Measure stage-wise time before changing anything
"Our TAT is too slow" isn't specific enough to act on. A report that takes six hours end to end could be losing four of those hours in a validation backlog, or two hours waiting for a single approver who's also doing something else. Map every stage from sample collection to report release, then time each one separately for a representative week.
This single step usually reveals that most labs don't have a uniform TAT problem — they have one or two specific bottleneck stages that account for most of the delay, while the rest of the pipeline is already reasonably fast.
A stage-wise framework to find the actual bottleneck
Use this as a starting structure. Track average time per stage for a week, then compare stages against each other, not against an abstract benchmark — the goal is finding your lab's specific bottleneck.
| Stage | Common cause of delay | What usually fixes it |
|---|---|---|
| Accession | Manual data entry, unclear priority triage | Barcode-linked entry, standardized priority rules |
| Analyzer processing | Delayed feeds, reruns not flagged clearly | Real-time exception alerts, a defined rerun protocol |
| Validation | Inconsistent thresholds between shifts | Written validation rules applied uniformly across shifts |
| Approval | Single approver bottleneck, unclear ownership | Named owner with a visible target window per shift |
| Release/delivery | Manual report handoff or printing | Automated digital delivery on approval |
Standardize decisions so TAT doesn't depend on who's on shift
A large share of avoidable delay comes from teams re-deciding the same situations differently each time: how to prioritize a rush sample, when a result needs a rerun, what counts as a critical value requiring immediate escalation. Write these rules down once, apply them across every shift, and TAT variance drops even before you touch any technology.
- A written priority protocol for STAT/urgent samples that every shift follows identically
- Clear validation thresholds so borderline results aren't re-judged case by case
- A defined exception path for delayed analyzer feeds and reruns that doesn't block the rest of the queue
- An explicit critical-value escalation chain, so nobody has to decide who to call in the moment
Give every queue an owner and a visible target
TAT improves reliably when accession, validation, approval, and release each have a named owner with a stated target window — not a shared queue that's everyone's responsibility and therefore nobody's. Live dashboard visibility into pending and delayed samples lets that owner intervene before a small delay becomes an end-of-day backlog.
Run a weekly review, not a quarterly one
Review TAT by test category, branch, and shift every week, not once a quarter. Weekly cadence catches drift — a specific shift, test type, or branch quietly slipping — while it's still a small, fixable gap. Document each corrective action with an owner and a deadline, then check the following week whether it actually moved the number. Consistency in this review habit outperforms any single one-time optimization project.