Combined strategy of knowledge‐based rule selection and historical data percentile‐based range determination to improve an autoverification system for clinical chemistry test results

• Published in: Journal of clinical laboratory analysis Vol. 36; no. 2; pp. e24233 - n/a
• Main 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
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.02.2022; John Wiley and Sons Inc
• Subjects: Artificial Intelligence; Automation; Automation, Laboratory; autoverification system; clinical chemistry test report; Clinical Chemistry Tests; Efficiency; Feasibility Studies; historical data percentile‐based; Humans; Immunoglobulins; Knowledge; Knowledge Bases; knowledge‐based; Medical Informatics Applications; Medical laboratories; Reagents; China; autoverification system; efficiency; clinical chemistry test report; historical data percentile-based; knowledge-based
• ISSN: 0887-8013; 1098-2825
• DOI: 10.1002/jcla.24233

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. 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.