When laboratory directors evaluate a new platform, the conversation almost always starts with performance metrics—sensitivity, specificity, and concordance with reference methods. Those numbers matter. But after more than two decades in life sciences, I've noticed a pattern that rarely comes up in product launches or publications: the best-performing technology doesn't always win. Adoption is decided at the bench, not in a white paper.
Labs are under real pressure right now. Volumes are rising, reimbursement rates are under sustained pressure, and staffing shortages aren't going away. In that environment, workflow efficiency isn't a secondary concern—it's a strategic one.
Performance gets the headlines. Workflow pays the bills
When a lab director evaluates a new platform, they're not just asking whether it performs well analytically. They're calculating something far more complicated: How many samples can we run per shift? Does the turnaround fit our reporting cycle? How long does onboarding take? Will it connect to our LIMS, or are we looking at manual workarounds?
These questions determine whether a technology can be implemented sustainably, as well as whether it will still be running two years after installation. A platform that requires complex pre-treatment steps, a steep learning curve, or constant troubleshooting will stall adoption no matter how strong the data. I've seen it happen more than once: a platform that performs well analytically ends up shelved because onboarding across rotating staff and workflow was too resource-intensive to sustain.
The hidden cost of complexity
A few dimensions of workflow tend to be consistently underestimated in pre-launch evaluations:
Hands-on time vs total run time
A 90-minute run time sounds efficient, but total hands-on time per batch (prep, loading, quality checks, and data review) often tells a different story. That's the number that matters for staffing.
Scalability in both directions
A platform optimized for high throughput may not be practical when you need results from a handful of samples urgently. Understanding performance across the realistic volume range matters as much as peak throughput.
Onboarding as an ongoing cost
Staff turnover in clinical labs is persistent. A platform that requires extensive training introduces recurring risk every time someone new joins the team. The training burden doesn't end at implementation.
3 key questions to ask vendors
Lab directors can strengthen their platform evaluations by adding a few operational questions alongside the usual analytical checklist:
1. What does a typical day actually look like for the person running this?
Not the training manual scenario—the realistic version, including where errors tend to occur and how the system handles exceptions.
2. Where do problems emerge at volume?
A platform that runs smoothly at 10 samples may behave differently at 90. Ask specifically about failure modes at both ends and of the scale.
3. What does onboarding actually require, and what does the cost per test look like at your volume?
Beyond what's in the documentation, understanding the total cost of running a platform at realistic scale, not just list price, is essential. References from comparable institutions give the most accurate picture.
A manufacturer that can answer these questions clearly is worth working with. One that can't is also communicating something important.
Designed for the lab, not the brochure
The most successful platform introductions I've been part of had one thing in common: the manufacturer understood the lab's operational reality before finalizing the product. That requires a different kind of conversation—one that starts not with what the platform can do, but with what the lab actually needs it to do.
Analytical data is the starting point. The workflow conversation is where the final decision is made.
