Automated selection of statistical quality-control procedures to assure meeting clinical or analytical quality requirements.

Efforts to totally automate laboratory testing processes must address the issue of how to assure the quality of the final test result. A recent survey by Tetrault and Steindel (1) reported that laboratories are using the same quality-control (QC) procedures today that they used 10 years ago. Actually, more than half of the laboratories indicated they are still using control limits set as the mean ± 2s (the 12s rule), a practice that dates to the 1950s and ’60s (2)(3), when statistical QC was first used with manual methods and the first generation of automated Technicon AutoAnalyzer systems. Other laboratories indicated they are using variations of the multirule type of QC procedure introduced in the early 1980s (4). Tetrault and Steindel (1) recommend that “the best set of control rules will vary from method to method and cannot be determined through simple algorithms or formulas. The laboratorian has to balance true error-detection capabilities against the probabilities of falsely rejecting a good run.” The information needed about the error-detection and false-rejection characteristics of stable QC procedures became available in the clinical chemistry literature almost 20 years ago (5)(6)(7)(8) and was extended by Cembrowski et al. (9)(10)(11)(12) to consider patient data algorithms. This information was incorporated into laboratory QC texts (13)(14) and clinical chemistry texts (15)(16) in the 1990s and continues to be expanded and improved by Parvin (17)(18)(19)(20) and by Smith and Kroft (21). Guidelines and strategies for selecting QC procedures were described in 1986 (13), and some detailed applications have been published to demonstrate the selection and design of QC procedures (22)(23)(24). QC selection grids were introduced in 1990 …

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