Computerized Techniques for Quality Control in the Clinical Chemistry Laboratory

1976 ◽  
pp. 143-150
Keyword(s):  
Quality Control ◽  
Clinical Chemistry ◽  
Chemistry Laboratory ◽  
Clinical Chemistry Laboratory

Analytical Bias in a Quality Control Scheme

1969 ◽  
Vol 15 (11) ◽  
pp. 1039-1044 ◽  
Author(s):  
John R Allen ◽  
Rachel Earp ◽  
E Christis Farrell ◽  
H D Grümer
Keyword(s):  
Quality Control ◽  
Clinical Chemistry ◽  
Control Program ◽  
Chemistry Laboratory ◽  
Quality Control Program ◽  
Control Scheme ◽  
Clinical Chemistry Laboratory ◽  
Random Times ◽  
Quality Control Scheme ◽  
Control Samples

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.

scholarly journals Application of sigma metrics for the assessment of quality control in clinical chemistry laboratory in Ghana: A pilot study

2015 ◽  
Vol 56 (1) ◽  
pp. 54 ◽  
Author(s):  
Justice Afrifa ◽  
SethA Gyekye ◽  
WilliamKBA Owiredu ◽  
RichardKD Ephraim ◽  
Samuel Essien-Baidoo ◽  
...  
Keyword(s):  
Quality Control ◽  
Pilot Study ◽  
Clinical Chemistry ◽  
Chemistry Laboratory ◽  
Clinical Chemistry Laboratory

Patients as Their Own Controls: Use of the Computer to Identify "Laboratory Error"

1975 ◽  
Vol 21 (11) ◽  
pp. 1648-1653 ◽  
Author(s):  
Jack H Ladenson
Keyword(s):  
Quality Control ◽  
Clinical Laboratory ◽  
Clinical Chemistry ◽  
Chemistry Laboratory ◽  
Test Results ◽  
Total Calcium ◽  
Laboratory Error ◽  
Total Thyroxine ◽  
Clinical Chemistry Laboratory ◽  
Check System

Abstract I describe a system of quality control based on computer detection of changes in individual patient test results. This system, called "delta check," was used to follow all the tests performed by the clinical chemistry laboratory in a 1200-bed hospital. Analysis of 22 months’ experience indicates that specimen misidentification is a serious problem in the clinical chemistry laboratory. Over a nine-month period, errors were most frequent in the results for total thyroxine, total calcium, and total protein. Instances of laboratory error detectable by the delta check system are not detected by other currently used methods of quality control. This system therefore appears to be a valuable asset to the clinical laboratory

scholarly journals Sigma metrics as a tool for evaluating the performance of internal quality control in a clinical chemistry laboratory

2018 ◽  
Vol 10 (02) ◽  
pp. 194-199 ◽  
Author(s):  
B. Vinodh Kumar ◽  
Thuthi Mohan
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Biochemical Parameters ◽  
Clinical Chemistry ◽  
Chemistry Laboratory ◽  
Internal Quality ◽  
Average Performance ◽  
Sigma Level ◽  
Clinical Chemistry Laboratory ◽  
Level 1

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.

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
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Performance Enhancement ◽  
Clinical Chemistry ◽  
Tertiary Care ◽  
Alternative Methods ◽  
Chemistry Laboratory ◽  
Care Hospital ◽  
Quality Performance ◽  
Clinical Chemistry Laboratory

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.

Sign in / Sign up

Export Citation Format

Share Document