The value of Sigma-metrics in laboratory medicine.

While Six Sigma is used in different disciplines to improve quality, Tony Badric and Elvar Theodorsson in a recent paper in CCLM have questioned Six Sigma application in medical laboratory concluding Six Sigma has provided no value to medical laboratory. In addition, the authors have expanded their criticism to Total Analytical Error (TAE) model and statistical quality control. To address their arguments, we have explained the basics of TAE model and Six Sigma and have shown the value of Six Sigma to medical laboratory.

Multirule procedures vs moving average algorithms for IQC: An appropriate comparison reveals how best to combine their strengths.

Background: Moving Average Algorithms (MAA) have been widely recommended for use in Patient Based Real Time Quality Control applications (PBRTQC) to supplement or replace traditional Internal Quality Control (IQC) techniques. A recent "proof of concept" study recommends applying MAAs to IQC data to replace traditional IQC procedures because they "outperform Westgard Rules," which is a current standard of practice for IQC.

Methods: We generated power curves for multi-rule procedures with 2 and 4 control measurements per QC event, as well as a Simple Moving Average having block sizes of 5, 10, and 20 control measurements.

A multi-test planning model for risk based statistical quality control strategies.

Background: Efforts to improve QC for multi-test analytic systems should focus on risk-based bracketed SQC strategies, as recommended in the CLSI C24-Ed4 guidance for QC practices. The objective is to limit patient risk by controlling the expected number of erroneous patient test results that would be reported over the period an error condition goes undetected.

Methods: A planning model is described to provide a structured process for considering critical variables for the development of SQC strategies for continuous production multi-test analytic systems.

Planning SQC strategies and adapting QC frequency for patient risk.

Background: Risk-based Statistical QC strategies are recommended by the CLSI guidance for Statistical Quality Control (C24-Ed4). Using Parvin's patient risk model, QC frequency can be determined in terms of run size, i.e.

POCT Analysts' Perspective: Practices and Wants for Improvement.

Background: Point-of-care testing (POCT) continues to expand worldwide. Concerns remain about result quality despite guidelines and standards that specify testing practices. To better understand POCT testing worldwide, we polled analysts to obtain their views on actual practices and needs for improvement.

Establishing Evidence-Based Statistical Quality Control Practices.

Objectives: To establish an objective, scientific, evidence-based process for planning statistical quality control (SQC) procedures based on quality required for a test, precision and bias observed for a measurement procedure, probabilities of error detection and false rejection for different control rules and numbers of control measurements, and frequency of QC events (or run size) to minimize patient risk.

Methods: A Sigma-Metric Run Size Nomogram and Power Function Graphs have been used to guide the selection of control rules, numbers of control measurements, and frequency of QC events (or patient run size).

Results: A tabular summary is provided by a Sigma-Metric Run Size Matrix, with a graphical summary of Westgard Sigma Rules with Run Sizes.

Planning Risk-Based SQC Schedules for Bracketed Operation of Continuous Production Analyzers.

Background: To minimize patient risk, "bracketed" statistical quality control (SQC) is recommended in the new CLSI guidelines for SQC (C24-Ed4). Bracketed SQC requires that a QC event both precedes and follows (brackets) a group of patient samples. In optimizing a QC schedule, the frequency of QC or run size becomes an important planning consideration to maintain quality and also facilitate responsive reporting of results from continuous operation of high production analytic systems.

Selecting a Risk-Based SQC Procedure for a HbA1c Total QC Plan.

Background: Recent US practice guidelines and laboratory regulations for quality control (QC) emphasize the development of QC plans and the application of risk management principles. The US Clinical Laboratory Improvement Amendments (CLIA) now includes an option to comply with QC regulations by developing an individualized QC plan (IQCP) based on a risk assessment of the total testing process. The Clinical and Laboratory Standards Institute (CLSI) has provided new practice guidelines for application of risk management to QC plans and statistical QC (SQC).

Planning Risk-Based Statistical Quality Control Strategies: Graphical Tools to Support the New Clinical and Laboratory Standards Institute C24-Ed4 Guidance.

