Combined strategy of knowledge‐based rule selection and historical data percentile‐based range determination to improve an autoverification system for clinical chemistry test results. Issue 2 (10th January 2022)

• Record Type: Journal Article
• Authors: Zhu, Jing; Wang, Hao; Wang, Beili; Hao, Xiaoke; Cui, Wei; Duan, Yong; Zhang, Yi; Ming, Liang; Zhou, Yingchun; Ding, Haitao; Ou, Hongling; Lin, Weiwei; Lu, Liu; Shang, Yuanjiang; Yang, Yong; Liang, Xianming; Ma, Jiangtao; Sun, Wenhua; Chen, Te; Han, Guang; Han, Meng; Yu, Weiting; Pan, Baishen; Guo, Wei
• Is Part Of: Journal of clinical laboratory analysis. Volume 36:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-10
• Subjects: autoverification system -- clinical chemistry test report -- efficiency -- historical data percentile‐based -- knowledge‐based; Diagnosis, Laboratory -- Periodicals; Medical laboratory technology -- Periodicals; 616
• DOI: 10.1002/jcla.24233
• Languages: English
• ISSNs: 0887-8013

Abstract

Abstract: Background: Current autoverification, which is only knowledge‐based, has low efficiency. Regular historical data analysis may improve autoverification range determination. We attempted to enhance autoverification by selecting autoverification rules by knowledge and ranges from historical data. This new system was compared with the original knowledge‐based system. Methods: New types of rules, extreme values, and consistency checks were added and the autoverification workflow was rearranged to construct a framework. Criteria for creating rules for extreme value ranges, limit checks, consistency checks, and delta checks were determined by analyzing historical Zhongshan laboratory data. The new system's effectiveness was evaluated using pooled data from 20 centers. Efficiency improvement was assessed by a multicenter process. Results: Effectiveness was evaluated by the true positive rate, true negative rate, and overall consistency rate, as compared to manual verification, which were 77.55%, 78.53%, and 78.3%, respectively for the new system. The original overall consistency rate was 56.2%. The new pass rates, indicating efficiency, were increased by 19%‒51% among hospitals. Further customization using individualized data increased this rate. Conclusions: The improved system showed a comparable effectiveness and markedly increased efficiency. This transferable system could be further improved and popularized by utilizing historical data from each hospital. Abstract : The clinical chemistry report results are evaluated following the process flow. The rules are selected by medical laboratory experts. The criteria for each rule are determined using historical data.