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

2019 ◽  
Vol 6 (5) ◽  
pp. 1524
Author(s):  
Kavita Aggarwal ◽  
Saurav Patra ◽  
Viyatprajna Acharya ◽  
Mahesh Agrawal ◽  
Sri Krushna Mahapatra

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.

Author(s):  
Mohit Mehndiratta ◽  
Eram Hussain Pasha ◽  
Nilesh Chandra ◽  
Edelbert Anthonio Almeida

Abstract Objective The aim of this study was to study the incidence of preanalytical errors in the clinical chemistry laboratory attached to a tertiary care hospital. Design and Methods The study was conducted in a clinical chemistry laboratory using the samples and forms received for analysis. Five hundred random samples were analyzed using a predefined set of quality indicators (QIs) over a period of 3 months. The incidence of each preanalytical error was described as a percentage of the total samples analyzed in the study. Statistical Analysis Individual QIs were assigned values as 0 and 1 and were used to assess each sample; 0 if the error was present, and 1 if absent. The incidence of each preanalytical error was described as a percentage of the total samples analyzed in the study. Result Out of the 500 samples observed, 138 samples were error free, while 21 samples had the maximum number of errors, that is, 6. The error committed most often was the omission of provisional diagnosis being mentioned on the requisition form. No preanalytical error was observed for QIs: selecting the appropriate blood collection vial or storage of sample. Conclusion This study confirms that error rate in the preanalytical phase is high and vastly ignored. Errors committed here may be overlooked, given the large number of samples received in the clinical laboratory of a tertiary center. To reduce these errors, the laboratory should provide training to all workers involved in the preanalytical phase. Daily or weekly QI scores should be recorded to assess and rectify shortcomings, thereby improving patient care.


2018 ◽  
Vol 10 (02) ◽  
pp. 194-199 ◽  
Author(s):  
B. Vinodh Kumar ◽  
Thuthi Mohan

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.


2019 ◽  
Vol 4 (2) ◽  
pp. 31-36
Author(s):  
Pier Angeli Medina ◽  
◽  
Jenny Matibag ◽  
Sarah Jane Datay-Lim ◽  
Elizabeth Arcellana-Nuqui

1969 ◽  
Vol 15 (11) ◽  
pp. 1039-1044 ◽  
Author(s):  
John R Allen ◽  
Rachel Earp ◽  
E Christis Farrell ◽  
H D Grümer

Abstract A quality control program utilizing both "known" and "blind" control specimens was analyzed in the routine clinical chemistry laboratory. The results obtained with the control samples of 18 automated and nonautomated procedures demonstrated the presence of analytical bias. Only through the evaluation of blind control samples tested at random times can a reliable measure of the proficiency of the laboratory be achieved.


2015 ◽  
Vol 56 (1) ◽  
pp. 54 ◽  
Author(s):  
Justice Afrifa ◽  
SethA Gyekye ◽  
WilliamKBA Owiredu ◽  
RichardKD Ephraim ◽  
Samuel Essien-Baidoo ◽  
...  

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