scholarly journals Six Sigma revisited: We need evidence to include a 1.5 SD shift in the extraanalytical phase of the total testing process

2020 ◽  
Vol 30 (1) ◽  
pp. 149-152
Author(s):  
Abdurrahman Coskun ◽  
Cristiano Ialongo
Keyword(s):  
Six Sigma ◽  
Laboratory Medicine ◽  
Distributed Data ◽  
Process Performance ◽  
Constant Parameter ◽  
World Class ◽  
Six Sigma Methodology ◽  
Analytical Phase ◽  
Total Testing Process ◽  
Normally Distributed

The Six Sigma methodology has been widely implemented in industry, healthcare, and laboratory medicine since the mid-1980s. The performance of a process is evaluated by the sigma metric (SM), and 6 sigma represents world class performance, which implies that only 3.4 or less defects (or errors) per million opportunities (DPMO) are expected to occur. However, statistically, 6 sigma corresponds to 0.002 DPMO rather than 3.4 DPMO. The reason for this difference is the introduction of a 1.5 standard deviation (SD) shift to account for the random variation of the process around its target. In contrast, a 1.5 SD shift should be taken into account for normally distributed data, such as the analytical phase of the total testing process; in practice, this shift has been included in all type of calculations related to SM including non-normally distributed data. This causes great deviation of the SM from the actual level. To ensure that the SM value accurately reflects process performance, we concluded that a 1.5 SD shift should be used where it is necessary and formally appropriate. Additionally, 1.5 SD shift should not be considered as a constant parameter automatically included in all calculations related to SM.

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

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.

National surveys on 15 quality indicators for the total testing process in clinical laboratories of China from 2015 to 2017

2018 ◽  
Vol 57 (2) ◽  
pp. 195-203 ◽  
Author(s):  
Min Duan ◽  
Xudong Ma ◽  
Jing Fan ◽  
Yanhong Guo ◽  
Wei Wang ◽  
...  
Keyword(s):  
Quality Indicators ◽  
Clinical Chemistry ◽  
Turnaround Time ◽  
Laboratory Medicine ◽  
General Information ◽  
Critical Values ◽  
Medical Quality ◽  
Clinical Laboratories ◽  
Analytical Phase ◽  
Total Testing Process

Abstract Background As effective quality management tools, quality indicators (QIs) are widely used in laboratory medicine. This study aimed to analyze the results of QIs, identify errors and provide quality specifications (QSs) based on the state-of-the-art. Methods Clinical laboratories all over China participated in the QIs survey organized by the National Health Commission of People’ Republic of China from 2015 to 2017. Most of these QIs were selected from a common model of QIs (MQI) established by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). All participants were asked to submit general information and original QIs data through a medical quality control data collection system. The results of QIs were reported in percentages and sigma, except turnaround time (TAT) which was measured in minutes. The 25th, 50th and 75th percentiles were, respectively, calculated as three levels of QSs, which were defined starting from the model proposed during the 1st Strategic Conference of the EFLM on “Defining analytical performance 15 years after the Stockholm Conference on Quality Specification in Laboratory Medicine”. Results A total of 76 clinical laboratories from 25 provinces in China continuously participated in this survey and submitted complete data for all QIs from 2015 to 2017. In general, the performance of all reported QIs have improved or at least kept stable over time. Defect percentages of blood culture contamination were the largest in the pre-analytical phase. Intra-laboratory TAT was always larger than pre-examination TAT. Percentage of tests covered by inter-laboratory comparison was relatively low than others in the intra-analytical phase. The performances of critical values notification and timely critical values notification were the best with 6.0σ. The median sigma level of incorrect laboratory reports varied from 5.5σ to 5.7σ. Conclusions QSs of QIs provide useful guidance for laboratories to improve testing quality. Laboratories should take continuous quality improvement measures in all phases of total testing process to ensure safe and effective tests.

