Managing Lab Autoverification Rules: A Practical, Risk‑Aware Lifecycle Approach

Labs that rely on LIS‑based autoverification see faster results, steadier outputs, and lighter workloads. Yet the rules that drive these checks can become outdated as new analyzers arrive, reagents change, calibrators shift, reference ranges evolve, and the mix of patients varies. Interfaces and the underlying information system may also be updated, creating a constant risk of drift. To keep patient safety intact, a clear plan for handling these changes is essential.

The new framework pulls together several international and national standards, including ISO 15189:2022, ISO 22367:2026, CLSI AUTO15, the archived CLSI AUTO10‑A, and China’s WS/T 616‑2018. It is built around a lifecycle model that maps every stage from creation to retirement. A taxonomy lists the most common change triggers, such as new reagent lots or updated reference intervals. A six‑domain risk assessment classifies each trigger as low, moderate, or high. Depending on the risk level, labs apply proportionate verification or revalidation steps. The system also records version control, tracks deployments, and defines monitoring indicators. Discipline‑specific guidance covers chemistry, immunoassay, hematology, and coagulation, and the framework introduces false‑pass and false‑hold metrics illustrated with a synthetic potassium case.

The framework treats autoverification rules like any other clinical decision‑support tool: they must be versioned, risk‑aware, and continuously watched. Structured change control ensures that every update is logged and reviewed. When a rule is altered, the lab decides how much testing is needed based on the risk rating. Ongoing monitoring catches unexpected behavior early, allowing quick correction before patient results are affected. This approach keeps the system reliable while reducing unnecessary revalidation work.

By following this practical, standards‑anchored approach, laboratories can maintain the benefits of autoverification without sacrificing safety. The method simplifies how teams handle updates, provides clear criteria for verification, and offers concrete metrics to gauge performance. In the end, labs enjoy smoother operations, more consistent reporting, and greater confidence that their automated checks remain accurate throughout the rule's life.