Basic definition of six sigma, why as it introduced in the first place, the mathematical expression, statistical definition, sig sigma application in clinical laboratory.
This document discusses the application of Six Sigma methodology in clinical laboratories to improve quality. It defines Six Sigma and explains how it was developed from earlier statistical quality control methods. Six Sigma uses a measurement scale from 1-6 sigma to assess process error levels, with 3 sigma considered the minimum acceptable level. The document then discusses how Six Sigma methodology, including DMAIC and DMADV, can be applied in clinical labs to reduce errors. It also provides an example of using Six Sigma metrics to evaluate error levels for various analytes tested on an automated chemistry analyzer.
This document discusses Total Quality Management in medical laboratories. It covers key aspects of quality management including defining quality, the core elements of a quality management system, and the seven common tools used in quality control. It also discusses requirements for laboratory accreditation such as establishing quality indicators, documentation, and quality assurance activities like proficiency testing and calibration. The overall goal of quality management in medical laboratories is to provide accurate and reliable test results to customers through an effective quality management system.
This document discusses quality control in laboratories. It defines key terms like quality assurance, quality assessment, total quality management, and continuous quality improvement. It describes factors that can affect quality like pre-analytical, analytical, and post-analytical variables. The importance of standard operating procedures, proficiency testing, and documenting quality control procedures is emphasized. Maintaining accurate and precise results through internal quality control using control charts and Westgard rules is also outlined.
A routine session on quality assurance practice in a medical laboratory to sensitize and provide basics to those interested in working in a medical testing laboratory.
Quality in clinical laboratory is a continuous journey of improving processes through team work, innovative solutions, regulatory compliance with final objective to meet the evolving needs of clinicians & patients.
This document discusses the application of Six Sigma methodology in clinical laboratories to improve quality. It defines Six Sigma and explains how it was developed from earlier statistical quality control methods. Six Sigma uses a measurement scale from 1-6 sigma to assess process error levels, with 3 sigma considered the minimum acceptable level. The document then discusses how Six Sigma methodology, including DMAIC and DMADV, can be applied in clinical labs to reduce errors. It also provides an example of using Six Sigma metrics to evaluate error levels for various analytes tested on an automated chemistry analyzer.
This document discusses Total Quality Management in medical laboratories. It covers key aspects of quality management including defining quality, the core elements of a quality management system, and the seven common tools used in quality control. It also discusses requirements for laboratory accreditation such as establishing quality indicators, documentation, and quality assurance activities like proficiency testing and calibration. The overall goal of quality management in medical laboratories is to provide accurate and reliable test results to customers through an effective quality management system.
This document discusses quality control in laboratories. It defines key terms like quality assurance, quality assessment, total quality management, and continuous quality improvement. It describes factors that can affect quality like pre-analytical, analytical, and post-analytical variables. The importance of standard operating procedures, proficiency testing, and documenting quality control procedures is emphasized. Maintaining accurate and precise results through internal quality control using control charts and Westgard rules is also outlined.
A routine session on quality assurance practice in a medical laboratory to sensitize and provide basics to those interested in working in a medical testing laboratory.
Quality in clinical laboratory is a continuous journey of improving processes through team work, innovative solutions, regulatory compliance with final objective to meet the evolving needs of clinicians & patients.
This document discusses laboratory errors, their causes, and ways to prevent them. It notes that errors can occur at any stage of the testing process, from specimen collection to reporting of results. The majority of errors are pre-analytical or post-analytical. Common causes of errors include inadequate staffing, poor quality control, time pressures, and lack of validation of tests. Errors are classified as latent due to organizational failures or infrastructure issues, or active including pre-analytical, analytical, and post-analytical errors. Preventing errors requires measures such as staff education, adherence to standards and procedures, quality monitoring, and effective communication across departments. Reducing errors is important for accurate diagnosis and treatment of patients
This is a series of notes on clinical pathology, useful for postgraduate students and practising pathologists. It covers all internal and external quality control techniques. The topics are presented point wise for easy reproduction.
Quality control, quality assurance, and quality assessment are important concepts for ensuring accuracy and reliability in medical laboratory testing. Quality control refers to internal processes like running controls to verify test accuracy during each run. Quality assurance encompasses the overall program to deliver correct results. Quality assessment challenges these programs through external proficiency testing. Proper documentation, trained personnel, validated methods and equipment, and monitoring control rules are key to achieving the goals of quality control, quality assurance and providing quality medical laboratory testing.
