Clinical Chemistry and Autoverification: A Path Less Traversed - Rashmi Rasi Datta; Anurag Bansal (Indian Journal of Medical Biochemistry)
Aims and background: Autoverification (AV) is an application of artificial intelligence that uses computer-based algorithmically established rules for release of patient reports. This allows effective time management, prevents probable laboratory errors, and ensures consistent results. However, not many labs have adopted AV into practice due to hesitations concerning cost-effectiveness, lack of robust software and informatics support along with dearth of knowledge for its implementation. Additionally, there is scant published literature on AV implementation. This study has been conducted as an attempt to outline benefits of AV and address the existing gaps. Methods: The study was conducted in the Department of Clinical Chemistry of a standalone lab. Autoverification implementation was done in stepwise manner. (i) Test selection and developing algorithms, (ii) Setting-up rules in middleware to prevent release of erroneous results, (iii) User acceptance testing (UAT), (iv) Going-live. Results: Efficacy of AV system was gauged based on following factors. (i) AV passing rate—initial 53.7–85.4% later was achieved with inclusion of more parameters and extension of tolerance limit, (ii) Significant improvement was observed in TAT for both immunoassay (from 88.28 to 97.32%) and routine chemistry (from 82.7 to 95.68%), (iii) decreased error rates as evidenced by reduced number of amended reports, (iv) reduction in staff required for manual verification allowing their utilization for other departmental activities. Conclusion: Implementation of AV by laboratories provides efficient and cost-effective work opportunities with scope for continuous growth. However, it doesn’t preclude the need for careful quality control.