The Capability Gap: Why Your Laboratory’s Improvements aren’t Surviving the Year

You’ve seen it before.

Turnaround times creep past acceptable limits. A task force gets assembled. A Lean or Six Sigma project launches, and for a few months, the numbers look exactly the way they should.

Then the project champion moves on, attention shifts to the next priority, and within six months you’re back where you started.

It’s easy to blame staff turnover, budget pressure or a lack of buy-in. But the real problem is usually hiding somewhere else entirely.

Most laboratories have at least one. The person who stays late to manually push samples through a bottleneck. The one who knows every workaround in the system and uses all of them, every shift.

For a while, it works. Until it doesn’t.

When that person burns out, goes on leave, or moves into another role, the delays come back. Because the process was never actually fixed, it was just held together by someone willing to absorb the dysfunction.

Dr. W. Edwards Deming spent decades arguing that most operational problems are driven by systems, not individuals.

Yet when improvements fail, our first instinct is still to retrain the people rather than redesign the environment they’re working in. We send staff on Lean courses, and they return to the same broken system that created the problem in the first place.

Laboratory test results inform around 70% of critical medical decisions. That’s a significant responsibility – and it makes getting the operational side right genuinely important.

Here’s what makes it complicated: most laboratory errors don’t happen inside the analyzer. Research consistently shows that between 46% and 68% of all errors occur during the pre-analytical phase, and nearly 75% of delayed reports are linked to factors outside the analytical process entirely.

This means many laboratories are investing heavily in analytical speed, while the real problems occur upstream, before the sample even reaches the machine.

A world-class analyzer cannot fix a broken intake process.

“If we just automate this section, our turnaround time problems will disappear.”

It’s one of the most common things laboratory managers say. And one of the most expensive assumptions to get wrong.

Automation doesn’t fix inefficient workflows. It accelerates them. If the process is broken before you automate it, you’ll simply reach the bottleneck faster than before.

Before technology can do its job, high-performing laboratories typically focus on three things: mapping the entire operational process end-to-end, identifying where the true constraint lives (which is often not where the symptoms appear), and simplifying and stabilizing workflows before introducing anything new.

Technology is a multiplier. It makes strong systems faster. It also makes weak ones worse.

Join us for our upcoming Activation Session, where we’ll break down the anatomy of a failed improvement project and walk through practical approaches to building lasting laboratory capability. It’s a conversation worth having.

Van Eetvelde, G. (2012). 70% of hospital strategic initiatives fail: How hospitals can avoid those failures. Becker’s Hospital Review.

Kazzaz, Y. (2023). The lens of profound knowledge. Global Journal on Quality and Safety in Healthcare, 6(3), 96-98. https://doi.org/10.36401/JQSH-23-X3

Hallworth, M. J. (2011). The “70% claim”: What is the evidence base? Annals of Clinical Biochemistry, 48(5), 487-488.

Plebani, M. (2006). Errors in clinical laboratories or errors in laboratory medicine? Clinical Chemistry and Laboratory Medicine, 44(6), 750-759.

Bhatt, R. D., Shah, D. S., Shah, M. K., & Shah, P. K. (2019). Factors affecting turnaround time in the clinical laboratory of Kathmandu University Hospital, Nepal. eJIFCC, 30(1), 14-24.

Wallask, S. (2024). Top challenges facing labs center on budget and staff. Lab Manager.