What is quality?
Importance of a quality management system in the laboratory
Quality system essential elements
The history of development of quality principles
Discuss relationship of this quality model to ISO and CLSI standards
Estimation of serum triglycerides by Dr. TehmasTehmas Ahmad
Estimation of Serum Triglycerides, Practical demonstration lecture for 2nd year MBBS students of Bannu Medical College, Bannu. Lecture delivered on 13/03/2018
Quality control (QC) is a procedure or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. QC is similar to, but not identical with, quality assurance (QA).
QC IN clinical biochemistry labs and hospitals
Laboratory Internal Quality Control presentation master revision, 2014Adel Elazab Elged
Short presentation about using internal quality control material in clinical laboratory to ensure analytical quality laboratory results for the sake of better patient care and minimizing errors in diagnosis, management, and follow up.
This document provides an overview of quality control in clinical biochemistry laboratories. It discusses that quality control aims to ensure test results are correct by minimizing errors. Errors can occur in the pre-analytical, analytical, and post-analytical phases. The pre-analytical phase, involving sample collection and handling, accounts for most errors. Laboratories use internal quality control methods like calibration, controls, and Levey-Jennings charts daily, as well as external quality assurance programs, to monitor performance and identify errors. Maintaining quality control is important for generating accurate, reliable test results.
Quality control, or QC for short, is a process by which entities review the quality of all factors involved in the production. ISO 9000 defines quality control as "A part of quality management focused on fulfilling quality requirements"
This presentation gives a brief idea of Quality control and how to execute it.
Quality control in the medical laboratoryAdnan Jaran
This document discusses quality control in medical laboratories. It emphasizes that quality is achieved through determining customer requirements, ensuring necessary resources are available, planning management procedures, training staff, undertaking tasks correctly, taking corrective action when errors occur, conducting regular reviews and audits, and total management commitment. The quality assurance cycle involves various steps from patient preparation to reporting. Achieving high quality requires addressing all aspects of the laboratory, including organization, personnel, equipment, purchasing, process control, information management, documents, occurrence management, assessment, process improvement, customer service, and facilities/safety. The goal is to detect and prevent errors through a quality management system.
Quality control in clinical biochemistryAshok Katta
This document discusses quality control in clinical biochemistry laboratories. It explains that laboratory tests play an important role in clinical diagnosis and treatment decisions. Therefore, test results must be reliable and accurate. Quality control involves measures to ensure test accuracy, including internal quality control procedures done daily in the lab and external quality assessment involving evaluation by an outside agency. Proper quality control is essential to producing test results that healthcare providers can trust in making decisions for patients.
This document discusses quality control, quality assurance, and quality assessment in medical laboratories. It defines key terms like quality control, quality assurance, and quality assessment. Quality control refers to analytical measurements used to assess data quality, while quality assurance is an overall management plan to ensure data integrity. Quality assessment determines the quality of results generated by evaluating internal and external quality programs. The document outlines quality assurance and quality control processes like standard operating procedures, equipment and reagent validation, personnel competency, and documentation. It also discusses error types, control chart interpretation, and Westgard rules for evaluating quality control results.
Analytical and post-analytical errors can occur in clinical chemistry laboratories. Analytical errors include issues like test systems not being calibrated properly, controls being out of range but results still reported, improper measurements or reagents, and instrument maintenance issues. This can lead to inaccuracies, imprecisions, insensitivities, and linearity problems. Post-analytical errors involve things like transcription mistakes in reporting results, reports going to the wrong location, illegible reports, or reports not being sent at all. Laboratories should develop systematic workflows, continuously monitor for errors, and strengthen defenses to minimize vulnerabilities and their impacts, which can include inadequate patient care, misdiagnosis, harm, or even death.
This document discusses laboratory errors and quality control in clinical testing. It describes three types of errors - pre-analytical, analytical, and post-analytical. Pre-analytical errors can occur before the sample reaches the lab due to improper patient preparation, collection, storage, or transport. Analytical errors occur during testing and can be due to issues with samples, equipment, reagents, or operator technique. Post-analytical errors involve improper result reporting. The document emphasizes the importance of quality control, calibration, and statistical analysis to monitor performance and identify errors. Quality control charts can reveal random errors or systematic shifts and trends.
