BACKGROUND: Traditionally, statistical quality control (SQC) planning is aimed at preventing the error rate from exceeding a pre-defined acceptable rate (Westgard JO. Basic QC Practices, 4th ed. Westgard QC, 2016). A pivotal characteristic for planning a QC procedure with the traditional approach is the probability of rejecting an analytical run that contains critical size errors (Pedc). Multi-rule QC procedures, with fully documented power curves, are important tools for SQC. In addition, it has been recommended (Parvin CA, Gronowski AM. Effect of analytical run length on quality-control (QC) performance and the QC planning process. Clin Chem 1997;43:2149-54) to optimize the frequency of QC on the basis of the maximum expected increase in the number of unacceptable patient results reported during the presence of an undetected out-of-control error condition [Max E(Nuf)]. The relationship between Pedc and Max E(Nuf) has been studied for single rule QC procedures (Yago M, Alcover S. Selecting statistical procedures for quality control planning based on risk management. Clin Chem 2016;62:959-65), but corresponding information for multi-rule QC is lacking. METHODS: We used a statistical model to investigate the relationship between Pedc and Max E(Nuf) for multi-rules commonly used in clinical laboratories, and constructed charts relating the Max E(Nuf) and the sigma capability of the examination procedure for multi-rules which can be used as practical tools for planning SQC. RESULTS: There is a close relationship between Pedc and Max E(Nuf) for commonly used multi-rules. Common multi-rule SQC procedures traditionally designed for high Pedc will also provide low Max E(Nuf) values. CONCLUSIONS: Multi-rule SQC procedures can be used for controlling intermediate and low sigma capability method to attain a low Max E(Nuf) so that the probability of patient harm is mitigated to acceptable levels.
BACKGROUND: Traditionally, statistical quality control (SQC) planning is aimed at preventing the error rate from exceeding a pre-defined acceptable rate (Westgard JO. Basic QC Practices, 4th ed. Westgard QC, 2016). A pivotal characteristic for planning a QC procedure with the traditional approach is the probability of rejecting an analytical run that contains critical size errors (Pedc). Multi-rule QC procedures, with fully documented power curves, are important tools for SQC. In addition, it has been recommended (Parvin CA, Gronowski AM. Effect of analytical run length on quality-control (QC) performance and the QC planning process. Clin Chem 1997;43:2149-54) to optimize the frequency of QC on the basis of the maximum expected increase in the number of unacceptable patient results reported during the presence of an undetected out-of-control error condition [Max E(Nuf)]. The relationship between Pedc and Max E(Nuf) has been studied for single rule QC procedures (Yago M, Alcover S. Selecting statistical procedures for quality control planning based on risk management. Clin Chem 2016;62:959-65), but corresponding information for multi-rule QC is lacking. METHODS: We used a statistical model to investigate the relationship between Pedc and Max E(Nuf) for multi-rules commonly used in clinical laboratories, and constructed charts relating the Max E(Nuf) and the sigma capability of the examination procedure for multi-rules which can be used as practical tools for planning SQC. RESULTS: There is a close relationship between Pedc and Max E(Nuf) for commonly used multi-rules. Common multi-rule SQC procedures traditionally designed for high Pedc will also provide low Max E(Nuf) values. CONCLUSIONS: Multi-rule SQC procedures can be used for controlling intermediate and low sigma capability method to attain a low Max E(Nuf) so that the probability of patient harm is mitigated to acceptable levels.
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Keywords:
frequency of quality control; patient risk; risk-based statistical quality control; statistical quality control