S. Nilakantam, Kusuma Kasapura Shivashankar, Akila Prashant, Melanahalli Dayananda, Suma M. Nataraj, Namratha G Dayananda
{"title":"西格玛-度量分析评价使用RCA和QGI在印度南部临床生物化学实验室的分析过程的质量管理","authors":"S. Nilakantam, Kusuma Kasapura Shivashankar, Akila Prashant, Melanahalli Dayananda, Suma M. Nataraj, Namratha G Dayananda","doi":"10.55691/2278-344x.1035","DOIUrl":null,"url":null,"abstract":"Introduction : This study aimed to identify laboratory errors at the earliest through Sigma-metric analysis and to evaluate quality management of analytical processes. Methods : Sigma-metrics and Quality Goal Index (QGI) were calculated by harvesting the IQC and EQC data of an accredited laboratory for 31 biochemical parameters run on Roche Cobas6000 and e411. Those with Sigma (cid:1) 2 were further analysed by applying the various Westgard rules, as suggested Results : Nearly 13 chemistry analytes showed world-class performance with Sigma > 6 and most of the immunoassay parameters showed marginal performance with sigma > 2 (cid:1) 6. Sodium, Chloride, Total T4, Beta-HCG and TSH were found to have Sigma < 2 indicating unacceptable performance. A signi fi cant improvement was observed in the Sigma-metrics analysis after performing the root cause analysis Conclusion : Sigma-metric analyses the quality management of various analytical processes in biochemistry. The poor assay performance will be picked up by the Root cause analysis and Quality Goal Indices calculation. With the help of RCA and QGI, we plan to increase the resource management by decreasing the frequency of QC runs.","PeriodicalId":54094,"journal":{"name":"International Journal of Health and Allied Sciences","volume":"13 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sigma-Metric Analysis to Evaluate Quality Management of Analytical Processes Using RCA and QGI in a Clinical Biochemistry Laboratory, South India\",\"authors\":\"S. Nilakantam, Kusuma Kasapura Shivashankar, Akila Prashant, Melanahalli Dayananda, Suma M. Nataraj, Namratha G Dayananda\",\"doi\":\"10.55691/2278-344x.1035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Introduction : This study aimed to identify laboratory errors at the earliest through Sigma-metric analysis and to evaluate quality management of analytical processes. Methods : Sigma-metrics and Quality Goal Index (QGI) were calculated by harvesting the IQC and EQC data of an accredited laboratory for 31 biochemical parameters run on Roche Cobas6000 and e411. Those with Sigma (cid:1) 2 were further analysed by applying the various Westgard rules, as suggested Results : Nearly 13 chemistry analytes showed world-class performance with Sigma > 6 and most of the immunoassay parameters showed marginal performance with sigma > 2 (cid:1) 6. Sodium, Chloride, Total T4, Beta-HCG and TSH were found to have Sigma < 2 indicating unacceptable performance. A signi fi cant improvement was observed in the Sigma-metrics analysis after performing the root cause analysis Conclusion : Sigma-metric analyses the quality management of various analytical processes in biochemistry. The poor assay performance will be picked up by the Root cause analysis and Quality Goal Indices calculation. With the help of RCA and QGI, we plan to increase the resource management by decreasing the frequency of QC runs.\",\"PeriodicalId\":54094,\"journal\":{\"name\":\"International Journal of Health and Allied Sciences\",\"volume\":\"13 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Health and Allied Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55691/2278-344x.1035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Health and Allied Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55691/2278-344x.1035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sigma-Metric Analysis to Evaluate Quality Management of Analytical Processes Using RCA and QGI in a Clinical Biochemistry Laboratory, South India
Introduction : This study aimed to identify laboratory errors at the earliest through Sigma-metric analysis and to evaluate quality management of analytical processes. Methods : Sigma-metrics and Quality Goal Index (QGI) were calculated by harvesting the IQC and EQC data of an accredited laboratory for 31 biochemical parameters run on Roche Cobas6000 and e411. Those with Sigma (cid:1) 2 were further analysed by applying the various Westgard rules, as suggested Results : Nearly 13 chemistry analytes showed world-class performance with Sigma > 6 and most of the immunoassay parameters showed marginal performance with sigma > 2 (cid:1) 6. Sodium, Chloride, Total T4, Beta-HCG and TSH were found to have Sigma < 2 indicating unacceptable performance. A signi fi cant improvement was observed in the Sigma-metrics analysis after performing the root cause analysis Conclusion : Sigma-metric analyses the quality management of various analytical processes in biochemistry. The poor assay performance will be picked up by the Root cause analysis and Quality Goal Indices calculation. With the help of RCA and QGI, we plan to increase the resource management by decreasing the frequency of QC runs.