: A retrospective analytical study was conducted over 6 months (January-June 2021). Internal quality control (IQC) and EQAS data were obtained from third-party QC providers (Bio-Rad, India) and analyzed by calculating sigma (σ) values based on the coefficient of variation, bias, and total error allowable in two NABL-accredited medical testing laboratories. To identify potential problems for analytes with poor sigma values, a quality goal index (QGI) analysis was performed. Results: By analyzing the sigma values obtained by both NABL-accredited laboratories, we can see that laboratory 2 performed better than laboratory 1. After calculating the QGI, there was a problem of inaccuracy and imprecision in laboratory 1, and laboratory 2 had QGI values that indicated only imprecision. Conclusion: Diagnostic laboratories should incorporate Six Sigma metrics to identify gaps in their performance to ensure better quality control and patient safety. [ABSTRACT FROM AUTHOR]

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 csf 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 26 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. [ABSTRACT FROM AUTHOR]

• A six sigma model was successfully applied for the quality management of CSF biochemical analytes. • We evaluated the analytical performance of CSF biochemical analytes using the six sigma model and formulated individualized IQC procedures. • We provided appropriate improvement measures for the CSF biochemical analytes with a sigma less than 6. In this study, we applied a six sigma model to examine cerebrospinal fluid (CSF) biochemical analytes for the first time. Our goal was to evaluate the analytical performance of various CSF biochemical analytes, design an optimized internal quality control (IQC) strategy, and formulate scientific and reasonable improvement plans. The sigma values of CSF total protein (CSF-TP), albumin (CSF-ALB), chloride (CSF-Cl), and glucose (CSF-GLU) were calculated using the following formula: sigma = [TEa(%)−|bias(%)|]/CV(%). The analytical performance of each analyte was shown using a normalized sigma method decision chart. Individualized IQC schemes and improvement protocols for CSF biochemical analytes were formulated using the Westgard sigma rule flow chart with batch size and quality goal index (QGI). The distribution of sigma values for CSF biochemical analytes ranged from 5.0 to 9.9, and the sigma values varied for different concentrations of the same analyte. The analytical performance of the CSF assays at the two QC levels is displayed visually in normalized sigma method decision charts. Individualized IQC strategies for CSF biochemical analytes were as follows: for CSF-ALB, CSF-TP and CSF-Cl, use 1 3s with N = 2 and R = 1000; for CSF-GLU, use 1 3s /2 2s /R 4s with N = 2 and R = 450. In addition, priority improvement measures for analytes with sigma values less than 6 (CSF-GLU) were formulated based on the QGI, and their analytical performance was improved after the corresponding improvement measures were taken. The six sigma model has significant advantages in practical applications involving CSF biochemical analytes and is highly useful for quality assurance and quality improvement. [ABSTRACT FROM AUTHOR]

Background: Limitations of Internal quality control (IQC) and external quality assessment system (EQAS) like cost for repeated analysis is a challenge to most of the clinical Biochemistry laboratories. Six sigma system is a part of lean laboratory practices. The higher the sigma value, chance of false test results are less likely. Aim: To calculate sigma metrics of liver function parameters in laboratory and using it to decide on the QC strategy using Westgard Sigma rules for reliable and accurate report. Methods: IQC data for Total bilirubin, Direct bililrubin , AST, ALT, ALP, Total protein & Albumin were analysed for 6 months. Sigma value is calculated and Quality goal index used to identify the reason for low sigma value. Results: Both AST and ALP in level 1 and AST, ALT and ALP in Level 2 has sigma value above 6. Parameters which showed sigma value between 3-6 are ALT and Albumin in Level 1 and BIT and TP in level 2 QC. Parameters which showed sigma value below 3 are BIT, BID & TP in Level 1 &BID and Albumin in Level 2 QC. Conclusion: Appropriate control limits and control measurements can be adapted for each parameters with westgard sigma rule and laboratories can use it as a self-assessment tool.

