Abstract: A clinical diagnostic analyzer for performing a virtual crossover study on quality control (QC) material includes a processor, memory, measurement hardware, and an input panel/display. The analyzer acquires data from a peer group relating to a new lot of quality control material to calculate a predicted mean and standard deviation based on that peer group data and adjusted for bias in the laboratory process. As new analyses are run on the new lot of QC material, the predicted mean and standard deviation are updated to incorporate the actual data on a weighted basis.

Abstract: A clinical diagnostic analyzer for performing an automated crossover study on quality control (QC) material includes a processor, memory, measurement hardware, and an input panel/display. The analyzer prompts a user to load a QC specimen, and to instigate testing and analysis to determine a mean and a standard deviation for the new material. Associated methods for using one or more clinical diagnostic analyzers to calculate a new mean and standard deviation for a new QC material, reduce error in the calculated mean value, and to reduce the total number of days to complete a crossover study are also disclosed.

Abstract: A clinical diagnostic analyzer for performing a virtual crossover study on quality control (QC) material includes a processor, memory, measurement hardware, and an input panel/display. The analyzer acquires data from a peer group relating to a new lot of quality control material to calculate a predicted mean and standard deviation based on that peer group data and adjusted for bias in the laboratory process. As new analyses are run on the new lot of QC material, the predicted mean and standard deviation are updated to incorporate the actual data on a weighted basis.

Abstract: Disclosed are a system and method to recover from a large out-of-control condition in a clinical diagnostic process. Upon detection of a large out-of-control condition, a CUSUM rule is designed to predict if specimen test data was produced during the occurrence of that condition. Specimen test data is re-evaluated and the CUSUM rule is applied to determine whether to accept or reject the test results. Upon completion of the re-evaluation, old test results identified as being invalid are replaced with the new, re-evaluated results and the new results are published if publication did not occur previously.

Abstract: Disclosed are a system and method to automatically spot-check for small out-of-control conditions of a clinical diagnostic process. The expected number of unreliable results evaluated since the last good quality control event is estimated by estimating the magnitude of the out-of-control condition, constructing concentration based bias, imprecision, and TEa profiles, and using those parameters to estimate the probability of a specimen result being unreliable. Patient specimens are automatically selected for reevaluation based on their probability of crossing a medical decision limit or having a probability of being unreliable. The system and method determine which reevaluated patient specimens need to be corrected when the results for those specimens have already been reported. Various exemplary embodiments and implementations of the system and method are included.

Abstract: A system and method for determining a sigma of a clinical diagnostic process are disclosed. Specimen data are collected from a plurality of laboratory instruments. The specimen data are evaluated to determine a concentration and an analytical standard deviation for each data point. A clinical diagnostic process is run and patient analyte values are acquired, and a standard deviation is assigned to each patient analyte value based on the standard deviation of specimen data having a corresponding concentration. A single sigma-metric is computed based on the patient analyte assigned standard deviations, the sigma-metric representing the sigma of the clinical diagnostic process. The computed sigma-metric is reported to a user or laboratory manager.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur. A method of determining an optimum frequency for bracketing patient data into batches using a minimum number of quality control samples is also provided.

Abstract: A system and method to automatically implement quality control of a clinical diagnostic process are disclosed. Upon generation of an internal error flag, a confirmation rule automatically checks a questionable patient statistic alert by testing a quality control specimen, applying event-related quality control rules to the results of that test, and provides an alert to the operator only upon a confirmed patient signal. The automatic quality control process thus eliminates the uncertainty of operator reaction to an alert signal and implements the quality control run automatically, without operator intervention.

Abstract: A system and method for determining a sigma of a clinical diagnostic process are disclosed. Specimen data are collected from a plurality of laboratory instruments. The specimen data are evaluated to determine a concentration and an analytical standard deviation for each data point. A clinical diagnostic process is run and patient analyte values are acquired, and a standard deviation is assigned to each patient analyte value based on the standard deviation of specimen data having a corresponding concentration. A single sigma-metric is computed based on the patient analyte assigned standard deviations, the sigma-metric representing the sigma of the clinical diagnostic process. The computed sigma-metric is reported to a user or laboratory manager.

Abstract: A system and method to automatically implement quality control of a clinical diagnostic process are disclosed. Upon generation of an internal error flag, a confirmation rule automatically checks a questionable patient statistic alert by testing a quality control specimen, applying event-related quality control rules to the results of that test, and provides an alert to the operator only upon a confirmed patient signal. The automatic quality control process thus eliminates the uncertainty of operator reaction to an alert signal and implements the quality control run automatically, without operator intervention.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur. A method of determining an optimum frequency for bracketing patient data into batches using a minimum number of quality control samples is also provided.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur.

Abstract: Systems and methods configured to guide and manage laboratory analytical process control operations. A Biometric quality control (QC) process application is configured to monitor bias and imprecision for each test, characterize patient population data distributions and compare, contrast, and correlate changes in patient data distributions to any change in QC data populations. The Biometric QC process monitors the analytical process using data collected from repetitive testing of quality control materials and patient data (test results). The QC process identifies the optimal combination of, for example, frequency of QC testing, number of QCs tested, and QC rules applied in order to minimize the expected number of unacceptable patient results produced due to any out-of-control error condition that might occur.