Assessment of quality control system by sigma metrics and quality goal index ratio: A roadmap towards preparation for NABL

Verma, Monica; Dahiya, Kiran; Ghalaut, Veena Singh; Dhupper, Vasudha

doi:10.5662/wjm.v8.i3.44

Cited by 24 publications

(31 citation statements)

References 10 publications

(14 reference statements)

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“…Verma M et al also reported a value of 9.9 and 11.8 for ALP while the sigma value for AST and ALT is <3 in their study. 7,14,15 In our study Sigma value for triglyceride is also more than 6 which is in consensus with study done by Adiga US et al while Manchana Lakshman et al reported a very high sigma value of 29.6 and 24.4 for triglyceride. 16,17 Sigma value for GGT and magnesium is reported by very few studies, Chakravarthy S et al found >6 value for both of these analytes.…”

Section: Discussionsupporting

confidence: 92%

Application of six sigma metrics and method decision charts in improvising clinical Chemistry laboratory performance enhancement

et al. 2019

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Background: Six sigma is a powerful tool which can be used by laboratories for assessing the method quality, optimizing Quality Control (QC) procedure, change the number of rules applied, and frequency of controls run .The aim of this study was to quantify the defects or errors in the analytical phase of laboratory testing by sigma metrics and then represent the sigma value in Method Decision Chart.Methods: A retrospective study was conducted in a tertiary care hospital in Bhubaneswar, India. The clinical chemistry laboratory has been NABL accredited for the past 5 years and strictly quality checked. Internal and external quality control data was collected for a period of six months from January - June 2018 for 20 biochemical analytes. Sigma metrics for each parameter was calculated and plotted on method decision chart.Results: The sigma metrics for level 2 indicated that 6 out of the 20 analytes qualified Six Sigma quality performance. Of these seven analytes failed to meet minimum sigma quality performance with metrics less than three and another seven analytes performance with sigma metrics was between three and six. For level 3, the data collected indicated that seven out of 20 analytes qualified Six Sigma quality performance, six analytes had sigma metrics less than 3 and seven analytes had sigma metrics between 3 and 6.Conclusion: In our study Sigma value was highest for amylase and lowest for potassium. Use of alternative methods and/ or change of reagents can be done for potassium to bring the sigma value within an acceptable range.

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Section: Discussionsupporting

confidence: 92%

Application of six sigma metrics and method decision charts in improvising clinical Chemistry laboratory performance enhancement

et al. 2019

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“…More interestingly, wide variations in sigma values between the two QC material levels and the two analysis modules were discovered for ALP, CRE, TP, ALT, and ECT in this study (Tables and ). However, this situation was not particular to this study because it was also discovered in other studies . The two analysis modules could be considered two separate analyzers, and differences in performance could not be avoidable.…”

Section: Discussionmentioning

confidence: 81%

“…The QGI was calculated using the formula

QGI = Bias / (1.5 \times CV)

. This index can help determine the main reason why the testing performance of a clinical chemistry project yields a lower sigma level and might aid the selection of the best quality improvement plan . A sigma value less than 4 (σ < 4) was used as the benchmark for the QGI analysis of analytes in this study.…”

Section: Methodsmentioning

confidence: 99%

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Practical application of Six Sigma management in analytical biochemistry processes in clinical settings

Zhou

et al. 2019

Clinical Laboratory Analysis

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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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“…[11] Verma et al study reveals that if TE of an analyte is within allowable error limits specific for that analyte, bias % and CV % might be more reliable than sigma metrics. [12] TE for levels 1&2 shows the values did not exceed the allowable total error recommended by CLIA. The suggested guidelines for choosing the Westgard rules and levels of IQC processed are as follows: for the biochemical parameters with Sigma Scale 6 or above (Excellent performance), evaluate with one level of QC per day (Alternating levels between days) and follow 1-3 s Westgard rule alone.…”

Section: Discussionmentioning

confidence: 84%

A Short-Term Assessment of Routine Chemistry Parameters by Sigma Metrics and Quality Goal Index Ratio in a Tertiary Care Hospital Laboratory

Sayeed¹,

Ganji²,

Mopuri³

2019

jemds

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BACKGROUND Sigma metrics is an important tool to evaluate the errors in quality control of laboratory system. It can easily quantify the exact number of errors by combining bias, precision, and total allowable error (TEa). Quality goal index is a newer parameter to represent the relative extent to which both bias and precision meet their respective quality goals. The aim of the present study is to assess the Internal and External quality control by Sigma Metrics and Quality Goal Index ratio (QGI). METHODSThis observational study was conducted at Clinical Biochemistry Laboratory, Osmania General Hospital, Hyderabad, Telangana. Data of 8 most commonly run analytes: glucose, urea, creatinine, total proteins, total bilirubin, cholesterol, albumin and uric acid were extracted over a period of 3 months. The two levels of controls of these parameters were run on a Beckman Coulter AU5800 autoanalyser. Mean, SD, and coefficient of variance (CV) were calculated for each month from IQC. Bias obtained from EQAS. Sigma, QGI and TE were calculated subsequently. RESULTSThe sigma metrics for level 1 for all eight parameters indicated that 5 failed to meet a minimum sigma quality performance wi th metrics less than 3 and another 2 just met minimal acceptable performance with sigma metrics between 3 and 6. For level 2, none of the parameters achieved six sigma, of which 5 parameters had metrics less than 3 and 3 had metrics between 3 and 6. The calculated TE of all the parameters was less than the specified TEa. QGI ratio indicated that out of five parameters of level 1 and level 2, which failed to meet six sigma quality performances, the main problem was imprecision in all parameters except albumin which showed both imprecision and inaccuracy for both levels. CONCLUSIONSBoth Internal and External quality control for the most routinely run parameters in our hospital need stringent monitoring in order to fulfill the requirements of a standard clinical laboratory. KEY WORDSExternal Quality Assurance Scheme (EQAS), Internal Quality Control (IQC), Quality Goal Index (QGI), Six Sigma HOW TO CITE THIS ARTICLE: Sayeed S, Ganji SB, Mopuri R. A short-term assessment of routine chemistry parameters by sigma metrics and quality goal index ratio in a tertiary care hospital laboratory. BACKGROUND Clinical biochemistry investigations are important in diagnosis and treatment. It is essential to follow a proper quality management system (QMS) to provide accurate and reliable reports in an agreed upon time frame. Sigma metrics is an important tool to evaluate the errors in quality control of laboratory system. Sigma is a metric that quantifies the performance of a process at a rate of defects per million. The sigma value indicates how often errors are likely to occur. The sigma value of one corresponds to 31% of accuracy which is equal to 698,000 defects per million tests.

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Assessment of quality control system by sigma metrics and quality goal index ratio: A roadmap towards preparation for NABL

Cited by 24 publications

References 10 publications

Application of six sigma metrics and method decision charts in improvising clinical Chemistry laboratory performance enhancement

Application of six sigma metrics and method decision charts in improvising clinical Chemistry laboratory performance enhancement

Practical application of Six Sigma management in analytical biochemistry processes in clinical settings

A Short-Term Assessment of Routine Chemistry Parameters by Sigma Metrics and Quality Goal Index Ratio in a Tertiary Care Hospital Laboratory

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