Quality control review: implementing a scientifically based quality control system

Westgard, James O.; Westgard, Sten A.

doi:10.1177/0004563215597248

Cited by 77 publications

(52 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, the accuracy/combination of bias and imprecision/ total error of the measurement result represents properties of the measurement system. The total error estimated from singleton measurements of control samples has been found appropriate for regulatory purposes and an extensive theoretical and practical framework has been developed around its use (42, 43). According to a recent definition total analytical error (TAE) defines the interval that contains a specified proportion (usually 95% or 99%) of the distribution of analytical measurement differences between a measurement procedure operating in its stable incontrol state and a comparative measurement procedure that is either a definitive reference method or one that is traceable to one (43).…”

Section: The Analytical Phase Of the Total Testing Chainmentioning

confidence: 99%

Quality Assurance in Clinical Chemistry: A Touch of Statistics and A Lot of Common Sense

Theodorsson

2016

Journal of Medical Biochemistry

View full text Add to dashboard Cite

SummaryWorking in laboratories of clinical chemistry, we risk feeling that our personal contribution to quality is small and that statistical models and manufacturers play the major roles. It is seldom sufficiently acknowledged that personal knowledge, skills and common sense are crucial for quality assurance in the interest of patients. The employees, environment and procedures inherent to the laboratory including its interactions with the clients are crucial for the overall result of the total testing chain. As the measurement systems, reagents and procedures are gradually improved, work on the preanalytical, postanalytical and clinical phases is likely to pay the most substantial dividends in accomplishing further quality improvements. This means changing attitudes and behaviour, especially of the users of the laboratory. It requires understanding people and how to engage them in joint improvement processes. We need to use our knowledge and common sense expanded with new skills e.g. from the humanities, management, business and change sciences in order to bring this about together with the users of the laboratory.

show abstract

Section: The Analytical Phase Of the Total Testing Chainmentioning

confidence: 99%

Quality Assurance in Clinical Chemistry: A Touch of Statistics and A Lot of Common Sense

Theodorsson

2016

Journal of Medical Biochemistry

View full text Add to dashboard Cite

show abstract

“…Lately, efforts have been made to expand the TAE concept to the evaluation of results of patient samples, including all phases of the total testing process [2,8,9]. However, several additional sources of errors influence patient samples compared with control samples, e.g.…”

Section: Resultsmentioning

confidence: 99%

“…TAE methods are commonly intertwined with quality assurance, analytical performance specifications and Six Sigma methods [2,[6][7][8][9], which are only partially included in this paper [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

The use of error and uncertainty methods in the medical laboratory

Oosterhuis

Bayat²,

Armbruster

et al. 2017

Clinical Chemistry and Laboratory Medicine (CCLM)

View full text Add to dashboard Cite

Error methods -compared with uncertainty methods -offer simpler, more intuitive and practical procedures for calculating measurement uncertainty and conducting quality assurance in laboratory medicine. However, uncertainty methods are preferred in other fields of science as reflected by the guide to the expression of uncertainty in measurement. When laboratory results are used for supporting medical diagnoses, the total uncertainty consists only partially of analytical variation. Biological variation, pre-and postanalytical variation all need to be included. Furthermore, all components of the measuring procedure need to be taken into account. Performance specifications for diagnostic tests should include the diagnostic uncertainty of the entire testing process. Uncertainty methods may be particularly useful for this purpose but have yet to show their strength in laboratory medicine. The purpose of this paper is to elucidate the pros and cons of error and uncertainty methods as groundwork for future consensus on their use in practical performance specifications. Error and uncertainty methods are complementary when evaluating measurement data.

show abstract

“…With the integration of Six Sigma concepts and metrics, the traditional error framework for managing analytical quality has evolved into a Six Sigma Quality Management System (6σQMS) [7,15] that is compliant with ISO 15189 requirements [16]. As shown in Figure 3, the objective and quantitative management of analytical quality is achieved through several steps in the process: -Step 1 -definition of quality for intended use in the form of ATE or a clinical decision interval (Dint) -Step 3 -validation of examination procedures with use of a method decision chart [10]; -Step 5 -formulation of a total quality control strategy based on the observed sigma-metric [17]; -Step 6 -selection/design of statistical QC (SQC) procedures using a Sigma-metric SQC Selection Tool [14], Charts of Operating Specifications [18,19], or Westgard sigma rules [7]); -Step 7 -development of a Total QC Plan that optimizes controls for risk-based QC plans based on a Sigma control prioritization matrix [20]; -Step 10 -monitoring performance by determination of MU from SQC data and quality on the sigma-scale through PT/EQA programs with the aid of a Sigma Proficiency Assessment Chart [21].…”

Section: Six Sigma Quality Management System Modelmentioning

confidence: 99%

Useful measures and models for analytical quality management in medical laboratories

Westgard¹

2016

Clinical Chemistry and Laboratory Medicine (CCLM)

View full text Add to dashboard Cite

Abstract: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. This opinion recommends careful consideration of the differences in the concepts of accuracy and traceability and the appropriateness of different measures, particularly TAE as a measure of accuracy and MU as a measure of traceability. TAE is essential to manage quality within a medical laboratory and MU and trueness are essential to achieve comparability of results across laboratories. With this perspective, laboratory scientists can better understand the many measures and models needed for analytical quality management and assess their usefulness for practical applications in medical laboratories.

show abstract

Quality control review: implementing a scientifically based quality control system

Cited by 77 publications

References 40 publications

Quality Assurance in Clinical Chemistry: A Touch of Statistics and A Lot of Common Sense

Quality Assurance in Clinical Chemistry: A Touch of Statistics and A Lot of Common Sense

The use of error and uncertainty methods in the medical laboratory

Useful measures and models for analytical quality management in medical laboratories

Contact Info

Product

Resources

About