The lab director's dilemma: Why workflow matters as much as performance

When laboratory directors evaluate new platforms, the conversation almost always begins with performance metrics — sensitivity, specificity, and concordance with reference methods. These numbers matter enormously. They represent years of development, rigorous validation, and the analytical foundation that everything else depends on. 

But in over two decades of working in life sciences — across molecular biology, oncology and immunology — I've observed a pattern that rarely gets discussed in product launches or peer-reviewed papers: the best-performing technology doesn't always win in the real world. Adoption is decided not in a publication, but at the bench. 

Laboratories today are operating under pressure from multiple directions at once. Patient volumes are rising. Reimbursement rates are under sustained pressure from regulatory changes,¹ and staffing shortages continue to strain operations at institutions of all sizes.² In this environment, workflow efficiency has moved from a secondary consideration to a strategic imperative. 

Performance gets the headlines. Workflow pays the bills. 

When lab directors evaluate a new platform, they are not simply asking whether it performs well. They are running a much more complicated calculation:

• How many samples can we process per shift?
• Does the turnaround time fit our reporting cycle?
• How many people does it take to run, and how long does it take to get the test up and running? Does it connect to our laboratory information management system, or does it introduce manual steps?
• And critically — does the workflow hold up under real volume, not just ideal conditions? 

These are not peripheral questions. They are central to whether a technology can be implemented sustainably — and whether it will still be in use two years after installation. 

A platform that requires specialized pre-treatment steps, a steep learning curve, or constant troubleshooting will quietly stall — or prevent — adoption, regardless of how impressive its analytical data looks. In practice, a few aspects of workflow tend to be consistently underestimated, and I've seen this play out repeatedly across research and commercial settings. A platform that performs well analytically can ultimately be shelved because onboarding requires weeks of training and continuous troubleshooting across rotating staff. Labs choose solutions not just because they perform well, but also because they can be incorporated easily into the existing laboratory environment. 

The hidden cost of complexity 

The gap between what works in a controlled research study and what works across shifts in a production laboratory is significant and consistently underestimated.³ A validation study conducted with dedicated personnel under ideal conditions may not reflect the experience of a mid-size hospital lab running the same platform with a rotating staff and a LIMS integration that took months to configure. 

This is not a criticism of innovation. It is a structural reality of translating technology into clinical operations. The solution, whether a new immunoassay, a molecular workflow, or a targeted proteomic application has to be designed with the operational environment in mind from the beginning, not just adapted after the fact. 

In practice, a few aspects of workflow complexity tend to be consistently underweighted in pre-launch evaluations: 

Hands-on time versus total run time. A platform may report a 90-minute run, but what matters to a lab director is total hands-on time per batch — including preparation, loading, quality checks, and data review. These steps are often left out of published performance summaries. 

Scalability in both directions. A system optimized for high-throughput runs may become impractical when only a handful of samples need to be processed urgently. A system designed for low volume may not scale to meet peak demand. Performance and cost per sample across realistic testing volumes is as relevant as peak throughput. 

Integration burden. LIMS integration is frequently listed as a capability but rarely described in terms of what implementation actually involves. The simpler the better; complex assay outputs can significantly increase the effort required for validation and integration. 

Onboarding and ongoing training requirements. Staff turnover in clinical laboratories remains a persistent operational challenge.² A platform that demands extensive training to operate or specialized complex equipment introduces recurring risk as personnel change. The training burden should be evaluated not just at implementation, but as an ongoing operational cost. 

A framework for workflow evaluation 

As platforms grow more sophisticated — incorporating multi-analyte targeted proteomic analysis, AI-assisted interpretation, and automated data outputs — the workflow evaluation becomes more important and more complex. The potential efficiency gains are real. But so is the risk of introducing systems that create new dependencies and failure points. 

Lab directors and procurement teams would benefit from building operational questions into standard platform evaluations, alongside the analytical data. In practice, that means asking vendors things that rarely appear on specification sheets: 

What does a typical day actually look like for the person running this? Not the scenario in the training manual — a realistic account of the steps involved, where errors occur, and how the system handles exceptions. 

Where do problems emerge at volume, including fluctuating volumes? A platform that runs smoothly with ten samples may behave differently at ninety. Understanding failure modes at scale — whether that means reagent management, instrument maintenance cycles, or data review bottlenecks — is information every lab needs before committing to implementation. 

What does onboarding actually require beyond what the product documentation describes? Evaluate personnel time and institutional resources. Ask for references from comparable institutions and speak with laboratory staff rather than management; this tends to yield the most honest picture. 

These questions are not adversarial. They are the foundation of a productive vendor relationship and ensure mutual success for both the lab and assay developer. A manufacturer should be able to clearly communicate these points. 

Designing for the lab, not the brochure 

There is a phrase that captures the challenge: a solution can be designed for the lab, or it can be designed for the brochure. The distinction rarely shows up in the specifications — it shows up in the experience of the people running it every day.³ 

As laboratory medicine continues to evolve toward higher complexity and more demanding performance requirements, the assumption that workflows will sort themselves out after adoption is increasingly untenable. The operational environment is too constrained, and the consequences of a poor implementation — in staff time, result turnaround, patient impact, and institutional cost — are too significant. 

The most successful platform introductions share a common characteristic: the manufacturer understood the lab's operational reality before the product was finalized. 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. 

For laboratory directors evaluating new technologies, the analytical data is the starting point. The cost and workflow discussions are where the real decision gets made. 

References

1. Centers for Medicare & Medicaid Services. Clinical Laboratory Fee Schedule. Updated 2025. Accessed April 2026. https://www.cms.gov/medicare/payment/fee-schedules/clinical-laboratory.
2. Garcia E, Kundu I, Ali A, Soles R. The American Society for Clinical Pathology's 2021 Vacancy Survey of Medical Laboratories in the United States. Am J Clin Pathol. 2022;157(6):874–889. 
3. Nichols JH. Laboratory quality control based on risk management. Ann Saudi Med. 2011;31(3):223–228.