This document discusses determinate (systematic) errors in laboratory analysis. It defines determinate errors as errors caused by faults in the analytical procedure or instruments used. These errors are systematic and cause results to be consistently too high or too low. Sources of determinate error include issues with reagents, instrumentation, analytical methods, contamination, and analyst errors. Methods for identifying and correcting for determinate errors are also presented.
Use of laboratory instruments and specimen processing equipment to perform clinical laboratory assays with only minimal involvement of technologist .
Automation in clinical laboratory is a process by which analytical instruments perform many tests with the least involvement of an analyst.
The International Union of Pure and Applied Chemistry (IUPAC) define automation as "The replacement of human manipulative effort and facilities in the performance of a given process by mechanical and instrumental devices that are regulated by feedback of information so that an apparatus is self-monitoring or self adjusting”.
1. A quality management system for a medical laboratory seeks to efficiently achieve the objectives of providing accurate test results to physicians and contributing to patient care.
2. Key aspects of a quality management system include personnel management, equipment management, process control, purchasing and inventory, and continuous improvement.
3. Implementing a quality management system can help detect and prevent errors, saving time, personnel costs, and improving patient outcomes compared to an error-prone laboratory.
Total Quality Management (TQM) by Dr Anurag YadavDr Anurag Yadav
Total quality management principles aim to improve patient care through monitoring laboratory work to detect deficiencies and correct them. Errors can occur in preanalytical, analytical, and postanalytical phases, and quality control procedures help control variables and ensure accuracy. Calibration, precision, accuracy, linearity, and detection limits are important analytical concepts, and factors like equipment, reagents, personnel, and documentation must be controlled and monitored to minimize errors and ensure quality.
This document provides an overview of Six Sigma, including:
- Six Sigma aims for near-perfect quality levels of 3.4 defects per million opportunities.
- It uses data-driven methods and statistical tools to measure, analyze, improve, and control processes.
- A key aspect is designating Belts (Green, Black, Master Black) to lead Six Sigma projects and drive process improvements.
- The goals are to reduce costs and defects while improving customer satisfaction through rigorous process analysis and control.
Six Sigma is a data-driven approach to process improvement that focuses on reducing defects. It aims for near perfect processes by minimizing variability. Six Sigma follows the DMAIC model - Define, Measure, Analyze, Improve, Control - to systematically improve processes. Belts are internal experts who assist teams, including Green Belts who spend part-time on projects and Black Belts who lead projects full-time, mentored by Master Black Belts. Six Sigma has helped many companies significantly improve performance and quality.
This document discusses laboratory errors, their causes, and ways to prevent them. It notes that errors can occur at any stage of the testing process, from specimen collection to reporting of results. The majority of errors are pre-analytical or post-analytical. Common causes of errors include inadequate staffing, poor quality control, time pressures, and lack of validation of tests. Errors are classified as latent due to organizational failures or infrastructure issues, or active including pre-analytical, analytical, and post-analytical errors. Preventing errors requires measures such as staff education, adherence to standards and procedures, quality monitoring, and effective communication across departments. Reducing errors is important for accurate diagnosis and treatment of patients
This is a series of notes on clinical pathology, useful for postgraduate students and practising pathologists. It covers all internal and external quality control techniques. The topics are presented point wise for easy reproduction.
Quality control, quality assurance, and quality assessment are important concepts for ensuring accuracy and reliability in medical laboratory testing. Quality control refers to internal processes like running controls to verify test accuracy during each run. Quality assurance encompasses the overall program to deliver correct results. Quality assessment challenges these programs through external proficiency testing. Proper documentation, trained personnel, validated methods and equipment, and monitoring control rules are key to achieving the goals of quality control, quality assurance and providing quality medical laboratory testing.
What is quality?
Importance of a quality management system in the laboratory
Quality system essential elements
The history of development of quality principles
Discuss relationship of this quality model to ISO and CLSI standards
Estimation of serum triglycerides by Dr. TehmasTehmas Ahmad
Estimation of Serum Triglycerides, Practical demonstration lecture for 2nd year MBBS students of Bannu Medical College, Bannu. Lecture delivered on 13/03/2018
Quality control (QC) is a procedure or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. QC is similar to, but not identical with, quality assurance (QA).
