Vladimira Rimac1, Ivana Lapic2, Kresimir Kules3, Dunja Rogic2, Marijana Miler4. 1. Department of Transfusion Medicine and Transplantation Biology, University Hospital Center Zagreb, Zagreb, Croatia. 2. Department of Laboratory Diagnostics, University Hospital Center Zagreb, Zagreb, Croatia. 3. IN2 Group, Zagreb, Croatia. 4. Department of Clinical Chemistry, Sestre Milosrdnice University Hospital Center, Zagreb, Croatia.
Abstract
OBJECTIVE: Autovalidation algorithm should be properly designed with clearly defined criteria and any data that do not meet the criteria, must be reviewed and manually validated. The aim was to define the rules for autovalidation in our laboratory information system (LIS), and validate the algorithm prior to its implementation in routine laboratory work. METHODS: Autovalidation was implemented for all routine serum biochemistry tests. The algorithm included analytical measurement ranges (AMR), delta check, critical values, serum indices and all preanalytical and analytical flags from the analyzer. RESULTS: In the validation process 9805 samples were included, and 78.3% (7677) of all samples were autovalidated. The highest percentage of non-validated samples (54.9%) refers to those with at least one result outside the method linearity ranges (AMR criteria) while critical values were observed to be the least frequent criterion for stopping autovalidation (1.8%). Also, 38 samples were manually validated as they failed to meet the autovalidation criteria. CONCLUSION: Implementation of algorithm for autovalidation in our institution resulted in the redesign of the existing LIS. This model of the autovalidation algorithm significantly decreased the number of manually validated test results and can be used as a model for introducing autovalidation in other laboratory settings.
OBJECTIVE: Autovalidation algorithm should be properly designed with clearly defined criteria and any data that do not meet the criteria, must be reviewed and manually validated. The aim was to define the rules for autovalidation in our laboratory information system (LIS), and validate the algorithm prior to its implementation in routine laboratory work. METHODS: Autovalidation was implemented for all routine serum biochemistry tests. The algorithm included analytical measurement ranges (AMR), delta check, critical values, serum indices and all preanalytical and analytical flags from the analyzer. RESULTS: In the validation process 9805 samples were included, and 78.3% (7677) of all samples were autovalidated. The highest percentage of non-validated samples (54.9%) refers to those with at least one result outside the method linearity ranges (AMR criteria) while critical values were observed to be the least frequent criterion for stopping autovalidation (1.8%). Also, 38 samples were manually validated as they failed to meet the autovalidation criteria. CONCLUSION: Implementation of algorithm for autovalidation in our institution resulted in the redesign of the existing LIS. This model of the autovalidation algorithm significantly decreased the number of manually validated test results and can be used as a model for introducing autovalidation in other laboratory settings.