Background: Clinical and Laboratory Standards Institute (CLSI)'s new guideline for statistical quality control (SQC; C24-Ed4) (CLSI C24-Ed4, 2016; Parvin CA, 2017) recommends the implementation of risk-based SQC strategies. Important changes from earlier editions include alignment of principles and concepts with the general patient risk model in CLSI EP23A (CLSI EP23A, 2011) and a recommendation for optimizing the frequency of SQC (number of patients included in a run, or run size) on the basis of the expected number of unreliable final patient results. The guideline outlines a planning process for risk-based SQC strategies and describes 2 applications for examination procedures that provide 9-σ and 4-σ quality.

Six Sigma Quality Management System and Design of Risk-based Statistical Quality Control.

Six sigma concepts provide a quality management system (QMS) with many useful tools for managing quality in medical laboratories. This Six Sigma QMS is driven by the quality required for the intended use of a test. The most useful form for this quality requirement is the allowable total error.

Rhetoric Versus Reality? Laboratory Surveys Show Actual Practice Differs Considerably from Proposed Models and Mandated Calculations.

The scientific debate on goals, measurement uncertainty, and individualized quality control plans has diverged significantly from the reality of laboratory operation. Academic articles promoting certain approaches are being ignored; laboratories may be in compliance with new regulations, mandates, and calculations, but most of them still adhere to traditional quality management practices. Despite a considerable effort to enforce measurement uncertainty and eliminate or discredit allowable total error, laboratories continue to use these older, more practical approaches for quality management.

Measuring Analytical Quality: Total Analytical Error Versus Measurement Uncertainty.

To characterize analytical quality of a laboratory test, common practice is to estimate Total Analytical Error (TAE) which includes both imprecision and trueness (bias). The metrologic approach is to determine Measurement Uncertainty (MU), which assumes bias can be eliminated, corrected, or ignored. Resolving the differences in these concepts and approaches is currently a global issue.

Utilizing global data to estimate analytical performance on the Sigma scale: A global comparative analysis of methods, instruments, and manufacturers through external quality assurance and proficiency testing programs.

Objective: To assess the analytical performance of instruments and methods through external quality assessment and proficiency testing data on the Sigma scale.

Design And Methods: A representative report from five different EQA/PT programs around the world (2 US, 1 Canadian, 1 UK, and 1 Australasian) was accessed. The instrument group standard deviations were used as surrogate estimates of instrument imprecision.

Quality control review: implementing a scientifically based quality control system.

This review focuses on statistical quality control in the context of a quality management system. It describes the use of a 'Sigma-metric' for validating the performance of a new examination procedure, developing a total quality control strategy, selecting a statistical quality control procedure and monitoring ongoing quality on the sigma scale. Acceptable method performance is a prerequisite to the design and implementation of statistical quality control procedures.

Assessing quality on the Sigma scale from proficiency testing and external quality assessment surveys.

Background: There is a need to assess the quality being achieved for laboratory examinations that are being utilized to support evidence-based clinical guidelines. Application of Six Sigma concepts and metrics can provide an objective assessment of the current analytical quality of different examination procedures.

Methods: A "Sigma Proficiency Assessment Chart" can be constructed for data obtained from proficiency testing and external quality assessment surveys to evaluate the observed imprecision and bias of method subgroups and determine quality on the Sigma scale.

The quality of laboratory testing today: an assessment of sigma metrics for analytic quality using performance data from proficiency testing surveys and the CLIA criteria for acceptable performance.

To assess the analytic quality of laboratory testing in the United States, we obtained proficiency testing survey results from several national programs that comply with Clinical Laboratory Improvement Amendments (CLIA) regulations. We studied regulated tests (cholesterol, glucose, calcium, fibrinogen, and prothrombin time) and nonregulated tests (international normalized ratio [INR], glycohemoglobin, and prostate-specific antigen [PSA]). Quality was assessed on the sigma scale with a benchmark for minimum process performance of 3 sigma and a goal for world-class quality of 6 sigma.