Predicting effects of quality-control practices on the cost-effective operation of a stable, multitest analytical system

Abstract

The cost-effective operation of an analytical system depends on quality-control (QC) practices such as the QC procedure itself (control rules, number of control measurements); the batch size or the run length; and the use of bracketed, nonbracketed, or pre-control modes of operation. Predictive value models that predict the defect rate and test yield of each test, as well as of the system as a whole, have been used to study these practices and to develop strategies for improving the quality and productivity of a multitest analyzer. Quality was optimized for most tests by achieving high error detection and low false rejection by the QC procedures. For a few tests where ideal QC performance could not be achieved, predictive models indicate that high quality is achieved, predictive models indicate that high quality is achieved as long as the observed stabilities (low frequencies of errors) of the measurement procedures are maintained. In our laboratories, productivity gains of 2.9% ($17,400/year) were achieved by changing QC procedures. Predictive models indicate that further gains are possible by increasing batch size and changing from bracketed to nonbracketed control operation. In general, the common practice of bracketed control on stable analytical systems may need to be re-examined owing to its effect on the cost of operation.