QC IN clinical biochemistry labs and hospitals
Laboratory Internal Quality Control presentation master revision, 2014Adel Elazab Elged
Short presentation about using internal quality control material in clinical laboratory to ensure analytical quality laboratory results for the sake of better patient care and minimizing errors in diagnosis, management, and follow up.
This document provides an overview of quality control in clinical biochemistry laboratories. It discusses that quality control aims to ensure test results are correct by minimizing errors. Errors can occur in the pre-analytical, analytical, and post-analytical phases. The pre-analytical phase, involving sample collection and handling, accounts for most errors. Laboratories use internal quality control methods like calibration, controls, and Levey-Jennings charts daily, as well as external quality assurance programs, to monitor performance and identify errors. Maintaining quality control is important for generating accurate, reliable test results.
Quality control, or QC for short, is a process by which entities review the quality of all factors involved in the production. ISO 9000 defines quality control as "A part of quality management focused on fulfilling quality requirements"
This presentation gives a brief idea of Quality control and how to execute it.
Quality control in the medical laboratoryAdnan Jaran
This document discusses quality control in medical laboratories. It emphasizes that quality is achieved through determining customer requirements, ensuring necessary resources are available, planning management procedures, training staff, undertaking tasks correctly, taking corrective action when errors occur, conducting regular reviews and audits, and total management commitment. The quality assurance cycle involves various steps from patient preparation to reporting. Achieving high quality requires addressing all aspects of the laboratory, including organization, personnel, equipment, purchasing, process control, information management, documents, occurrence management, assessment, process improvement, customer service, and facilities/safety. The goal is to detect and prevent errors through a quality management system.
Quality control in clinical biochemistryAshok Katta
This document discusses quality control in clinical biochemistry laboratories. It explains that laboratory tests play an important role in clinical diagnosis and treatment decisions. Therefore, test results must be reliable and accurate. Quality control involves measures to ensure test accuracy, including internal quality control procedures done daily in the lab and external quality assessment involving evaluation by an outside agency. Proper quality control is essential to producing test results that healthcare providers can trust in making decisions for patients.
This document discusses quality control, quality assurance, and quality assessment in medical laboratories. It defines key terms like quality control, quality assurance, and quality assessment. Quality control refers to analytical measurements used to assess data quality, while quality assurance is an overall management plan to ensure data integrity. Quality assessment determines the quality of results generated by evaluating internal and external quality programs. The document outlines quality assurance and quality control processes like standard operating procedures, equipment and reagent validation, personnel competency, and documentation. It also discusses error types, control chart interpretation, and Westgard rules for evaluating quality control results.
Analytical and post-analytical errors can occur in clinical chemistry laboratories. Analytical errors include issues like test systems not being calibrated properly, controls being out of range but results still reported, improper measurements or reagents, and instrument maintenance issues. This can lead to inaccuracies, imprecisions, insensitivities, and linearity problems. Post-analytical errors involve things like transcription mistakes in reporting results, reports going to the wrong location, illegible reports, or reports not being sent at all. Laboratories should develop systematic workflows, continuously monitor for errors, and strengthen defenses to minimize vulnerabilities and their impacts, which can include inadequate patient care, misdiagnosis, harm, or even death.
This document discusses laboratory errors and quality control in clinical testing. It describes three types of errors - pre-analytical, analytical, and post-analytical. Pre-analytical errors can occur before the sample reaches the lab due to improper patient preparation, collection, storage, or transport. Analytical errors occur during testing and can be due to issues with samples, equipment, reagents, or operator technique. Post-analytical errors involve improper result reporting. The document emphasizes the importance of quality control, calibration, and statistical analysis to monitor performance and identify errors. Quality control charts can reveal random errors or systematic shifts and trends.
This document discusses determinate (systematic) errors in laboratory analysis. It defines determinate errors as errors caused by faults in the analytical procedure or instruments used. These errors are systematic and cause results to be consistently too high or too low. Sources of determinate error include issues with reagents, instrumentation, analytical methods, contamination, and analyst errors. Methods for identifying and correcting for determinate errors are also presented.
