Quality Management

Klee, George G.; Westgard, James O.

doi:10.1016/b978-1-4160-6164-9.00008-1

Cited by 16 publications

(5 citation statements)

References 87 publications

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“…[1] To achieve Six sigma goal, the error rate should be 0.1% (4.6 IJBR (2015) 6 (03) www.ssjournals.com sigma) to 0.01% (5.2 sigma) and ultimately 0.001% (5.8 sigma). 2 We aimed to gauze the sigma values on sigma metrics scale.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of sigma metrics in a Medical Biochemistry lab

Lakshman

Reddy

Bhulaxmi

et al. 2015

Int Jour of Biomed Res

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Sigma metrics is calculated in our lab and evaluated from December 2013 to November 2014. It is observed that Triglycerides, Lactate, Uric acid, AST, Urea, Creatine kinase(CK), Phosphate, Total Bilirubin are the best performers and the sigma value is more than 6.0 in both in normal and abnormal levels. Iron and Creatinine are best performers in normal level and Prolactin and Vitamin B12 are best performers in abnormal level. Amylase and LDH are the poor performers at level 1 (normal), though they show sigma between 3 to 6 at level 2 which is clinically acceptable.

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Section: Introductionmentioning

confidence: 99%

Evaluation of sigma metrics in a Medical Biochemistry lab

Lakshman

Reddy

Bhulaxmi

et al. 2015

Int Jour of Biomed Res

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“…The obtained sigma value indicates the rate of errors in a process. A higher sigma value indicates fewer inaccurate results reported by the laboratory 21. The quality performance of average processes has a sigma value of approximately 4 irrespective of their complexity 22…”

Section: Discussionmentioning

confidence: 99%

Detecting Preanalytical Errors Using Quality Indicators in a Hematology Laboratory

Alshaghdali

Alcantara

Rezgui

et al. 2021

Quality Management in Health Care

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Background and Objectives: Monitoring laboratory performance continuously is crucial for recognizing errors and fostering further improvements in laboratory medicine. This study aimed to review the quality indicators (QIs) and describe the laboratory errors in the preanalytical phase of hematology testing in a clinical laboratory. Methods: All samples received in the Hematology Laboratory of the Maternity and Pediatric Hospital in Hail for 3 years were retrospectively reviewed and evaluated for preanalytical issues using a set of QIs. The rate of each QI was compared to the quality specifications cited in the literature. Results: A total of 95002 blood samples were collected for analysis in the hematology laboratory from January 2017 through December 2019. Overall, 8852 (9.3%) were considered to show preanalytical errors. The most common were “clotted specimen” (3.6%) and “samples not received” (3.5%). Based on the quality specifications, the preanalytical QIs were classified generally as low and medium level of performance. In contrast, the sigma-based performance level indicates acceptable performance on all the key processes. Further analysis of the study showed a decreasing rate of preanalytical errors from 11.6% to 6.5%. Conclusions: Preanalytical errors remain a challenge to hematology laboratories. The errors in this case were predominantly related to specimen collection procedures that compromised the specimen quality. Quality indicators are a valuable instrument in the preanalytical phase that allows an opportunity to improve and explore clinical laboratory process performance and progress. Continual monitoring and management of QI data are critical to ensure ongoing satisfactory performance and to enhance the quality in the preanalytical phase.

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“…Quality control materials (QCM) or other reference materials are routinely used to monitor performance of analyzers and validate assay methods at human and veterinary clinical pathology laboratories and point‐of‐care locations . The American Society for Veterinary Clinical Pathology (ASVCP) quality assurance guidelines note: “Control materials can be pooled patient samples or commercially purchased controls or calibrators” .…”

Section: Introductionmentioning

confidence: 99%