However, despite its potential and efforts, MA QC is still struggling to meet these expectations.
The potential of MA QC as a valuable QC instrument has been shown and experts consider MA QC to be a valuable tool to support the analytical quality assurance of at least a selection of the standard biomarkers available in medical laboratories. internal (statistical) QC, confirmation and authorization procedures, etc., MA is one of the tools currently available to medical laboratories for QC purposes. Supported by these studies, the average of normals method has evolved into a more general MA approach, which is not necessarily based on “normals” and mean calculations.
#Glims meaning how to
Since then, studies on this average of normals concept have resulted in (i) alternative methods and new algorithms to calculate average values, (ii) deeper understanding of moving average (MA) error detection and its characteristics and (iii) guidance on how to obtain optimal MA settings. They proposed a method that averages the results obtained within (more or less) the reference range and plotted these in a control chart. Moving average quality control (MA QC) was first described as “average of normals” by Hoffmann and Waid in 1965 as an analytical quality control (QC) instrument. 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.
#Glims meaning upgrade
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. Recently developed simulation methods provide realistic error detecting properties for MA QC and are available for laboratories. Each of the MA QC optimization methods currently available has their own advantages and disadvantages. 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. Although a potentially valuable tool, it is struggling to meet expectations due to its complexity and need for evidence-based guidance. Moving average quality control (MA QC) was described decades ago as an analytical quality control instrument.
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