ArticleBMC medical informatics and decision making2021
Development and implementation of an LIS-based validation system for autoverification toward zero defects in the automated reporting of laboratory test results.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- National recommendations of the Working Group for Post-analytics of the Croatian Society of Medical Biochemistry and Laboratory Medicine: implementation of autovalidation procedures.Biochemia medica · 2025Review
- Reflex testing automatization for HBsAg carriers: A novel era for hepatitis delta management.JHEP reports : innovation in hepatology · 2024Article
- Key Recommendations for Antimicrobial Resistance Surveillance: Takeaways From the CAPTURA Project.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Designing and validating an autoverification system of biochemical test results in Hatay Mustafa Kemal University, clinical laboratory.Biochemia medica · 2022Article
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Authors and funding
9 authors.
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Abstract
backgroundValidation of the autoverification function is one of the critical steps to confirm its effectiveness before use. It is crucial to verify whether the programmed algorithm follows the expected logic and produces the expected results. This process has always relied on the assessment of human-machine consistency and is mostly a manually recorded and time-consuming activity with inherent subjectivity and arbitrariness that cannot guarantee a comprehensive, timely and continuous effectiveness evaluation of the autoverification function. To overcome these inherent limitations, we independently developed and implemented a laboratory information system (LIS)-based validation system for autoverification.
methodsWe developed a correctness verification and integrity validation method (hereinafter referred to as the "new method") in the form of a human-machine dialog. The system records personnel review steps and determines whether the human-machine review results are consistent. Laboratory personnel then analyze the reasons for any inconsistency according to system prompts, add to or modify rules, reverify, and finally improve the accuracy of autoverification.
resultsThe validation system was successfully established and implemented. For a dataset consisting of 833 rules for 30 assays, 782 rules (93.87%) were successfully verified in the correctness verification phase, and 51 rules were deleted due to execution errors. In the integrity validation phase, 24 projects were easily verified, while the other 6 projects still required the additional rules or changes to the rule settings. Taking the Hepatitis B virus test as an example, from the setting of 65 rules to the automated releasing of 3000 reports, the validation time was reduced from 452 (manual verification) to 275 h (new method), a reduction in validation time of 177 h. Furthermore, 94.6% (168/182) of laboratory users believed the new method greatly reduced the workload, effectively controlled the report risk and felt satisfied. Since 2019, over 3.5 million reports have been automatically reviewed and issued without a single clinical complaint.
conclusionTo the best of our knowledge, this is the first report to realize autoverification validation as a human-machine interaction. The new method effectively controls the risks of autoverification, shortens time consumption, and improves the efficiency of laboratory verification.
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