Long story short: an introduction to the short-term and longterm Six Sigma quality and its importance in laboratory medicine for the management of extra-analytical processes

2018 ◽  
Vol 56 (11) ◽  
pp. 1838-1845 ◽  
Author(s):  
Cristiano Ialongo ◽  
Sergio Bernardini
Keyword(s):  
Six Sigma ◽  
Control Charts ◽  
Clinical Chemistry ◽  
Laboratory Medicine ◽  
Effective Control ◽  
Full Potential ◽  
Short Term ◽  
Performance Requirements ◽  
Analytical Phase

Abstract There is a compelling need for quality tools that enable effective control of the extra-analytical phase. In this regard, Six Sigma seems to offer a valid methodological and conceptual opportunity, and in recent times, the International Federation of Clinical Chemistry and Laboratory Medicine has adopted it for indicating the performance requirements for non-analytical laboratory processes. However, the Six Sigma implies a distinction between short-term and long-term quality that is based on the dynamics of the processes. These concepts are still not widespread and applied in the field of laboratory medicine although they are of fundamental importance to exploit the full potential of this methodology. This paper reviews the Six Sigma quality concepts and shows how they originated from Shewhart’s control charts, in respect of which they are not an alternative but a completion. It also discusses the dynamic nature of process and how it arises, concerning particularly the long-term dynamic mean variation, and explains why this leads to the fundamental distinction of quality we previously mentioned.

Defects Reduction in Steering Gear Product Using Six Sigma Methodology

2014 ◽  
Vol 598 ◽  
pp. 647-651
Author(s):  
Ganesh Kumar Nithyanandam ◽  
Manmohanraj Raju ◽  
Gokulraj Srinivasan
Keyword(s):  
Six Sigma ◽  
South India ◽  
Lean Six Sigma ◽  
Quality Level ◽  
Systematic Methodology ◽  
Data Driven Approach ◽  
World Class ◽  
Component Manufacturing ◽  
Six Sigma Methodology ◽  
Product Defect

Lean Six Sigma is a disciplined data driven approach to improve the quality and the performance of a process or a system with which finally results to the profitability of a firm. Many of the organizations have adopted Six Sigma methodology to improve their quality and their performance to competitive industrial world. This work is concentrated with one of the leading Automotive component manufacturing company in South India. The main objective of this paper was to reduce the product defect with the application of Lean Six Sigma methodology. The paper follows the DMAIC methodology to investigate defects and its root causes and provide a solution to reduce and/or eliminate these defects. This paper also explores how a manufacturing process can use a systematic methodology to move towards world-class quality level.

scholarly journals Sigma metric revisited: True known mistakes

2018 ◽  
Vol 29 (1) ◽  
pp. 142-148 ◽  
Author(s):  
Abdurrahman Coskun ◽  
Mustafa Serteser ◽  
Ibrahim Ünsal
Keyword(s):  
Six Sigma ◽  
Laboratory Medicine ◽  
Error Rates ◽  
Performance Level ◽  
Actual Performance ◽  
A Value ◽  
Actual Error ◽  
Six Sigma Methodology ◽  
Modified Equation

Six Sigma methodology has been used successfully in industry since the mid-1980s. Unfortunately, the same success has not been achieved in laboratory medicine. In this case, although the multidisciplinary structure of laboratory medicine is an important factor, the concept and statistical principles of Six Sigma have not been transferred correctly from industry to laboratory medicine. Furthermore, the performance of instruments and methods used in laboratory medicine is calculated by a modified equation that produces a value lower than the actual level. This causes unnecessary, increasing pressure on manufacturers in the market. We concluded that accurate implementation of the sigma metric in laboratory medicine is essential to protect both manufacturers by calculating the actual performance level of instruments, and patients by calculating the actual error rates.

Productivity Improvement in the Chassis Preparation Stage of the Amplifier Production Process: A DMAIC Six Sigma Methodology

2016 ◽  
Vol 23 (06) ◽  
pp. 1640012 ◽  
Author(s):  
Anshu Gupta ◽  
Pallavi Sharma ◽  
S. C. Malik ◽  
Neha Agarwal ◽  
P. C. Jha
Keyword(s):  
Quality Improvement ◽  
Production Process ◽  
Six Sigma ◽  
Control Measure ◽  
Process Performance ◽  
Productivity Improvement ◽  
Six Sigma Methodology ◽  
Moderate Cost ◽  
And Control ◽  
Improved Technology

Manufacturing in India is witnessing a wave of growth, which is required to be supported by measures to increase productivity. Improved technology and infrastructure, design and process innovation, skill development, quality improvement and waste minimization are some of the ways that can be adopted to achieve this goal. At the same time demand for higher value at reduced price is also increasing on the consumer front. As a result, manufacturers are increasingly adopting quality improvement techniques to improve productivity and quality, reduce waste and thereby providing higher value at moderate cost. The data driven Six Sigma quality improvement methodology provides a framework to identify, eliminate and control the causes of variation in an operational process. In this paper, we study the chassis preparation process of an amplifier production process and implement the Six Sigma DMAIC model to identify the causes of variation, suggest and implement measures for improvement, and establish control measure to control the process performance post implementation stage. The implementation of the DMAIC methodology improved the process performance and provided measure to maintain dependable quality in the process.

