Exponentially adjusted moving mean procedure for quality control. An optimized patient sample control procedure.

Abstract

The idea of using patient samples as the basis for control procedures elicits a continuing fascination among laboratorians, particularly in the current environment of cost restriction. Average of normals (AON) procedures, although little used, have been carefully investigated at the theoretical level. The performance characteristics of Bull's algorithm have not been thoroughly delineated, however, despite its widespread use. The authors have generalized Bull's algorithm to use variably sized batches of patient samples and a range of exponential factors. For any given batch size, there is an optimal exponential factor to maximize the overall power of error detection. The optimized exponentially adjusted moving mean (EAMM) procedure, a variant of AON and Bull's algorithm, outperforms both parent procedures. As with any AON procedure, EAMM is most useful when the ratio of population variability to analytical variability (standard deviation ratio, SDR) is low.