scholarly journals Evaluation of Quality Control Data of Hormones Using Six Sigma Metrics Tool in Clinical Laboratory

2021 ◽  
pp. 133-137
Author(s):  
G. Anuradha ◽  
S. Santhini Gopalakrishnan ◽  
. Hemalatha
Keyword(s):  
Health Care ◽  
Quality Control ◽  
Health Care System ◽  
Six Sigma ◽  
Clinical Laboratory ◽  
Control Data ◽  
Sigma Level ◽  
Level 1 ◽  
Quality Control Data ◽  
Level 2

Background: In health care system it is necessary to provide high quality and reliable test results to the patients. Many clinical laboratories are using six sigma as a tool to improve the quality control in health care system. Keeping this in mind, the present study was conducted using the quality control data of hormones under NABL(National Accreditation Board for Testing and Calibration Laboratories) which were assayed in our clinical laboratory. Materials and Methods: In this retrospective study, both the internal and external quality control data of 11 hormones were collected for a period of 6 months from April 2020 to September 2020 and the six sigma analysis was done. Results: Testosterone level 1(6.8), level 2(6.5) and Folate level1(6.9), level 2(6.6) showed sigma level more than 6 and hence excellent performance. The hormones, FT3 level 1(3.7), level 2(4.8), HCG level 2(3.6), TSH level 1(4.8), level 2(4.7) and Vitamin B12 level 1(4.4), level 2(4.5) showed average performance with sigma level between 3.5 and 6. The hormones, FT4 level 1(1.7), level 2(2), HCG level 1(2.2), Prolactin level 1(3), level 2(3.3), FSH level 1(1.9), level 2(2.0), LH level 1(2), level 2(1.9) and Progesterone level 1(3.4), level 2(3.3) showed poor performance with sigma level less than 3.5. Conclusion: Stringent rules need not be applied for hormones with sigma>6. Moreover, control limits can be relaxed to 3S so that false rejections can be minimized. For hormones with sigma< 6, internal QC rules have to be strictly applied and the root cause analysis has to be done. To conclude, six sigma metrics is a powerful quality control tool which helps to improve the performance of the clinical laboratory and hence the efficiency of the health cares system.

scholarly journals Evaluation of Quality Control Data of Clinical Chemistry Parameters using Six Sigma Metrics Tool in Clinical Laboratory

2021 ◽  
pp. 305-309
Author(s):  
G. Anuradha ◽  
S. Santhinigopalakrishnan ◽  
S. Sumathy
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Clinical Laboratory ◽  
Clinical Biochemistry ◽  
Clinical Chemistry ◽  
Control Data ◽  
Laboratory Performance ◽  
Clinical Biochemistry Laboratory ◽  
Quality Control Data

Background: Physicians rely on laboratory results for treating patients. So it is the duty of laboratories to assure quality of the results released. So laboratory performance should be validated to maintain the quality. Six sigma has now gained recent interest in monitoring the laboratory quality.This study was designed to gauge the clinical chemistry parameters based on six sigma metrics. Materials and Methods: In this retrospective study, both the internal and external quality control data of 26 clinical chemistry parameters were collected for a period of 6 months from June 2020 to November 2020 and the six sigma analysis was done at the Central clinical biochemistry laboratory of Chettinad Hospital and research institute. Results: AST, amylase, lipase, triglyceride, HDL, iron, magnesium, creatine kinase showed sigma values more than 6.Uric acid, total protein, ALT, direct bilirubin, GGT,cholesterol, cholesterol, calcium, TIBC and phosphorus shows sigma values between 3.5 to 6. Glucose, BUN, creatinine, albumin, Na, K, Chloride, showed sigma values less than 3.5. Conclusion: Six sigma metrics can help in improving the quality of laboratory performance and also helps to standardisethe actual amount of QC that is required by the laboratory for maintaining quality.

scholarly journals An automated tool for a uniform decentralized quality control and data analysis in multicenter studies with health care registry data

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Florian Endel ◽  
Christa Strassmayr ◽  
Heinz Katschnig
Keyword(s):  
Health Care ◽  
Quality Control ◽  
Data Structure ◽  
Data Analysis ◽  
Web Application ◽  
Registry Data ◽  
Analysis Tool ◽  
Control Data ◽  
Standardized Report ◽  
Quality Control Data

