Internal quality control - past, present and future trends.

Objectives: This paper offers an historical view, through a summary of the internal quality control (IQC) models used from second half of twentyth century to those performed today and wants to give a projection on how the future should be addressed.

Methods: The material used in this work study are all papers collected referring IQC procedures. The method used is the critical analysis of the different IQC models with a discussion on the weak and the strong points of each model.

Intelligent Quality Management 2 with IntraSpect™ technology for quality control of GEM® Premier™ 5000 blood gas analyzers- A novel application of the patient sample as its own control.

Background: Quality control (QC) in point-of-care (POC) testing has been greatly improved by automatic control processes, such as the Intelligent Quality Management (iQM®) technology found in GEM Premier blood gas analyzers (Werfen, Bedford, MA). The 2nd generation technology, iQM2, provides additional capabilities, notably the incorporation of IntraSpect software that monitors the response curves of individual tests to detect transient errors caused by micro-clots, micro-bubbles or any event that disturbs the sensor response during sample data acquisition. IntraSpect is a novel form of patient-based, real-time quality control (PBRTQC).

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.

Mistaken assumptions drive new Six Sigma model off the road.

Oosterhuis and Coskun recently proposed a new model for applying the Six Sigma concept to laboratory measurement processes. In criticizing the conventional Six Sigma model, the authors misinterpret the industrial basis for Six Sigma and mixup the Six Sigma "counting methodology" with the "variation methodology", thus many later attributions, conclusions, and recommendations are also mistaken. Although the authors attempt to justify the new model based on industrial principles, they ignore the fundamental relationship between Six Sigma and the process capability indices.

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.

Analytical Sigma metrics: A review of Six Sigma implementation tools for medical laboratories.

Sigma metrics have become a useful tool for all parts of the quality control (QC) design process. Through the allowable total error model of laboratory testing, analytical assay performance can be judged on the Six Sigma scale. This not only allows benchmarking the performance of methods and instruments on a universal scale, it allows laboratories to easily visualize performance, optimize the QC rules and numbers of control measurements they implement, and now even schedule the frequency of running those controls.

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.

A Total Quality-Control Plan with Right-Sized Statistical Quality-Control.

A new Clinical Laboratory Improvement Amendments option for risk-based quality-control (QC) plans became effective in January, 2016. Called an Individualized QC Plan, this option requires the laboratory to perform a risk assessment, develop a QC plan, and implement a QC program to monitor ongoing performance of the QC plan. Difficulties in performing a risk assessment may limit validity of an Individualized QC Plan.

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.

Useful measures and models for analytical quality management in medical laboratories.

The 2014 Milan Conference "Defining analytical performance goals 15 years after the Stockholm Conference" initiated a new discussion of issues concerning goals for precision, trueness or bias, total analytical error (TAE), and measurement uncertainty (MU). Goal-setting models are critical for analytical quality management, along with error models, quality-assessment models, quality-planning models, as well as comprehensive models for quality management systems. There are also critical underlying issues, such as an emphasis on MU to the possible exclusion of TAE and a corresponding preference for separate precision and bias goals instead of a combined total error goal.

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.

Quality management in European screening laboratories in blood establishments: A view of current approaches and trends.

The screening laboratory has a critical role in the post-transfusion safety. The success of its targets and efficiency depends on the management system used. Even though the European Union directive 2002/98/EC requires a quality management system in blood establishments, its requirements for screening laboratories are generic.

The role of uncertainty regarding the results of screening immunoassays in blood establishments.

The risk of uncertain results in infectious agents' tests is recognized in blood establishments, being particularly evident during the blood donor selection. The current risk-based approaches require risk assessment and "risk-based thinking". Accordingly, the blood establishment should consider the effect of uncertainty in all the technical decisions taken in a screening laboratory.

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.

Scheme for the selection of measurement uncertainty models in blood establishments' screening immunoassays.

Blood establishments routinely perform screening immunoassays to assess safety of the blood components. As with any other screening test, results have an inherent uncertainty. In blood establishments the major concern is the chance of false negatives, due to its possible impact on patients' health.