Patient‐based real‐time quality control in medical laboratories: On the design and robustness of the moving average control chart with truncation limits

控制图 稳健性(进化) X-条形图 截断(统计) 图表 统计 计算机科学 控制限值 \条形图x和R 数学 控制(管理) 控制理论(社会学) 可靠性工程 工程类 人工智能 生物化学 化学 过程(计算) 基因 操作系统
作者
Ugur Murat,Murat Caner Testik,Aslı Pinar
出处
期刊:Quality and Reliability Engineering International [Wiley]
标识
DOI:10.1002/qre.3502
摘要

Abstract Quality is an indispensable requirement in medical laboratories, where controls are performed at different phases of the laboratory processes. In the following, we consider the internal quality control activities during the analytical phase of medical laboratories for verifying the test results prior to their release. To monitor the measurement process for errors in the test results, one approach that is considerably attracting the health professionals is patient‐based real‐time quality control. In this respect, real‐time patient test results are monitored, commonly by implementing the Moving Average (MA) control chart with truncation limits. A usual practice is the use of truncation limits to exclude abnormal patient test results from the MA calculations. However, an open issue in the literature is how to determine the width of the truncation limits and the window size in the MA calculations. Furthermore, the distributions of the test results are often nonnormal and difficult to model by personnel having insufficient training in statistics. Consequently, robustness of the performance of the MA control chart to non‐normal distributions of test results is another open topic in the literature. In this study, a detailed robustness analysis is performed by considering various distributions having different shapes. Performance of several MA control chart designs constructed by altering the truncation limits and window sizes are investigated. Based on our simulation results with selected distributions, we conclude that the truncation limits should not be used and a window size of 20 is recommended. Under such a setting, the MA control chart performs similar to its intended performance in terms of false alarms and in detecting systematic errors, hence being robust to non‐normality of the test results.
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