ABSTRACTObjectivesThe EU FP7 funded project CEPHOS-LINK investigates hospital re-admissions of patients with a psychiatric disorder in 6 European countries by using linked health care registry data. In addition to the problems of different healthcare, payment and data collection systems, coordinating quality control, data analysis, and statistical modeling of sensitive data with six partners is challenging. For this purpose we have designed a secure online data analysis tool to diminish the time necessary to get results and incremental adaptions of reports as well as decreasing the chance and effects of misunderstandings between national and linguistic boundaries. ApproachA comprehensive study protocol clearly defining variables to be obtained and methods to be applied has been put together. The protocol is based on a thorough investigation of the different healthcare systems and related registries. It became clear that nonetheless misconceptions occur and the incremental improvements consumed vast amounts of available resources. Therefore a system which automatically creates the required reports including all tables, graphics and statistical models including data preparation based on a defined data structure has been developed. The report system is based on the statistical environment R and the document markup language LaTeX, tightly integrated with R's package “knitr”.As this highly flexible solution is not straight forward to apply and implies various technical dependencies, a secure online platform hiding all technical details from the users has been developed. Utilizing state of the art software containers based on Linux and docker, a customized VPN solution, authentication and SSL encryption were put together. The web application itself is developed with R's “shiny” package and allows users to simply upload a dataset in the predefined format, interactively explore the contents, apply filters and generate the customizable, standardized report. Additionally, an offline version of the application is available for all major (desktop) operating systems. ResultsThe new platform advances data analysis and reporting in a situation where several partners are involved in analyzing local datasets, as is the case of the CEPHOS-LINK project. Integrating new features, graphics and research topics can be managed centrally while users can update their results and reports in nearly no time. ConclusionThe additional effort spent on developing a customized platform for quality control, data analysis and reporting has been worth the effort. Benefits include quick detection of implausible results, unifying the layout and graphics often depending on the software utilized and an established common data structure.

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 A novel practical approach to calculate measurement uncertainty in clinical pathology laboratories using quality control data with the use of biological variation where applicable

2021 ◽  
Vol 10 (5) ◽  
pp. 196-210
Author(s):  
Ashraf Mina ◽  
Shanmugam Banukumar ◽  
Santiago Vazquez
Keyword(s):  
Health Care ◽  
Quality Control ◽  
Quality Assurance ◽  
Clinical Pathology ◽  
Internal Quality ◽  
External Quality ◽  
Control Data ◽  
Iso 15189 ◽  
Care Costs ◽  
Quality Control Data

Background: Measurement Uncertainty (MU) can assist the interpretation and comparison of the laboratory results against international diagnostic protocols, facilitate a reduction in health care costs and also help protect laboratories against legal challenges. Determination of MU for quantitative testing in clinical pathology laboratories is also a requirement for ISO 15189. Methods: A practical and simple to use statistical model has been designed to make use of data readily available in a clinical laboratory to assess and establish MU for quantitative assays based on internal quality control data to calculate Random Error and external quality assurance scheme results to calculate Systematic Error. The model explained in this article has also been compared and verified against quality specifications based on Biological Variation. Results: Examples that explain and detail MU calculations for the proposed model are given where different components of MU are calculated with tabulated results. Conclusions: The designed model is cost-effective because it utilises readily available data in a clinical pathology laboratory. Data obtained from internal quality control programs and external quality assurance schemes are used to calculate the MU using a practical and convenient approach that will not require resources beyond what is available. Such information can additionally be useful not only in establishing limits for MU to satisfy ISO 15189 but also in selecting and/or improving methods and instruments in use. MU can as well play an important role in reducing health care costs as shown by examples in the article.

Trend Analysis of Quality Control Data

1995 ◽  
pp. 175-188 ◽  
Author(s):  
H. R. Adams ◽  
M. A. Channing ◽  
J. E. Divel ◽  
B. B. Dunn ◽  
D. O. Kiesewetter ◽  
...  
Keyword(s):  
Quality Control ◽  
Trend Analysis ◽  
Control Data ◽  
Quality Control Data

Computerized Preparation of Two-Way Analysis of Variance Control Charts for Clinical Chemistry

1972 ◽  
Vol 18 (3) ◽  
pp. 250-257 ◽  
Author(s):  
J H Riddick ◽  
Roger Flora ◽  
Quentin L Van Meter
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
Computer Program ◽  
Analysis Of Variance ◽  
Control Charts ◽  
Clinical Chemistry ◽  
Control Data ◽  
Laboratory Error ◽  
Quality Control Data

Abstract A system of quality-control data analysis by computer is described, in which two-way analysis of variance is used for partitioning sources of laboratory error into day-to-day, within-day, betweenpools and additivity variation. The partition for additivity is described in detail as to its advantages and applications. In addition, control charts based on two-way analysis of variance computations are prepared each month by computer. This computer program is designed to operate with the IBM 1800 or 1130 computers or any computer with a Fortran IV compiler. Examples are presented of use of the control charts and of tables of analysis of variance.

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