Optimization and validation of moving average quality control procedures using bias detection curves and moving average validation charts

Rossum, Huub H. van; Kemperman, Hans

doi:10.1515/cclm-2016-0270

Cited by 43 publications

(47 citation statements)

References 13 publications

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“…2 These methods are based on running realistic error detection simulations that reflect true laboratory PBRTQC performance. 2,[17][18][19] They require a manageable number of PBRTQC alarms and therefore use much stricter false-alarm criteria than iQC, which is essential for practical application of PBRTQC. A major breakthrough is that one of these methods has been made available online in the MA Generator application, so that laboratories can readily use the advanced simulation techniques to set up and validate their laboratory-specific PBRTQC procedures.…”

Section: Patient-based Real-time Quality Controlmentioning

confidence: 99%

When internal quality control is insufficient or inefficient: Consider patient-based real-time quality control!

Rossum

2020

Ann Clin Biochem

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Section: Patient-based Real-time Quality Controlmentioning

confidence: 99%

When internal quality control is insufficient or inefficient: Consider patient-based real-time quality control!

Rossum

2020

Ann Clin Biochem

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“…However, most often and generally supported by MA management software packages, these criteria are based on the exclusion of extreme or not-normal assay results. The limits used for this purpose are also referred to as truncation limits [2,6]; however, this approach has some limitations. For example, the exclusion of too many results from the MA calculations implies that an MA value is less frequently calculated and that MA alarming might be delayed.…”

Section: Inclusion Criteria and Truncation Limitsmentioning

confidence: 99%

“…For example, the exclusion of too many results from the MA calculations implies that an MA value is less frequently calculated and that MA alarming might be delayed. Also, when truncation limits are used, extreme systematic errors might become undetectable due to the exclusion of all generated systematic errors containing assay results [6]; this is illustrated in the bias detection curves presented in Figure 1B, C and D. To select optimal inclusion criteria, these effects need to be taken into consideration.…”

Section: Inclusion Criteria and Truncation Limitsmentioning

confidence: 99%

Moving average quality control: principles, practical application and future perspectives

Rossum

2018

Clinical Chemistry and Laboratory Medicine (CCLM)

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Moving average quality control (MA QC) was described decades ago as an analytical quality control instrument. Although a potentially valuable tool, it is struggling to meet expectations due to its complexity and need for evidence-based guidance. For this review, relevant literature and the world wide web were examined in order to (i) explain the basic concepts and current understanding of MA QC, (ii) discuss moving average (MA) optimization methods, (iii) gain insight into practical aspects related to applying MA in daily practice and (iv) describe future prospects to enable more widespread acceptance and application of MA QC. Each of the MA QC optimization methods currently available has their own advantages and disadvantages. Recently developed simulation methods provide realistic error detecting properties for MA QC and are available for laboratories. Operational MA management issues have been identified that allow developers of MA software to upgrade their packages to support optimal MA QC application and guide laboratories on MA management issues, such as MA alarm workup. The new insights into MA QC characteristics and operational issues, together with supporting online tools, may promote more widespread acceptance and application of MA QC.

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“…Currently, special attention is paid to solving issues in the management decision-making system [1], to introducing and developing TQM techniques in industrial enterprises [2], to carrying out new optimization techniques and implementing quality control procedures in the quality management systems (QMS) of organizations [3] and industrial enterprises [4,5,6] of various industries, including: construction [7,8], medicine [9,10], oil refining and gas industry [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Integrating the Innovation Management Methodology Into the Existing Quality Management System of a Machine-Building Enterprise

2019

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The problem of integrating European standards in the field of innovation management (SIM) into the existing quality management systems (QMS) of organizations is considered. The authors analyze the most relevant developments of the methodological foundations of innovation management, as a promising direction of scientific knowledge, and research aimed at summarizing the practical experience of its implementation in various sectors of the economy and the service sector. Particular attention is paid to analyzing scientific publications in the field of industrial innovation. The main directions of integrating innovation management methodologies into the systems of quality management of products and services operating at enterprises and organizations are proposed. The conceptual model of the innovation management system is presented, which contains its key elements. It is shown that for industrial enterprises and manufacturing firms it is inappropriate to have two different systems (QMS and SIM), which tend to the same goal namely to improve the product quality. A process model of innovation management that implements the principle of quality management called “Improvement” is proposed, and the description of the model is given. The specificity of implementing QMS and SIM integration processes at Russian enterprises of the machine-building complex is revealed.

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Optimization and validation of moving average quality control procedures using bias detection curves and moving average validation charts

Abstract: Bias detection curves and MA validation charts are useful tools for optimization and validation of MA procedures.

Cited by 43 publications

References 13 publications

When internal quality control is insufficient or inefficient: Consider patient-based real-time quality control!

When internal quality control is insufficient or inefficient: Consider patient-based real-time quality control!

Moving average quality control: principles, practical application and future perspectives

Integrating the Innovation Management Methodology Into the Existing Quality Management System of a Machine-Building Enterprise

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