Customer Story: A National-Scale Molecular Lab
As a national-scale molecular lab expanded its testing capabilities, years of accumulated complexity had turned manual data entry and QA handoffs into operational bottlenecks.
- Onymos Team
- 1 min read
- Higher processing throughput
- 9×
- Per specimen, down from 27 minutes
- 2m 43s
Operational complexity rarely appears overnight. As laboratories introduce new molecular tests, they also introduce new internal processes, documentation requirements, data fields, validation rules, and dependencies.
Each addition may seem manageable on its own, but their cumulative effect can gradually turn efficient workflows into significant operational bottlenecks.
Requirements: Simplifying workflows without compromising quality
This was the challenge facing one national-scale molecular lab, whose expanding test portfolio had gradually made its laboratory intake operations more complicated, labor-intensive, and expensive.
Onymos DocKnow needed to solve:
- Duplicated work: Manual double-data entry consumed significant staff time.
- Too many handoffs: Moving cases between employees for data entry, correction, and approval introduced waiting time and made processing dependent on staff availability.
- Complicated validation: Each test could have different data requirements, making it difficult to standardize how information was reviewed and verified.
- Limited scalability: Increasing test volumes meant increasing the amount of administrative work, potentially requiring additional personnel even when testing infrastructure had capacity.
Results: 90% less processing time
With DocKnow, the lab introduced automation to TRF and medical-record processing across its laboratories, reducing the manual effort required for data extraction, validation, and review.
Rather than relying on employees to repeatedly transcribe and verify information, the new approach automated much of the work that had previously created bottlenecks.
The impact was substantial.
| Metric | Before DocKnow | After DocKnow |
|---|---|---|
| Processing time per specimen | 27 minutes | 2 minutes, 43 seconds |
| Processing throughput | Baseline | 9× higher |
Overall processing time fell by approximately 90%, reducing a 27-minute workflow to just 2 minutes and 43 seconds per specimen.
The improvements also addressed the underlying problem of operational complexity.
Instead of continuing to layer additional administrative work onto existing processes, the organization now has an intake system that can keep pace with its growth.
