The Technology Trap: Why New Systems Fail to Fix Old Problems.

Clinical laboratories have never had as much access to sophisticated technology as now. There are many examples. Automation can process thousands of samples every hour. Machine learning is increasingly supporting diagnostic workflows. Modern LIMS platforms connect instruments, data, and reporting in ways unimaginable only a decade ago. Despite these advances, many laboratories still struggle with inconsistent turnaround times, staffing pressures, and persistent systemic delays.

So, technology has advanced rapidly. Operational performance often still lags. The desire to assume that better technology always delivers better performance is understandable. Unfortunately, the evidence suggests otherwise.

The Automation Paradox

A study published in the Journal of Laboratory Automation evaluated the implementation of total laboratory automation in a large clinical laboratory. With a straightforward expectation: automation would enhance operational performance by default.

It didn’t happen. Improvements in turnaround time, laboratory errors, and staff morale “were not evident” after implementation¹.

This illustrates what we describe as the automation paradox.

Laboratories habitually invest heavily in optimizing the analytical phase of testing. But an estimated 46% to 75% of laboratory errors occur before analysis even starts².

Automation excels when every sample arrives correctly labelled, in the appropriate container and in good condition.

Daily laboratory operations are rarely that predictable.

Delayed samples, incorrect tubes and haemolysed specimens require manual intervention, regardless of how sophisticated the automation process thereafter may be. In such cases, technology is only solving one part of the system, while many of the biggest operational challenges occur elsewhere.

The key issue is that technology doesn’t exist in isolation.

Every new LIMS, automation line, or digital workflow has to align with the realities of day-to-day laboratory operations.

When it doesn’t, people adapt. Then they create workarounds4.

Most of these informal fixes are well-intentioned. They’re developed to maintain patient care or keep work moving. But over time they introduce unnecessary variation, increase operational risk, and reduce the consistency that standardized systems are designed to primarily achieve.

This challenge extends beyond laboratory medicine. Research from organizations including McKinsey, BCG and Gartner consistently indicates that a significant proportion of digital transformation initiatives fail to achieve their intended outcomes³.

The technology usually isn’t the problem. Implementation is.

Organizations often digitize existing processes without first asking whether those processes are fit for purpose.

Another common oversight is the assumption that technology will always be available.

Dr Joe El-Khoury from Yale School of Medicine has highlighted this challenge in discussions around total laboratory automation⁵.

Implementation plans typically focus on normal operating conditions.

But what happens when a module fails?

What happens during maintenance?

What happens when the unexpected occurs?

Highly reliable laboratories answer those questions before problems arise.

They design contingency plans, maintain manual fallback procedures, and ensure staff remains capable of working safely without automation when necessary.

Technology should improve resilience. And it should never become a single point of failure.

If technology alone isn’t the answer, then what is? Many of the world’s safest organizations, including aviation, nuclear power, and leading healthcare systems, operate according to High Reliability Organization (HRO) principles.

Instead of just focusing solely on technology, they focus on how the entire workflow operates.

According to the Agency for Healthcare Research and Quality, HROs share five characteristics:

• Preoccupation with failure
• Reluctance to simplify interpretations
• Sensitivity to operations
• Commitment to resilience
• Deference to expertise⁶

Notice what isn’t included.

No guideline says: “Buy better software.”

Technology supports these organizations, yet it never defines them.

Laboratories seeking sustained operational improvement should adopt the same mindset. Technology is an important investment, but it cannot compensate for inadequate system design.

Long-term performance comes from capable people, resilient processes, and technology that supports both.

Join us for our upcoming Activation Session, where we’ll explore the technology trap in greater detail and discuss how High Reliability Organization principles can be applied to laboratory medicine.

[1] Lam, C. W., & Jacob, E. (2012). Implementing a laboratory automation system: Experience of a large clinical laboratory. SLAS Technology (Journal of Laboratory Automation), 17(1), 16–23. https://doi.org/10.1177/2211068211430186

[2] Plebani, M. (2006). Errors in clinical laboratories or errors in laboratory medicine? Clinical Chemistry and Laboratory Medicine, 44(6), 750–759. https://doi.org/10.1515/CCLM.2006.123

[3] MeltingSpot. (2025). Why 70% of digital transformation projects still fail in 2026.

[4] Barrett, A. K. (2018). Technological appropriations as workarounds: Integrating electronic health records and adaptive structuration theory research. Information Technology & People, 31(2), 368–387. https://doi.org/10.1108/ITP-01-2016-0023

[5] El-Khoury, J. M. (2024). Breaking the chain: Navigating the pitfalls of total laboratory automation. The Journal of Applied Laboratory Medicine, 9(5), 1095–1096. https://doi.org/10.1093/jalm/jfae061

[6] Vogus, T., Lee, M., & Mossburg, S. E. (2025). High reliability organization (HRO) principles and patient safety. PSNet, Agency for Healthcare Research and Quality.

[7] World Health Organization. (2019). Laboratory leadership competency framework (WHO/WHE/CPI/2019.3). World Health Organization.