Curtis A Parvin1. 1. Washington University School of Medicine, Department of Pathology and Immunology, Saint Louis, MO 63110, USA. parvin@wustl.edu
Abstract
BACKGROUND: The traditional measure used to evaluate QC performance is the probability of rejecting an analytical run that contains a critical out-of-control error condition. The probability of rejecting an analytical run, however, is not affected by changes in QC-testing frequency. A different performance measure is necessary to assess the impact of the frequency of QC testing. METHODS: I used a statistical model to define in-control and out-of-control processes, laboratory testing modes, and quality control strategies. RESULTS: The expected increase in the number of unacceptable patient results reported during the presence of an undetected out-of-control error condition is a performance measure that is affected by changes in QC-testing frequency. I derived this measure for different out-of-control error conditions and laboratory testing modes and showed that a worst-case expected increase in the number of unacceptable patient results reported can be estimated. The laboratory thus has the ability to design QC strategies that limit the expected number of unacceptable patient results reported. CONCLUSIONS: To assess the impact of the frequency of QC testing on QC performance, it is necessary to move beyond thinking in terms of the probability of accepting or rejecting analytical runs. A performance measure based on the expected increase in the number of unacceptable patient results reported has the dual advantage of objectively assessing the impact of changes in QC-testing frequency and putting focus on the quality of reported patient results rather than the quality of laboratory batches.
BACKGROUND: The traditional measure used to evaluate QC performance is the probability of rejecting an analytical run that contains a critical out-of-control error condition. The probability of rejecting an analytical run, however, is not affected by changes in QC-testing frequency. A different performance measure is necessary to assess the impact of the frequency of QC testing. METHODS: I used a statistical model to define in-control and out-of-control processes, laboratory testing modes, and quality control strategies. RESULTS: The expected increase in the number of unacceptable patient results reported during the presence of an undetected out-of-control error condition is a performance measure that is affected by changes in QC-testing frequency. I derived this measure for different out-of-control error conditions and laboratory testing modes and showed that a worst-case expected increase in the number of unacceptable patient results reported can be estimated. The laboratory thus has the ability to design QC strategies that limit the expected number of unacceptable patient results reported. CONCLUSIONS: To assess the impact of the frequency of QC testing on QC performance, it is necessary to move beyond thinking in terms of the probability of accepting or rejecting analytical runs. A performance measure based on the expected increase in the number of unacceptable patient results reported has the dual advantage of objectively assessing the impact of changes in QC-testing frequency and putting focus on the quality of reported patient results rather than the quality of laboratory batches.