Design and assessment of average of normals (AON) patient data algorithms to maximize run lengths for automatic process control

Abstract

Achieving high quality and high productivity with automated testing processes will require process control systems that are optimized for the necessary error detection, minimum false rejection, and maximum run length. This study investigates whether run length could be monitored by average of normals (AON) algorithms that truncate the patient test distribution and estimate the average of a suitable number of patient results. The design of AON algorithms for individual analytes is facilitated by computer-simulated power curves that consider the ratio of the population biological variation (s(pop)) to the test method variation (s(meas)), represent a range of s(pop)/s(meas) ratios from 2 to 15, and include numbers of patient test results from 10 to 600. The potential applications of AON algorithms are assessed for 38 tests whose quality requirements represent the total error criteria from the Ontario Medical Association Laboratory Proficiency Testing Program, s(pop)/s(meas) ratios from 0 to 32, critical systematic shifts from 0.02 to 10.85 s(meas), and test workloads representative of a regional reference laboratory. Approximately half of these tests provide high potential for applying AON algorithms to monitor run length.