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

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.

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Application of Sigma Metrics for Evaluating the Analytical Performance of Thyroid Profile and Cortisol in Clinical Biochemistry Laboratory

JOURNAL FOR CLINICAL AND DIAGNOSTIC RESEARCH ◽

10.7860/jcdr/2020/47345.14391 ◽

2020 ◽

Author(s):

Smita Natvarbhai Vasava ◽

Roshni Gokaldas Sadaria

Keyword(s):

Quality Control ◽

Six Sigma ◽

Clinical Laboratory ◽

Clinical Biochemistry ◽

Poor Performance ◽

Analytical Performance ◽

Internal Quality ◽

Six Sigma Methodology ◽

Clinical Biochemistry Laboratory ◽

Thyroid Profile

Introduction: Now-a-days quality is the key aspect of clinical laboratory services. The six sigma metrics is an important quality measurement method for evaluating the performance of the clinical laboratory. Aim: To assess the analytical performance of clinical biochemistry laboratory by utilising thyroid profile and cortisol parameters from Internal Quality Control (IQC) data and to calculate sigma values. Materials and Methods: Study was conducted at Clinical Biochemistry Laboratory, Dhiraj General Hospital, Piparia, Gujarat, India. Retrospectively, IQC data of thyroid profile and cortisol were utilised for six subsequent months (July to December 2019). Coefficient of Variation (CV%) and bias were calculated from IQC data, from that the sigma values were calculated. The sigma values <3, >3 and >6 were indicated by poor performance procedure, good performance and world class performance, respectively. Results: The sigma values were estimated by calculating mean of six months. The mean sigma value of Thyroid Stimulating Hormone (TSH) and Cortisol were >3 for six months which indicated the good performance. However, sigma value of Triiodothyronine (T3), Tetraiodothyronine (T4) were found to be <3 which indicated poor performance. Conclusion: Six sigma methodology applications for thyroid profile and cortisol was evaluated, it was generally found as good. While T3 and T4 parameters showed low sigma values which requires detailed root cause analysis of analytical process. With the help of six sigma methodology, in clinical biochemistry laboratories, an appropriate Quality Control (QC) programming should be done for each parameter. To maintain six sigma levels is challenging to quality management personnel of laboratory, but it will be helpful to improve quality level in the clinical laboratories.

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

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2021/v33i55b33856 ◽

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.

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

Clinical Chemistry ◽

10.1093/clinchem/18.3.250 ◽

1972 ◽

Vol 18 (3) ◽

pp. 250-257 ◽

Cited By ~ 4

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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Quality Management in Libraries Case Study: Book Collection Retrieval in Academic Libraries

Khizanah al-Hikmah Jurnal Ilmu Perpustakaan Informasi dan Kearsipan ◽

10.24252/kah.v9cf1 ◽

2021 ◽

Vol 9 (2) ◽

pp. 182

Author(s):

Vilianty Rizki Utami ◽

Desni Sri Hastuti Sihite

Keyword(s):

Quality Control ◽

Quality Management ◽

Comprehensive Evaluation ◽

Case Study Method ◽

Control Data ◽

User Expectations ◽

And Performance ◽

Quality Control Data

Quality management in the library is related to quality control of the library's work to meet user expectations and contributes to the continued success of the organization. However, quality control is lacking and not be the main focus in many libraries. This paper aims to explain how quality management improves work results in libraries. The research method used in this paper is qualitative research using a case study method. We conduct research in Library X that already underwent quality control in book processing activities. The data were collected through observation, and interviews for book processing activities and its quality control data during 2018-2020. The data was then analyzed document analysis. The study found that Library X could perform better by improving the quality of book processing and fixing the error just before they put the book on its shelves. Quality control gives a comprehensive evaluation in Library X either for humans, processes, and systems of book processing activities that help Library X conduct its duty to provide their user needs and expectations. Quality control and quality management also help Library X describe the library working atmosphere and can be used for giving motivation to all librarians and staff to give better service and performance for the end-users.

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Assessment of six sigma metrics applications of quality control in clinical biochemistry laboratory

MedPulse International Journal of Biochemistry ◽

10.26611/10021136 ◽

2019 ◽

Vol 11 (3) ◽

pp. 100-103

Author(s):

Ramya KR ◽

◽

Vijetha Shenoy Belle ◽

Pravesh Hegde ◽

Sushma Jogi ◽

...

Keyword(s):

Quality Control ◽

Six Sigma ◽

Clinical Biochemistry ◽

Clinical Biochemistry Laboratory

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Computer-Assisted Analysis of Clinical Chemistry Quality Control Data

Laboratory Medicine ◽

10.1093/labmed/8.6.24 ◽

1977 ◽

Vol 8 (6) ◽

pp. 24-31 ◽

Cited By ~ 1

Author(s):

Paul R. Finley ◽

Roberta Hahn ◽

John Gaines ◽

Donald Lichti ◽

James E. Peebles

Keyword(s):

Quality Control ◽

Clinical Chemistry ◽

Computer Assisted ◽

Control Data ◽

Assisted Analysis ◽

Quality Control Data

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The use of six sigma methodology to evaluate the analytical performances of clinical chemistry analyzers

Turkish Journal of Biochemistry ◽

10.1515/tjb-2016-0223 ◽

2016 ◽

Vol 43 (1) ◽

pp. 1-8

Author(s):

Özlem Gülbahar ◽

Murat Kocabıyık ◽

Mehmed Zahid Çıracı ◽

Canan Demirtaş ◽

Fatma Uçar ◽

...