Introduction: The analytical phase of the total testing process is the one in which the clinical biochemist can directly intervene to improve the quality of tests reporting. The sigma metrics and Operational Process Specification (OPSpec) chart can specify to which category the laboratory belongs. Aim: To apply sigma metrics to analytical process of testing, do the root cause analysis and apply the corrective measures according to Westgard rules to improve laboratory performance towards the quality assurances. Materials and Methods: This was a retrospective-prospective study carried out in a clinical laboratory of MKCG Medical College and Hospital, Berhampur, Odisha, India, from July 2020 to March 2021. A retrospective secondary data analysis of six months duration was carried out in a clinical chemistry laboratory with a follow-up prospective study for three months. During this period, 16 analytes were tabulated to analyse the Internal Quality Control (IQC). External Quality Control (EQC) for the same analytes were obtained on monthly basis and the sigma metrics was calculated for each analytes. For analytes with sigma value <3, appropriate measures were taken according to Westgard rules to improvise the quality of laboratory investigations. The statistical analysis of sigma metrics was performed in “R” v-3.6.3. Results: Out of total 16 analytes, three analytes at level 1 and two analytes at level 2 Quality control (QC) showed a world class performance whereas four analytes showed a poor performance at both the QC levels with sigma metrics value <3. From Quality Goal Index (QGI) and root cause analysis, the source of error was detected and corrected. Conclusion: The inaccuracy and imprecision of different parameters in the analytical phase of the testing process can be addressed by calculating the sigma metrics and do the root cause analysis. Application of corrective measures according to Westgard rule can improve the laboratory performance towards the quality assurance. [ABSTRACT FROM AUTHOR]

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 ( 1.2) for only 2 parameters SGPT and SGOT (with sigma value

Objectives: Sigma metric model is one of the most popular quality management system tools used for Six Sigma process improvement. This model provides three features that improve the results: low inaccuracy, less deviation, and correct performance of the analytical method. Internal quality control and external quality assessment programs are routinely carried out in all clinical laboratories to evaluate and continually improve the analytical quality. The Six Sigma model is a global quality management system that can also be applied in the determination of glycated hemoglobin (HbA1c). In recent years, this model has been supported by Quality Goal Index (QGI). In this study, we aimed to evaluate the analytical performances of Arkray HA8180V HbA1c analyzer according to internal and external quality Sigma metrics and QGI. Methods: The data have been evaluated according to two internal control materials (Bio-DPC and KBUDEK External Quality Program) to calculate Sigma levels [Sigma=(TEa%-bias%)/CV%] and quality target indexes (QGI=bias/1.5×CV), where TEa is the total analytical error and CV is the coefficient of variation. QGI is a metric that can distinguish between techniques to deal with sensitivity and accuracy issues as well as calibrator lot changes. Results: The mean Sigma levels for low and high quality control materials were found to be 5.17 and 2.51, respectively. QGI was found to be 0.8-1.2 for both levels. Conclusion: The performance of HA8180V HbA1c analyzers was found to be acceptable compared to Sigma metrics. The values of OGI between 0.8 and 1.2 indicate inaccurate and inconsistent result. However, when evaluated as a whole with Sigma values, the results of the devices were found reliable. [ABSTRACT FROM AUTHOR]

Six medical testing laboratories at six different sites in China participated in this study. We applied a six sigma model for (a) the evaluation of the analytical performance of serum enzyme assays at each of the laboratories, (b) the design of individualized quality control programs and (c) the development of improvement measures for each of the assays, as appropriate. Internal quality control (IQC) and external quality assessment (EQA) data for selected serum enzyme assays were collected from each of the laboratories. Sigma values for these assays were calculated using coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts were generated using these parameters. IQC design and improvement measures were defined using the Westgard sigma rules. The quality goal index (QGI) was used to assist with identification of deficiencies (bias problems, precision problems, or their combination) affecting the analytical performance of assays with sigma values <6. Sigma values for the selected serum enzyme assays were significantly different at different levels of enzyme activity. Differences in assay quality in different laboratories were also seen, despite the use of identical testing instruments and reagents. Based on the six sigma data, individualized quality control programs were outlined for each assay with sigma <6 at each laboratory. In multi-location laboratory systems, a six sigma model can evaluate the quality of the assays being performed, allowing management to design individualized IQC programs and strategies for continuous improvement as appropriate for each laboratory. This will improve patient care, especially for patients transferred between sites within multi-hospital systems. [ABSTRACT FROM AUTHOR]