Improving the resolution time performance of an application support process using Six Sigma methodology

2020 ◽  
Vol 11 (4) ◽  
pp. 663-686
Author(s):  
Boby John ◽  
Rajeshwar S. Kadadevaramath
Keyword(s):  
Six Sigma ◽  
Medical Diagnostics ◽  
Process Conditions ◽  
Process Performance ◽  
Resolution Time ◽  
Content Type ◽  
Time Performance ◽  
Domain Expertise ◽  
Potential Factors ◽  
Six Sigma Methodology

Purpose This paper is a case study on the successful application of Six Sigma methodology in the information technology industry. The purpose of this paper is to improve the resolution time performance of an application support process. Design/methodology/approach Through brainstorming, the potential factors influencing the resolution time are identified. From the potential factors, the important factors, namely, day-wise ticket volume, team’s software engineering skill and domain expertise are shortlisted using test of hypothesis, correlation, etc. Then a model is developed using principal component regression, linking the critical to quality characteristic with the root causes or important factors. Finally, a solution methodology is developed using the model to obtain the team composition and size with optimum software skill and domain expertise to resolve the tickets within the required time. Findings The implementation of the solution resulted in improving the process performance significantly. The process performance index increased from 0.00 to 1.2 and parts per million reduced from 501366.31 to 153. 33. Practical implications The software engineers can use the similar approach to improve the performance of core software activities such as coding, testing and bug fixing. The approach can also be used for improving the performance of other skill-based operations such as error reduction in medical diagnostics. Originality/value This is one of the rare Six Sigma case studies on improving skill-based processes such as software development. The study also demonstrates the usefulness of the Six Sigma methodology for solving dynamic problems whose solution needs to be continuously adjusted with the changes in the input or process conditions.

Errors in clinical laboratories or errors in laboratory medicine?

2006 ◽  
Vol 44 (6) ◽  
Author(s):  
Mario Plebani
Keyword(s):  
Laboratory Testing ◽  
Laboratory Medicine ◽  
Total Quality ◽  
Modern Approach ◽  
Laboratory Errors ◽  
Phase Errors ◽  
Clinical Laboratories ◽  
Laboratory Services ◽  
Analytical Phase ◽  
Total Testing Process

AbstractLaboratory testing is a highly complex process and, although laboratory services are relatively safe, they are not as safe as they could or should be. Clinical laboratories have long focused their attention on quality control methods and quality assessment programs dealing with analytical aspects of testing. However, a growing body of evidence accumulated in recent decades demonstrates that quality in clinical laboratories cannot be assured by merely focusing on purely analytical aspects. The more recent surveys on errors in laboratory medicine conclude that in the delivery of laboratory testing, mistakes occur more frequently before (pre-analytical) and after (post-analytical) the test has been performed. Most errors are due to pre-analytical factors (46–68.2% of total errors), while a high error rate (18.5–47% of total errors) has also been found in the post-analytical phase. Errors due to analytical problems have been significantly reduced over time, but there is evidence that, particularly for immunoassays, interference may have a serious impact on patients. A description of the most frequent and risky pre-, intra- and post-analytical errors and advice on practical steps for measuring and reducing the risk of errors is therefore given in the present paper. Many mistakes in the Total Testing Process are called “laboratory errors”, although these may be due to poor communication, action taken by others involved in the testing process (e.g., physicians, nurses and phlebotomists), or poorly designed processes, all of which are beyond the laboratory's control. Likewise, there is evidence that laboratory information is only partially utilized. A recent document from the International Organization for Standardization (ISO) recommends a new, broader definition of the term “laboratory error” and a classification of errors according to different criteria. In a modern approach to total quality, centered on patients' needs and satisfaction, the risk of errors and mistakes in pre- and post-examination steps must be minimized to guarantee the total quality of laboratory services.

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