No abstract

The rst essential in setting up internal quality control (IQC) of a test procedure in the clinical laboratory is to select the proper IQC procedure to implement, i.e. choosing the statistical criteria or control rules, and the number of control measurements, according to the quality required for the test and the observed performance of the method. Then the right IQC procedure must be properly implemented. This review focuses on strategies for planning and implementing IQC procedures in order to improve the quality of the IQC. A quantitative planning process is described that can be implemented with graphical tools such as power function or critical-error graphs and charts of operating speci cations. Finally, a total QC strategy is formulated to minimize cost and maximize quality.A general strategy for IQC implementation is recommended that employs a threestage design in which the rst stage provides high error detection, the second stage low false rejection and the third stage prescribes the length of the analytical run, making use of an algorithm involving the average of normal patients' data.

We describe an approach for formulating criteria that can be used to judge whether an analytical method has acceptable precision and accuracy. We derive criteria for several experiments that are commonly used in method-evaluation studies: precision or replicates, recovery, interference, and comparison of patient values between the new method and a proven method. These criteria are based on the medical usefulness of the test results, thus the acceptability of the method is judged with respect to the clinical requirements.

When assessing the performance of an internal quality control system, it is useful to determine the probability for false rejections (pfr) and the probability for error detection (ped). These performance characteristics are estimated here by use of a computer stimulation procedure. The control rules studied include those commonly employed with Shewhart-type control charts, a cumulative sum rule, and rules applicable when a series of control measurements are treated as a single control observation. The error situations studied include an increase in random error, a systematic shift, a systematic drift, and mixtures of these. The probability for error detection is very dependent on the number of control observations and the choice of control rules. No one rule is best for detecting all errors, thus combinations of rules are desirable. Some appropriate combinations are suggested and their performance characteristics are presented.

Sigma metrics have become a useful tool for all parts of the quality control (QC) design process. Through the allowable total error model of laboratory testing, analytical assay performance can be judged on the Six Sigma scale. This not only allows benchmarking the performance of methods and instruments on a universal scale, it allows laboratories to easily visualize performance, optimize the QC rules and numbers of control measurements they implement, and now even schedule the frequency of running those controls.