James K Fleming1, Alexander Katayev2. 1. Laboratory Corporation of America, Holdings, Department of Science and Technology, 112 Orange Drive, Elon, NC 27244, USA. Electronic address: fleminj@labcorp.com. 2. Laboratory Corporation of America, Holdings, Department of Science and Technology, 112 Orange Drive, Elon, NC 27244, USA. Electronic address: katayea@labcorp.com.
Abstract
OBJECTIVES: To develop and implement a quality control protocol using real-time patient data with immediate failure analysis and prevention of releasing results that exceed the allowable total error. DESIGN AND METHODS: Patient data are analyzed in real time using algorithms that incorporate moving medians and moving means for selected chemistry analytes. Simulation software was developed to determine optimal algorithms, establish error limits, and number of patient results for calculation of a single cumulative datum point. Algorithms for moving median (MovMed) and mean (MovMen) were chosen and validated for each analyte. Error limits (TEa) were established using biological and analytical variation with a goal of greater than 90% error detection rate during simulation runs. Middleware software was developed to prohibit the release of patient results upon error detection. RESULTS: A block size of 50 was determined to be the optimal number of patient results used in cumulative calculations. The application of MovMed and MovMen algorithms achieved 0% false rejection for 24 out of 28 tests (85.7%) during the simulation phase. Four tests had a false rejection rate ranging from 0.2 to 1.0%. Error detection rates of 100% were achieved for 16 out of 28 tests (57.1%). Twelve tests had error detection rates ranging from 94.5 to 99.8%. Traditional QC material utilization was reduced by approximately 75-85% and repeat analysis was reduced by approximately 50%. CONCLUSIONS: We successfully developed and implemented a real-time quality control protocol using patient results with true error detection and without release of erroneous results.
OBJECTIVES: To develop and implement a quality control protocol using real-time patient data with immediate failure analysis and prevention of releasing results that exceed the allowable total error. DESIGN AND METHODS: Patient data are analyzed in real time using algorithms that incorporate moving medians and moving means for selected chemistry analytes. Simulation software was developed to determine optimal algorithms, establish error limits, and number of patient results for calculation of a single cumulative datum point. Algorithms for moving median (MovMed) and mean (MovMen) were chosen and validated for each analyte. Error limits (TEa) were established using biological and analytical variation with a goal of greater than 90% error detection rate during simulation runs. Middleware software was developed to prohibit the release of patient results upon error detection. RESULTS: A block size of 50 was determined to be the optimal number of patient results used in cumulative calculations. The application of MovMed and MovMen algorithms achieved 0% false rejection for 24 out of 28 tests (85.7%) during the simulation phase. Four tests had a false rejection rate ranging from 0.2 to 1.0%. Error detection rates of 100% were achieved for 16 out of 28 tests (57.1%). Twelve tests had error detection rates ranging from 94.5 to 99.8%. Traditional QC material utilization was reduced by approximately 75-85% and repeat analysis was reduced by approximately 50%. CONCLUSIONS: We successfully developed and implemented a real-time quality control protocol using patient results with true error detection and without release of erroneous results.