Use of laboratory instruments and specimen processing equipment to perform clinical laboratory assays with only minimal involvement of technologist .
Automation in clinical laboratory is a process by which analytical instruments perform many tests with the least involvement of an analyst.
The International Union of Pure and Applied Chemistry (IUPAC) define automation as "The replacement of human manipulative effort and facilities in the performance of a given process by mechanical and instrumental devices that are regulated by feedback of information so that an apparatus is self-monitoring or self adjusting”.
1. A quality management system for a medical laboratory seeks to efficiently achieve the objectives of providing accurate test results to physicians and contributing to patient care.
2. Key aspects of a quality management system include personnel management, equipment management, process control, purchasing and inventory, and continuous improvement.
3. Implementing a quality management system can help detect and prevent errors, saving time, personnel costs, and improving patient outcomes compared to an error-prone laboratory.
Total Quality Management (TQM) by Dr Anurag YadavDr Anurag Yadav
Total quality management principles aim to improve patient care through monitoring laboratory work to detect deficiencies and correct them. Errors can occur in preanalytical, analytical, and postanalytical phases, and quality control procedures help control variables and ensure accuracy. Calibration, precision, accuracy, linearity, and detection limits are important analytical concepts, and factors like equipment, reagents, personnel, and documentation must be controlled and monitored to minimize errors and ensure quality.
This document provides an overview of Six Sigma, including:
- Six Sigma aims for near-perfect quality levels of 3.4 defects per million opportunities.
- It uses data-driven methods and statistical tools to measure, analyze, improve, and control processes.
- A key aspect is designating Belts (Green, Black, Master Black) to lead Six Sigma projects and drive process improvements.
- The goals are to reduce costs and defects while improving customer satisfaction through rigorous process analysis and control.
Six Sigma is a data-driven approach to process improvement that focuses on reducing defects. It aims for near perfect processes by minimizing variability. Six Sigma follows the DMAIC model - Define, Measure, Analyze, Improve, Control - to systematically improve processes. Belts are internal experts who assist teams, including Green Belts who spend part-time on projects and Black Belts who lead projects full-time, mentored by Master Black Belts. Six Sigma has helped many companies significantly improve performance and quality.
Six Sigma is a methodology that aims to improve processes and minimize defects. It uses a data-driven approach called DMAIC (Define, Measure, Analyze, Improve, Control) to identify and address sources of variation. The goal of Six Sigma is to achieve nearly perfect processes with fewer than 3.4 defects per million opportunities. It was developed by Motorola to improve quality and has since been widely adopted. Key aspects of Six Sigma include defining problems, measuring processes, analyzing sources of variation, improving processes, and controlling improvements.
Six Sigma is a methodology that aims to improve processes and minimize defects. It uses a data-driven approach called DMAIC (Define, Measure, Analyze, Improve, Control) to identify and address sources of variation. The goal of Six Sigma is to achieve nearly perfect processes with fewer than 3.4 defects per million opportunities. It was developed by Motorola to improve quality and has since been widely adopted. Key aspects of Six Sigma include defining problems, measuring processes, analyzing sources of variation, improving processes, and controlling improvements.
A term (Greek) used in statistics to represent standard deviation from mean value, an indicator of the degree of variation in a set of a process.
Sigma measures how far a given process deviates from perfection. Higher sigma capability, better performance
Six Sigma - A highly disciplined process that enables organizations deliver nearly perfect products and services.
The figure of six arrived statistically from current average maturity of most business enterprises
A philosophy and a goal: as perfect as practically possible.
A methodology and a symbol of quality
What is Six Sigma?,Methodology of Six sigma, What Six Sigma involve? , Why to adopt Six sigma?, Six sigma levels, Advantages of Six Sigma?, Disadvantages of Six Sigma?, Six Sigma Companies
Six Sigma is a data-driven methodology for improving processes by reducing variability and minimizing defects. It aims for near perfection by targeting no more than 3.4 defects per million opportunities. The Six Sigma methodology includes DMAIC (Define, Measure, Analyze, Improve, Control) for improving existing processes and DMADV (Define, Measure, Analyze, Design, Verify) for developing new processes. Key roles include Champions, Master Black Belts, Black Belts and Green Belts who lead Six Sigma projects and use statistical tools to drive process improvement. Implementing Six Sigma helps companies better meet customer expectations, accelerate improvement rates, and enhance business performance and value.
