The Problem
A CAP/CLIA genomics clinical lab needed to scale Sanger sequencing without adding headcount, but the existing workflow could not scale due to manual steps, inefficient automation, and high labor requirements.
Key Issues:
- Throughput limited to 2 batches of 48 samples per week
- Full PCR workflow required a licensed Clinical Laboratory Scientist (CLS)
- High manual burden with handwritten labeling
- Bead-based cleanup introduced variability and required frequent intervention
- Automation increased setup time, runtime, and contamination risk
- Limited auditability and sample tracking
What Was Breaking
The system was not limited by instrumentation. It was limited by workflow design.
Automation existed, but it did not reduce complexity — it redistributed it.
- Manual steps embedded within automated processes
- Cleanup chemistry required constant intervention and introduced variability
- Sample tracking relied on handwritten labeling
- High-cost CLS time used for routine execution
- Workflow design prevented scaling without adding headcount
As a result, throughput, cost, and reliability were constrained by the system, not the technology.
System-Level Approach
Redesigned the workflow to remove failure-prone steps, reduce complexity, and enable single-operator execution.
- Replaced bead-based cleanup with 96-well filtration plate workflow
- Optimized automation to reduce setup time, runtime, and deck movement
- Reduced flyover and contamination risk
- Implemented barcode-based tracking to replace handwritten labeling
- Shifted execution to technician with targeted CLS witnessing
Outcomes
Increased throughput while reducing reliance on high-cost labor.
- Throughput increased from 96/week to 192/day
- CLS hands-on time reduced from ~2 hours to 5 minutes
- Total hands-on time reduced to ~45 min (tech) + 5 min (CLS)
- Maintained single-operator workflow
In addition:
- Removed variability from bead-based cleanup
- Reduced contamination risk
- Improved auditability through barcode tracking
- Reduced operational complexity and training burden
Why This Worked
The improvement did not come from adding automation. It came from redesigning the workflow as a system. High-impact changes were made at the points where variability, cost, and complexity lived.
- Simplified cleanup chemistry to remove a major failure mode
- Optimized automation to increase throughput, reduce unnecessary movement and intervention
- Aligned labor model to critical control points
By addressing assay design, automation, and operator workflow together, the system became both more scalable and more reliable.
This enabled a step-change increase in throughput without increasing headcount or operational risk.