How can I use molecular diagnostics instruments to scale a lab workflow without sacrificing turnaround time?

To scale a molecular diagnostics workflow without slowing turnaround time (TAT), the key is to optimize for end-to-end throughput, not just instrument speed. The most successful labs usually combine the right instrument mix, workflow design, and process controls.

1) Match instrumentation to your test menu and volume

Choose platforms based on:

• Sample volume: peak daily and weekly load, not just average
• Test complexity: syndromic panels, singleplex PCR, qPCR, RT-PCR, digital PCR, etc.
• Batch size behavior: high-throughput systems help if you can batch without delaying results
• Hands-on time: automation reduces labor bottlenecks

A common mistake is buying a high-throughput analyzer but leaving extraction, accessioning, or setup as the bottleneck.

2) Automate the pre-analytic steps first

TAT is often lost before the assay even starts. Consider automation for:

• Specimen accessioning and barcode tracking
• Decapping/recapping
• Aliquoting
• Nucleic acid extraction
• Plate or cartridge setup

If extraction is manual, it becomes the main constraint even if the analyzer is fast.

3) Use parallelization instead of single-path workflows

To maintain TAT at scale:

• Run multiple smaller systems in parallel rather than one oversized bottleneck if your lab has variable demand
• Separate workflows by test urgency:
  • STAT/priority lane
  • Routine batch lane
• Use queue management rules so urgent samples don’t wait behind large batches

This improves responsiveness while preserving throughput.

4) Reduce batching delays

Batching improves efficiency, but it can hurt TAT if samples wait too long. A good compromise is:

• Set dynamic batch thresholds and time-to-run cutoffs
• Start runs when either:
  • the batch is full, or
  • a defined maximum wait time is reached

This prevents “waiting for the last sample” from delaying release of critical results.

5) Build a workflow around instrument uptime

High throughput only helps if the system is available:

• Schedule preventive maintenance proactively
• Keep critical consumables and reagents in stock
• Track failure points, reruns, and invalid runs
• Train backup staff on instrument setup and troubleshooting

Downtime often costs more TAT than raw assay duration.

6) Integrate informatics

Laboratories scale better when instruments are connected to:

• LIS/LIMS
• Middleware
• Autoverification rules
• QC dashboards

This reduces manual result handling, transcription errors, and release delays. Automated result review can significantly shorten final reporting time.

7) Design for the bottleneck, not the average

Map your workflow and identify the true constraint:

• Specimen receipt
• Extraction
• Amplification
• Data review
• Result reporting

Then upgrade the slowest step first. In many labs, the limiting factor is not the analyzer but the people, prep work, or result interpretation.

8) Standardize and simplify the menu

A broad menu can create operational complexity. To scale efficiently:

• Consolidate assays where possible
• Use common extraction and amplification platforms
• Standardize consumables and protocols
• Reduce variability in run setup

Fewer assay formats usually means fewer errors and shorter training time.

9) Use QC and process metrics continuously

Track:

• Turnaround time by test type
• Hands-on time
• First-pass success rate
• Rerun rate
• Instrument utilization
• Queue time at each workflow stage

These metrics show where scale is causing delay before it becomes visible to clinicians.

10) Plan capacity for peak demand, not average demand

If your system works only under normal volume, TAT will slip during spikes. Build in:

• Surge capacity
• Cross-trained staff
• Flexible shift coverage
• Backup instruments or service agreements

Practical rule of thumb

To scale without sacrificing TAT, aim for:

• Less manual prep
• Shorter queue times
• Parallel processing
• Fast result reporting
• Low rerun/error rates

If you want, I can also help you with one of these:

1. a workflow redesign template for a molecular lab,
2. a buying checklist for selecting instruments, or
3. a capacity planning model to estimate how much throughput you need.