No More Backward Thinking: How to Evolve Biometrics for Today’s Clinical Complexity 

I have spent nearly three decades in the biometrics and clinical research space, and I can say with confidence that we are living through one of the most transformative periods our industry has ever experienced. Today’s trials generate more data than ever before, and the richness of information available to us contains incredible potential for furthering patient outcomes and scientific discovery.  

The dramatic increase in data would be overwhelming if not for the fact that the technology supporting clinical development is evolving at pace. Advanced analytics platforms, automation capabilities, and AI-enabled tools are creating new opportunities to manage, interpret, and act on data in ways that were unimaginable a decade ago. 

But a gap is emerging between what is technologically possible and what organizations are operationally prepared to absorb. Bridging that gap and empowering your clinical data management and operations teams to manage an increasing amount of data requires alignment across people, process, and technology. It demands a very deliberate approach to change.

 

Change Is Hard. Here Are the Main Obstacles.  

Our industry is comfortable discussing logical and technological barriers. We are less comfortable discussing human and operational ones. Yet, in my experience, overcoming the human and operational barriers are the real determinates of whether transformation succeeds. 

The main obstacles I’ve witnessed fall into three categories. 

1. Trust issues 

Clinical research professionals are trained to be rigorous, evidence-driven, and risk-aware. That mindset is essential to protecting patients and ensuring data integrity, but it can make adapting to and embracing technological change more difficult.  

Then there’s the additional issue of trust: We trust that our old processes work, and we’re skeptical of new processes that are yet unproven. For us, trust must be earned, particularly when automation and AI are involved.  

2. Limited resources 

It’s a rare organization that undergoes a digital transformation from a position of surplus. If your teams are like mine, they’re managing aggressive timelines, constrained budgets, and increasing study complexity, while at the same time being asked to innovate. 

There’s very little opportunity to “pause and redesign.” In many cases, the same teams responsible for delivering trials today are also responsible for implementing the technologies that will define how they deliver trials tomorrow. 

3. Rigid structures 

Many clinical development processes were built for a different era where there were fewer data sources, simpler trial designs, and more linear workflows. Again, we trust these processes. They worked for us in the past. When rigid processes meet dynamic technology, friction is inevitable.  

Enabling Technology Adoption That Drives Impact 

Unless people and processes evolve in tandem, organizations risk underutilizing the very tools designed to help them. So how do we close that gap?  

What we’ve discovered on the Biometrics Services team is that evolution happens through deliberate, disciplined shifts in three areas: mindset, process, and evaluation. For us, these shifts have led to important increases in new module adoption, employee engagement, clinical expertise, and financial growth. 

Below, I outline the steps we took to transform our use of technology: 

1. Start With People, Not With Tools 

Innovation starts with people, and it must be cultivated intentionally across teams. For us, that meant:  

• Aligning teams strategically by placing like-minded players together, allowing shared strengths to accelerate growth. 
• Creating accountability for continuous improvement, as well as recognizing and rewarding process optimization efforts.  
• Investing in soft skills like communication, listening, and collaboration, as these can often make or break the success of an initiative.  

Technical upskilling is essential, but transformation accelerates when individuals feel empowered to rethink how work gets done. It makes sense that normalizing curiosity and experimentation can reduce resistance before it takes root. 

2. Redesign Processes to Enable Agility 

You cannot deploy transformative technology into rigid, over-engineered processes and expect success. We had to take a hard look at our workflows and ask: 

• Are there unnecessary handoffs? 
• Are we creating manual checkpoints where automation could exist? 
• Are our controls enabling agility or preventing it? 

What we found was that imperfect humans plus unnecessary complexity equaled risk. We challenged each function to identify automation opportunities and then transformed our processes accordingly. 

3. Measure, Then Measure Again 

If you don’t measure progress, you’re operating on instinct. These days, with so much data at our fingertips, instinct alone isn’t enough. You’ve got to do the evaluative work. 

What we’ve done is implement dashboards and KPIs that allow us to evaluate performance across studies, adoption of technologies, efficiency improvements, and areas where more training or further alignment is needed.  

Sometimes data confirms progress. Sometimes it tells us we need to adjust. But either way, we are making decisions based on evidence.  

A Final Note for Leadership 

If you’re a leader in this space, I encourage you to challenge rigid processes that block agility and to be honest about your own emotional resistance to change. Address it.  

And last but not least, identify your champions of change. These are your people who drive change from within the organization. These folks will help you think forwards, because we cannot afford to evolve slowly while our industry continues to accelerate.  

Explore how our Biometrics Services team uses elluminate Clinical Data Cloud^® to optimize data management and decrease cycle times.