Quality control. We all know the term, and we all think we know what it means. Moreover, we’re all completely, 100%, sure we know what it means for a clinical lab and its lab information system (LIS). After all, QC done by hand is slow, tedious, and strangely easy to get wrong. A lab tech examines a control value and decides it looks fine, thus they release the run, dozens of times a shift. Automating this simple process should be a doozy, right?
Well, not really.
Automating QC in your LIS is critical, as it helps separate two jobs the LIS can take over: judging whether the analytical run is in control and deciding whether each patient result is safe to release. The first is classic QC automation. However, the second is autoverification.
And this is exactly why this article matters.
Automating the Lab QC Rules Themselves
The first thing you’d want to automate is the control evaluation. Every time a control sample runs, the LIS platform should compare it against the standard deviation and automatically apply the accepted, predefined rules.
Thus, breaches or shifts will trigger alerts instantly, instead of staff squinting endlessly at charts in hopes of catching something.
Automating the QC process with predefined rules and definitions will eliminate the two major failure modes of manual QC: missed violations and inconsistent judgment. Creating automated consistency is the whole basic point of moving QC into the LIS platform in the first place.
QC automation also creates records that are needed for compliance and regulatory inspections. Each control result, each rule evaluation, and each accept-or-reject decision will be logged automatically, creating a comprehensive autism trail. This is exactly what a compliance inspector is looking for – but more importantly, this is the exact tedious routine that creates the human element burnout that brings down so many labs.
Bottom line? Automated QC turns documentation from a chore into an everyday routine computerized task – and everybody wins.
Autoverification: Automating Lab QC on Every Result
Autoverification is the process in which the LIS platform applies QC logic to patient results, not just controls. To conduct a precise audit of the patients’ results against their own history, the platform will use predefined rules, limit and delta checks, and instrument error flags. This way, the platform automatically releases clean results and holds questionable ones for a human to review.
And the evidence to support this process? Well, it is striking:
A study on the verification of thyroid tests with automation proves that it’s possible to cut the median turnaround time from 122 minutes to under 89, doing so by routing only flagged results to manual review. Another study, dedicated to the CLSI AUTO-10A guideline, demonstrates ways to reduce error rates and turnaround times across millions of biochemistry results. This remarkable (yet known) pattern repeats in this study of HbA1c autoverification, which reported shorter turnaround time, reduced labor, and fewer manual-review errors.
And if you prefer the short version, here are the highlights – autoverification eventually delivers:
- Faster turnaround, since normal results no longer wait in a manual queue
- Fewer errors, because the rules never tire or get distracted
- Consistency, as every result faces the same checks, exactly
- Better use of staff, who focus only on the results that genuinely need judgment
The results are quite unanimous: results are reached sooner, the backlog stops, and skilled staff spend their attention where it changes outcomes instead of clicking accept on results that were never going to be a problem.
Automating Lab QC Without Losing Control
However, automation is not a license to stop thinking. Despite the proven improvements across every metric (from quality to time spent), the QC rules must be designed, validated, and monitored. Additionally, a portion of the results should always reach a human review. No matter how good your lab’s automation process is, there is still a human QC that must be done periodically. Just like working with every machine learning or AI protocol, the human element is critical.
When done carelessly, autoverification can pass a bad result as confidently as a good one. However, with human supervision that takes into account staffing, ROI, and efficiency – and done well – automation can catch what tired humans miss. At the end of the day, automation will free your staff to do the work humans do best.
Automation Works Better on the Cloud
The punchline is that cloud-native LIS platforms make everything we’ve talked about practical, with the QC engine and autoverification logic built into the core. Most legacy systems bolt on through extra middleware. But a cloud-native LIS platform can scale easily and handle increased workloads without a glitch (for example, because the on-premises IT burden is out of the picture). Thus, automating the rules, validating them hard, and keeping humans for exceptions is second nature to cloud-based LIS platforms. That is how you automate QC and come out safer, not just faster.
➡️ DISCOVER HOW
