Designing and Evaluating an Autoverification RCV-Based System for Thyroid Function Profiles

Background/Objectives: Autoverification systems integrated with fully automated analyzers and expert middleware can reduce manual review workload and improve turnaround time (TAT). This study aimed to develop an autoverification system for thyroid function profiles and to evaluate its performance in clinical practice. Methods: A total of 1,219,141 routine thyroid function test results collected from 1 January 2016 to 31 December 2020 were used to design the autoverification system. The system incorporated quality control checks, instrument error flags, limit range rules, delta check rules, and logical rules. Validation was performed using an independent dataset comprising 81,713 test results. Results: Twelve instrument error flags, a two-step delta check algorithm, one set of limit range rules, and ten logical rules were established. The overall autoverification pass rate was 75.2%. After system optimization, the overall pass rate increased to 77.8%, and the median laboratory TAT decreased from 122.1 min to 88.6 min. Conclusions: The autoverification system for thyroid function profiles significantly improved laboratory efficiency and reduced TAT. While RCV-based delta checks require prior results and are therefore not applicable to new patients, other autoverification rules remain active, and only results that cannot be autoverified are routed to manual review, ensuring safe and complete result verification. This system provides a practical framework for implementing autoverification in routine clinical chemistry testing.