Six Sigma (Quality Management System)
Six Sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects.
Six sigma process is one in which 99.9999966% of the products manufactured are statistically expected to be free of defects.
Six sigma is a very clever way of branding and packaging many aspects of TOTAL QUALITY MANAGEMENT.
This document discusses how Lean Six Sigma can be applied in healthcare settings to improve processes and outcomes. It provides examples of how Six Sigma approaches could be used to measure and improve patient satisfaction, reduce waiting times, and streamline triage processes in emergency rooms. The goals would be to continuously monitor performance, identify issues when metrics fall outside control limits, find root causes of problems, implement solutions, and re-evaluate outcomes. Six Sigma structures could help healthcare organizations make data-driven decisions, monitor project progress, and integrate process improvements.
Six Sigma is a methodology that aims for near perfection in meeting customer requirements. It was developed by Motorola in the late 1970s and focuses on reducing costs and waste through statistical analysis and process improvement. The main steps of Six Sigma are Define, Measure, Analyze, Improve, and Control (DMAIC). Benefits include a focus on customer expectations, strong participation across the organization, and a change in culture to prioritize profit through process improvement. Leading companies continuously integrate new and older quality methods to drive business success through close understanding of customers.
Lean Six Sigma is a methodology that combines Lean and Six Sigma to improve processes and eliminate waste. It uses tools like DMAIC (Define, Measure, Analyze, Improve, Control) and 5S (Sort, Straighten, Shine, Standardize, Sustain) to systematically identify and remove causes of defects and minimize variability in processes. A case study on implementing Lean Six Sigma in a call center showed improvements like increasing the actualization rate from 2.6% to 20% and updating procedures to better address customer issues. The future plan is to optimize billing processes and further improve call center quality and efficiency.
Six Sigma is a methodology that aims to reduce defects and variation in processes. It uses a data-driven, five-phase approach called DMAIC (Define, Measure, Analyze, Improve, Control) to optimize processes. Six Sigma defines quality as 3.4 defects per million opportunities. It uses statistical tools and aims for near-zero defect rates through the elimination of defects from processes. Projects are led by Belts (Black, Green, etc.) who are trained in Six Sigma tools and methods.
Lean Six Sigma in healthcare management.pptdrparul6375
Lean Six Sigma is a methodology aimed at improving the efficiency and quality of processes within an organization. It combines the principles of Lean manufacturing, which focuses on reducing waste and increasing efficiency, with Six Sigma, which emphasizes minimizing defects and variations in processes.
This document provides an introduction to Lean Six Sigma (6σ) in higher education. It discusses the history of quality initiatives in higher education and defines Six Sigma and Lean methodologies. Key aspects covered include the DMAIC process for Six Sigma, tools used in the analyze phase like ANOVA and design of experiments, and the seven types of waste targeted by Lean. Examples of Six Sigma and Lean Sigma being applied at universities are also presented.
This document provides an introduction to Lean Six Sigma (6σ) in higher education. It discusses the history of quality initiatives in higher education and defines Six Sigma and Lean methodologies. Key aspects covered include the DMAIC process for Six Sigma and the seven types of waste targeted by Lean. Examples are given of implementing statistical quality control and Lean Six Sigma at universities.
This document provides an introduction to Lean Six Sigma (6σ) in higher education. It discusses the history of quality initiatives in higher education and defines Six Sigma and Lean methodologies. Six Sigma aims to reduce process variability and defects, achieving 99.99966% quality. Lean seeks to eliminate waste and non-value added activities to increase process speed. The document explains tools and methods used in Six Sigma's DMAIC process and gives examples of universities implementing these approaches.
A business methodology for quality improvement that measures how many defects there are in a current process and seeks to systematically eliminate
The key sigma principles are the following:
Customer focus
Use data
Improve continuously
Involve people
Be thorough
Six Sigma is a set of techniques and strategies aimed at process improvement. It uses data and statistical analysis to identify and eliminate defects in manufacturing and business processes. The goal of Six Sigma is to achieve close to zero defects by reducing variation and errors. It aims for no more than 3.4 defects per million opportunities. Six Sigma provides a rigorous methodology for defining, measuring, analyzing, improving, and controlling process performance to drive customer satisfaction and increase profits.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
3. History
■ “Six Sigma” coined- Motorola engineer named Bill Smith.
