September 26, 2009
All time downloads
Description of MultiQC 18.104.22.168
Quality control tool in clinical chemistry laboratories
MultiQC is a Windows software application for quality control in clinical chemistry laboratories. It was created by a chemical pathologist disappointed with the heterogeneity, the inappropriate design and the poor efficiency of the ancillary QC programmes supplied with analyzers or LIS. The daily investigation of issues arising at the workbench combined with the great improvements in industrial QC for the last three decades led to MultiQC, a programme which provides technicians with a comprehensive control panel to monitor and improve the quality of analytical processes.
QC in clinical chemistry is aimed at keeping analytical uncertainty within medical tolerance at the lowest cost to achieve the most profitable zero-defect laboratory production. The difficulty of the task is reliant on the relative extents of tolerance and uncertainty intervals. The capability indice is the ratio of the former to the latter. It decides on the best way to perform QC :
- Low capability methods : Quality control is essential to keep them in-control.
- High capability methods : They may perform out-of-control and produce however acceptable results. An acceptance chart (pdf file 130 KB) should be prefered because it saves time and cuts costs in comparison to control charts.
- Incapable methods : They must be improved or discarded.
In addition to Shewhart (Levey and Jennings) charts, MultiQC implements more recent statistical tools:
- Exponentially weighted moving average (EWMA) to monitor the bias
- Exponentially weighted moving variance (EWMV) to monitor the imprecision
- Multivariate process control (Hotelling's T2) to monitor multi-level quality control.
- Calibration charts, a new approach to the analysis of QC data.
Add your review
Top popularity in Science
Erdas Imagine 9.3
Analyze imagery to increase the value of your geospatial information
Study, sizing, simulation of complete PV systems
Tool for phase identification from powder diffraction data