Introduction: Any error in the laboratory testing processes can affect the diagnosis and patient management. Six Sigma is a data driven quality management system for identifying and reducing errors and variations in clinical laboratory processes. Aim: This study was carried out to estimate Sigma metrics of various biochemical analytes in order to evaluate performance of quality control and implement optimum quality control strategy for each analyte. Materials and Methods: This retrospective, observational study was conducted in year 2020 based on the data obtained for a period of six months (July 2019 to December 2019). Sigma metrics for 20 analytes was calculated by using internal quality control and external quality control data. Further, QGI was calculated for analytes having sigma value of 6 sigma). Westgard rule (13s) with two control measurement (N2) per QC event and run size (R1000) i.e. 1000 patient samples between consecutive QC events was adopted for these analytes. For analytes with sigma value of 4-6, more rules (sigma 4-5: Westgardrules13s/22s/R4s/41s, N4 and R200 and for sigma value 5-6: 13S/22s/ R4s, N2 and R450) were adopted. The sigma values of six analytes (Creatinine, Sodium, Potassium, Calcium, Chloride, Inorganic phosphate) were

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 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.

Background: The performance of 17 routine chemical detection methods was evaluated by the Sigma (s) index, and separate quality control standards were established according to the sigma values of different detection methods. Methods: The internal quality control (IQC) and external quality assessment (EQA) data of 17 assays in the biochemical laboratory of our hospital were collected from January to June 2019. Referring to the total allowed error (TEa) standards established in the Health Industry Standards of the People's Republic of China (WS/T 403-2012), the sigma metric of each assay was calculated, the performance level for inspection was evaluated, the quality goal index (QGI) was calculated for items with analysis performance < 5 sigma, and the main causes of poor performance were determined to guide quality improvement. Results: For level 1 internal quality control (IQC), five assays (AMY, Crea, UA, TP, and Na) showed a performance of = 6 sigma levels. Five assays (GGT, LDH, ALP, K, and Ca) had a performance lower than 3 sigma. For level 2 IQC, nine assays (ALT, AST, CK, AMY, Crea, UA, TP, Na, and Mg) achieved 6 sigma, and four assays (GGT, LDH, ALP, and K) achieved less than 3 sigma. Among the 12 assays with a sigma value < 5, the precision of 1 assay should be improved first, the accuracy of 6 assays should be improved next, and both the precision and the accuracy of 5 assays should be improved. Conclusions: The sigma metric is the best tool for evaluating the performance of different test methods. Assays with high sigma values can be evaluated with single-rule quality control, while assays with low values should be evaluated with strict quality control rules. [ABSTRACT FROM AUTHOR]

Background: The Six Sigma theory is an important tool for laboratory quality management. It has been widely used in clinical chemistry, haematology and other disciplines. The aim of our study was to evaluate the analytical performance of plasma proteins by application of Sigma metric and to compare the differences among three different allowable total errors in evaluating the analytical performance of plasma proteins. Methods: Three different allowable total error values were used as quality goals. Data from an external quality assessment were used as bias, and the cumulative coefficient of variation in internal quality control data was used to represent the amount of imprecision during the same period. Sigma metric of analytes was calculated using the above data. The quality goal index was calculated to provide corrected measures for continuous improvements in analytical quality. Results: The Sigma metric was highest using the external quality assessment standards of China: it was sigma ≥6 or higher in 57.1% of plasma proteins. But Sigma metric was lower by using RiliBÄK or biological variation standards. IgG, C3 and C-reactive protein all required quality improvements in imprecision. A single-rule 13s for internal quality control was recommended for IgA, IgM, C4 and rheumatoid factor, whereas multiple rules (13s/22s/R4s) were recommended for IgG, C3 and C-reactive protein, according to the external quality assessment standards of China. Conclusions: Different quality goals can lead to different Sigma metric for the same analyte. As the lowest acceptable standard in clinical practice, the external quality assessment standard of China can guide laboratories to formulate reasonable quality improvement programmes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]