(Incidentally, “Six Sigma” is a federally registered trademark of
Motorola).
■ In the early and mid-1980s with Chairman Bob Galvin at the helm,
Motorola engineers decided that the traditional quality levels —
measuring defects in thousands of opportunities – didn’t provide
enough granularity. Instead, they wanted to measure the defects per
million opportunities.
■ documented more than $16 Billion in savings as a result of our Six
Sigma efforts
4.
5. Measures of Central Tendency
■ Central tendency is the tendency of data to be around this
mean.
■ Mean is the arithmetic average of a process data set.
■ Standard Deviation (also known as Sigma or σ) determines
the spread around this mean/central tendency
6. Standard deviations
■ The more number of standard deviations between process average
and acceptable process limits fits, the less likely that the process
performs beyond the acceptable process limits, and it causes a
defect.
■ This is the reason why a 6σ (Six Sigma) process performs better
than 1σ, 2σ, 3σ, 4σ, 5σ processes.
7.
8. •
Six Sigma stands for 6 standard deviations (6σ)
between average and acceptable limits
• LSL and USL stand for “Lower Specification Limit” and
“Upper Specification Limit” respectively.
• Specification Limits are derived from the customer
they specify the minimum and maximum acceptable
9. Example
■ For instance in a car manufacturing system
the desired average length (Mean length)
of car door can be 1.37185 meter. In order
to smoothly assemble the door into the car,
LSL can be 1.37179 meter, and USL can
be 1.37191 meter. To reach a 6σ quality
level in such a process, the standard
deviation of car door length must be at
most 0.00001 meter around the mean
length
10.
11. SIX SIGMA
■ “Six Sigma is a quality program that, when all is said and done,
improves your customer’s experience, lowers your costs, and builds
better leaders”.
■ Six Sigma at many organizations simply means - measure of quality
that strives for near perfection
■ Generic or customized name for the organization like “Operational
Excellence,” “Zero Defects,” or “Customer Perfection.”
■ Six Sigma is a disciplined, data-driven approach and methodology for
eliminating defects (driving toward six standard deviations between the
mean and the nearest specification limit) in any process – from
manufacturing to transactional and from product to service.
12. Definition – statistical (industrial aspect)
■ The statistical representation of Six Sigma describes quantitatively
how a process is performing.
■ To achieve Six Sigma — statistically — a process must not produce
more than 3.4 defects per million opportunities.
■ A Six Sigma defect is defined as anything outside of customer
specifications.
13.
14. OBJECTIVES OF SIX SIGMA
■ to reduce process output variation so that on a long
term basis, this will result in no more than (or 3.4 defects
per million opportunities – DPMO).
■ Sigma is also the capability of the process to produce
defect free work. Higher the capability, lower the
defects
■ Better sigma metrics compared to 1σ, 2 σ, 3 σ, 4 σ.etc
17. Can we have any process which has 6σ
level of performance?
■
The answer is yes
■ Pharmaceutical Companies, Airline Manufacturing
Organizations, Automobile Manufacturers, among others are
bound to work at a sigma level which is either 6σ or more
than that. If they are not able to perform at this efficiency,
the organization cannot exist. Think about it, you are in the
air, 5000 feet above the ground, flying in a Boeing 777
Aircraft and suddenly a nut-bolt in the wing of the plane
loosens (probably due to manufacturing defect) making it
difficult for the pilot to steer the flight! This is the only
reason why defects are not welcome and organizations try to
achieve higher Sigma levels.
19. ■ Six Sigma methodology to use - help laboratories in
establishing better quality management systems.
Specifically, Six Sigma can help clinical lab managers better
evaluate the analytical quality of lab results as well as
the equipment and products used to produce them.
■ Employing the Six Sigma metric – 3.4 errors per million
opportunities – can help labs better determine if “bias,
imprecision, or both” have contributed to lower
Sigma metrics for equipment currently used in a lab
20. Six Sigma can be used to help answer one of the
most commonly asked questions in laboratory
quality control. How often should I run QC?
