Effect of analytical run length on quality-control (QC) performance and the QC planning process

Abstract

Abstract The performance measure traditionally used in the quality-control (QC) planning process is the probability of rejecting an analytical run when an out-of-control error condition exists. A shortcoming of this performance measure is that it doesn’t allow comparison of QC strategies that define analytical runs differently. Accommodating different analytical run definitions is straightforward if QC performance is measured in terms of the average number of patient samples to error detection, or the average number of patient samples containing an analytical error that exceeds total allowable error. By using these performance measures to investigate the impact of different analytical run definitions on QC performance demonstrates that during routine QC monitoring, the length of the interval between QC tests can have a major influence on the expected number of unacceptable results produced during the existence of an out-of-control error condition.