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Idea detail description
Being able to remove specific outliers (QC results) fromt he calculation of MoU stats would be really beneficial and save the customer time and manual manipulation to identify and remove these to cacluate a more accurate MoU. QCs that fall out of the normal range due to perfromeance issues is ok to include. However in some situations QC results get accepted before a proper analysis is done, for example when the wrong level is performed. The customer wants to be able to identify and have the option to remove these results whether they have been accepted or rejected in nPOC OPs. (cobas infinity does not allow you to reject results after they have been accepted first). Possible solution is to include a deviation filter (like the one in the QC Insights dashboard) that allows you to limit the number of outliers that would inaccurately skew the MoU calculation |