■ Six Sigma model allows laboratories to evaluate the effectiveness of their
current QC processes.
■ common use is to help implement a risk-based approach to QC, where an
optimum QC frequency and multi-rule procedure can be based on the sigma
score of the test in question.
■ The performance of tests or methods with a high sigma score of six or more
may be evaluated with one QC run (of each level) and a single 1:3s warning
rule. On the other hand, tests or methods with a lower sigma score should be
evaluated more frequently with multiple levels of QC and a multi-rule strategy
designed to increase identification of errors and reduce false rejections.
21. Table shows how multi-rules and QC
frequency can be applied according to
Sigma Metrics:
22. Underutilized Methodology – In Clinical Laboratory
■ Six Sigma metric that tests variance and errors in a process
■ Mostly underutilized due to lack of knowledge or lack of information
or both
Six Sigma Has Made Its Way Into Lab Work Before
■ Countries like Utah, Oklahoma and Illinois – adapted this metric
system for better process efficiency.
23. Application of SixSigma in Clinical Lab
• Reduce errors in the pre-analytical, analytical, and
post-analytical phases of testing
• Improve safety
• Reduce turnaround times (TATs)
• Improve accuracy of analytical tests
• Optimize quality control (QC) rule
24.
25. How to implement Six Sigma
■ Critical to involve all levels of staff in the process to produce better
results
■ Lean six sigma methodologies improve clinical laboratory efficiency
and reduce turnaround times.
Keys to Six Sigma success in the lab include:
• A leader who is knowledgeable in Six Sigma concepts
• Proper communication with lab staff throughout the process
• Proper training for lab staff
• Change management strategies
• A shift in workplace culture
28. ■ Define who the customer is and what their needs and
expectations are. In the case of clinical labs,
patients or the health care professionals who rely on
■ Measure the process you’re looking to improve. First
you need to create a plan to collect data, then collect
figure out what errors/defects are occurring and the
measure them against.
29. ■ Analyze your collected data to identify the key causes of
errors in the lab and areas that could be improved.
■ Improve the lab process you’re focusing on through
solutions/plans your team has come up with. This could
automating a process to reduce human errors and prevent
injuries.
■ Control the new and improved process to ensure the changes
stick. Make sure staff aren’t going back to the old methods of
things. Develop, document, and implement a monitoring plan
the new process is maintained. This might mean ongoing
staff to refresh them in the new methods.
31. Measure the Quality on the Sigma
Scale
■ The first one: counting the errors or defects.
■ This methodology is useful in evaluation of all errors in
total
testing process, except analytical phase.
Sigma Calculator
32.
33. ■ The second: using the following equation.
■ Sigma = (TEa – bias) / CV
Where:
■ TEa: total error allowable (quality goal).
■ bias and CV (coefficient of variation) are the indicator of
systematic and random errors respectively.
Measure the Quality on the Sigma
Scale
38. 1- North Shore University Hospital, New
York
■ Define: Total Turnaround Time (TAT) taking too long
■ Measure: Target TAT set to 120 min. and upper
specification limit set to 150min., defect defined as a TAT
over 150 min., collected information on 195 patients
■ Analyze: Use data to identify underlying problem
- the underling problem was employees lacked
proficiency with the hospitals bed tracking system
(BTS)
39. ■ Improve: Improved communication within the staff by:
documenting communication and Retraining employees
on BTS.
■ Control: Monitoring the process (TAT continued to be
monitored on a monthly basis)
Results
■ – Went from a slightly over one sigma process to a
3.1 sigma process
■ – The average TAT went from 226 minutes to 69
minutes
40. Conclusion
■ Six Sigma is not only a means to quantify
analytical performance, but is also a management
method that can improve the organization in an
orderly way,
■ with the target being to reach optimal quality
characterized by a level of 3.4 DPMO
41. Conclusion
■ The errors that we are interest are primarily analytical
errors, which represent only the tip of the iceberg.
However, the reality is quite different.
■ When we see the whole iceberg and control it all, then it
will be possible to reach Six Sigma level and even
higher quality in clinical laboratories.
42.
43. If we don’t measure, we
don’t know, and if we don’t
know , we can’t manage