Designation and development of autoverification for biochemical parameters in central laboratory of Hatay Mustafa Kemal University Health application and Research Hospital
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Abstract (EN)
Background and Aim: Autoverification (AV) is commonly described as a post-analytical process improvement tool that uses algorithms to evaluate and approve laboratory test results in accordance with the specified criteria. In our study, we aimed to design AV for clinical biochemistry tests through the middleware (LIOS) to develop algorithms and increase the efficiency of autoverification in the Central Laboratory of HMKU. Methods: Autoverification algorithms were developed by use of middleware. A flowchart of AV was created by defining evaluation criteria of 30 biochemistry test through the LIOS. In order to verify the criteria work as specified, the expected validation rates were obtained by testing 720 patient results produced specifically on the simulator software. 194,520 reports and 2,025,948 tests from 01.06.2019 to 31.05.2020 were used to evaluate the AV system with actual patient data. Results: In our study, the AV passing rate calculated between 77.11% and 85.03%. Limit check were observed to be the highest frequent criterion for stopping autovalidation with 40.78% and absurdity values were detected to be the least frequent criterion for non-validated results with 0.09%. A total of 328 reports, which were evaluated by seven users, were compared with autoverification. Statistical analysis resulted in a kappa statistic between 0.387 and 0.629 (p <0.001), and 79.27-88.11% of autoverification rate. Conclusion: The autoverification algorithm that we designed indicated high rate of automated validation could be achieved in clinical biochemistry tests thereby significantly reducing the number of manually validated test results. We believe that implementing AV algorithms enable us to detect analytical and pre-analytical error and ensure obtaining a more consistent review of test results. Keywords: autoverification, biochemistry, clinical laboratory
Author
Bahar Ünlü Gül
How to Cite
Bahar Ünlü Gül (Medical Specialty Thesis). Designation and development of autoverification for biochemical parameters in central laboratory of Hatay Mustafa Kemal University Health application and Research Hospital, 2021, Hatay Mustafa Kemal University.
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