Keyword(s):

Six Sigma ◽

Clinical Laboratory ◽

Clinical Chemistry ◽

Total Quality ◽

Biochemical Tests ◽

Analytical Process ◽

World Class ◽

Six Sigma Methodology ◽

Analytical Platforms

AbstractIntroduction:In our study, we aimed to evaluate the analytical process performances of the biochemistry tests in the analysis systems that were widely used in the clinical laboratories by using the six-sigma methodology.Methods:The analytical performances of four different analytical platforms (Beckman Coulter-Olympus AU2700, Abbott-Architect C8000, Roche-Cobas 8000, and Siemens-ADVIA 2400) running 18 biochemical tests (urea, creatinine, uric acid, total bilirubin, AST, ALT, ALP, LDH, HDL-C, CaResults:The parameters that have σ≥6 which means in world class are HDL-C and ALP in all four systems, while only NaDiscussion and conclusion:To improvement and monitoring of the analytical process performance as a part of total quality of a clinical laboratory to provide continuous improving, sigma levels can be used as it is a reliable method.

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Accuracy in Clinical Chemistry

Clinical Chemistry ◽

10.1093/clinchem/17.2.63 ◽

1971 ◽

Vol 17 (2) ◽

pp. 63-71 ◽

Cited By ~ 10

Author(s):

Wendell T Caraway

Keyword(s):

Quality Control ◽

Long Range ◽

Clinical Laboratory ◽

Clinical Chemistry ◽

Interlaboratory Comparisons ◽

Laboratory Measurements ◽

Laboratory Performance ◽

Improved Accuracy

Abstract Improved accuracy of clinical laboratory measurements requires a broad range concept of quality control. Items considered include standardization, precision, specificity, recovery studies, interlaboratory comparisons, and long-range stability of laboratory performance.

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Sigma Metrics: A Powerful Tool for Performance Evaluation and Quality Control Planning in a Clinical Biochemistry Laboratory

JOURNAL FOR CLINICAL AND DIAGNOSTIC RESEARCH ◽

10.7860/jcdr/2021/47818.14722 ◽

2021 ◽

Author(s):

Trupti Diwan Ramteke ◽

Anita Shivaji Chalak ◽

Shalini Nitin Maksane

Keyword(s):

Quality Control ◽

Quality Management System ◽

Clinical Laboratory ◽

Clinical Biochemistry ◽

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Internal Quality ◽

Method Performance ◽

Control Measurement ◽

Clinical Biochemistry Laboratory

Introduction: Any error in the laboratory testing processes can affect the diagnosis and patient management. Six Sigma is a data driven quality management system for identifying and reducing errors and variations in clinical laboratory processes. Aim: This study was carried out to estimate Sigma metrics of various biochemical analytes in order to evaluate performance of quality control and implement optimum quality control strategy for each analyte. Quality goal index (QGI) was also calculated to identify the problems of inaccuracy and imprecision for parameters having lower sigma values. Materials and Methods: This retrospective, observational study was conducted at the Central Clinical Biochemistry Laboratory of Seth GS Medical College and KEM hospital in Mumbai for a period of six months (July 2019 to December 2019). Sigma metrics for 20 analytes was calculated by using internal quality control and external quality control data. Further, QGI was calculated for analytes having sigma value of <4 to identify imprecision or inaccuracy. Statistical analysis was performed using Microsoft office excel 2010 software. Results: Total protein, Glucose, Urea, Triglyceride (TAG), High Density Lipoprotein (HDL), and Low Density Lipoprotein (LDL) for normal (L1) and pathological (L2) controls achieved excellent performance (>6 sigma). Westgard rule (13s) with two control measurement (N2) per QC event and run size (R1000) i.e. 1000 patient samples between consecutive QC events was adopted for these analytes. For analytes with sigma value of 4-6, more rules (sigma 4-5: Westgardrules-13s/22s/R4s/41s, N4 and R200 and for sigma value 5-6: 13S/22s/R4s, N2 and R450) were adopted. The sigma values of six analytes (Creatinine, Sodium, Potassium, Calcium, Chloride, Inorganic phosphate) were <4 at one or more QC levels. For these analytes, strict QC procedures (Westgard rules-13s/22s/R4s/41s/6x, N4 and R45) were incorporated. QGI of these analytes was <0.8 which indicated the problem of imprecision. Staff training programs and review of standard operating procedures were done for these analytes to improve method performance. Conclusion: Sigma Metrics estimation helps in designing optimum QC protocols, minimising unnecessary QC runs and reducing the cost for analytes having high sigma metrics. Focused and effective QC strategy for analytes having low sigma helps in improving the performance of those analytes.

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