Abstract: Methods, apparatuses, and systems are disclosed for analyzing quality control (QC) strategies that are applied to testing processes an analyte in order to meet an acceptable level of probability of patient harm that could result from incorrect test results. The measure of patient harm takes into account severity of patient harm, as well as its occurrence. Methods include calculating, based on the parameters of the QC strategies and the test apparatus, an expected number of incorrect final results E(Nuf) due to a test system failure. The value of E(Nuf) can be used as part of a calculation of a predicted level of probability patient harm. The ratio of the acceptable level of probability of patient harm to the predicted level of probability patient harm can determine the adequacy of the QC strategies.

Abstract: Methods, apparatuses, and systems are disclosed for analyzing quality control (QC) strategies that are applied to testing processes an analyte in order to meet an acceptable level of probability of patient harm that could result from incorrect test results. The measure of patient harm takes into account severity of patient harm, as well as its occurrence. Methods include calculating, based on the parameters of the QC strategies and the test apparatus, an expected number of incorrect final results E(Nuf) due to a test system failure. The value of E(Nuf) can be used as part of a calculation of a predicted level of probability patient harm. The ratio of the acceptable level of probability of patient harm to the predicted level of probability patient harm can determine the adequacy of the QC strategies.

Abstract: Methods, apparatuses, and systems are disclosed for analyzing quality control (QC) strategies that are applied to testing processes an analyte in order to meet an acceptable level of probability of patient harm that could result from incorrect test results. The measure of patient harm takes into account severity of patient harm, as well as its occurrence. Methods include calculating, based on the parameters of the QC strategies and the test apparatus, an expected number of incorrect final results E(Nuf) due to a test system failure. The value of E(Nuf) can be used as part of a calculation of a predicted level of probability patient harm. The ratio of the acceptable level of probability of patient harm to the predicted level of probability patient harm can determine the adequacy of the QC strategies.

Abstract: Methods, apparatuses, and systems are disclosed for analyzing quality control (QC) strategies that are applied to testing processes an analyte in order to meet an acceptable level of probability of patient harm that could result from incorrect test results. The measure of patient harm takes into account severity of patient harm, as well as its occurrence. Methods include calculating, based on the parameters of the QC strategies and the test apparatus, an expected number of incorrect final results E(Nuf) due to a test system failure. The value of E(Nuf) can be used as part of a calculation of a predicted level of probability patient harm. The ratio of the acceptable level of probability of patient harm to the predicted level of probability patient harm can determine the adequacy of the QC strategies.

Abstract: A method analyzes a quality control strategy. A quality control rule can define quality control events and specifying a control limit for determining whether a quality control event passes or fails. The quality control rule and a number of patient samples tested between quality control events can be received. A first expected number of correctible errors when a quality control event fails can be computed based on the quality control rule and the number of patient samples tested between quality control events. A second expected number of final errors that are not correctible when a quality control event fails can be computed based on the quality control rule and the number of patient samples tested between quality control events. An assessment of the quality control rule can include the first expected number of correctible errors and the second expected number of final errors as separate values.

Abstract: A method analyzes a quality control strategy. A quality control rule can define quality control events and specifying a control limit for determining whether a quality control event passes or fails. The quality control rule and a number of patient samples tested between quality control events can be received. A first expected number of correctible errors when a quality control event fails can be computed based on the quality control rule and the number of patient samples tested between quality control events. A second expected number of final errors that are not correctible when a quality control event fails can be computed based on the quality control rule and the number of patient samples tested between quality control events. An assessment of the quality control rule can include the first expected number of correctible errors and the second expected number of final errors as separate values.

Abstract: The present invention proposes a method for optimizing a quality control strategy for rapid release results. An embodiment of the invention includes generating a set of candidate quality control rules and for each candidate rule, computing a maximum number of patient specimens that can be tested between quality control events while keeping the expected number of correctable unacceptable results below a predetermined correctable maximum and keeping the expected number of final unacceptable results below a predetermined final maximum. Furthermore a quality control utilization rate can be computed based on the number of patient specimens tested between each quality control event and the number of reference samples tested at each quality control event. The candidate rule for which the best quality control utilization rate may be selected along with the corresponding number of patients to be tested between each quality control as the optimum quality control strategy.

Abstract: A method of establishing statistically valid assay means and ranges for quality control materials, used to qualify medical testing machines, utilizes tests on a new lot of quality control material to establish an assay mean, and uses data from a database of historical test results to establish an assay range. The system may estimate the variability of test results from prior lot data, and then compute the limits of the assay range such that a new test on a new lot of the quality control material will be expected to fall within the range with a specified probability. Because historical data is used to estimate the test variability, the number of new tests required to specify a statistically valid mean and range may be dramatically reduced, as compared with establishing the mean and range based only on tests of the new lot of material.

Abstract: The present invention proposes a method for optimizing a quality control strategy for rapid release results. An embodiment of the invention includes generating a set of candidate quality control rules and for each candidate rule, computing a maximum number of patient specimens that can be tested between quality control events while keeping the expected number of correctable unacceptable results below a predetermined correctable maximum and keeping the expected number of final unacceptable results below a predetermined final maximum. Furthermore a quality control utilization rate can be computed based on the number of patient specimens tested between each quality control event and the number of reference samples tested at each quality control event. The candidate rule for which the best quality control utilization rate may be selected along with the corresponding number of patients to be tested between each quality control as the optimum quality control strategy.

Abstract: A method of establishing statistically valid assay means and ranges for quality control materials, used to qualify medical testing machines, utilizes tests on a new lot of quality control material to establish an assay mean, and uses data from a database of historical test results to establish an assay range. The system may estimate the variability of test results from prior lot data, and then compute the limits of the assay range such that a new test on a new lot of the quality control material will be expected to fall within the range with a specified probability. Because historical data is used to estimate the test variability, the number of new tests required to specify a statistically valid mean and range may be dramatically reduced, as compared with establishing the mean and range based only on tests of the new lot of material.