The Future of Clinical Trials Starts With Reality
- 22 hours ago
- 3 min read
Updated: 34 minutes ago
Modernize for the World that Exists
Chuck Piccirillo
Zealic Health, CEO
In life sciences—across both clinical and commercial—the digital environment is crowded with systems that were meant to help, but often create more burden than benefit. Nowhere is this more apparent than at clinical trial sites.
Reality: Patchwork Tools & Duplicate Data Entry
Sites are asked to operate inside a patchwork of tools: sponsor systems, CRO systems, site systems, spreadsheets, and sometimes paper. And because those systems rarely connect cleanly (if at all), the same information gets entered repeatedly in multiple places.
That duplication doesn’t just consume time—it introduces unintentional errors. Those errors then require additional edit checks, rework, and oversight to catch and correct. This is necessary for trial quality, but it also creates a hidden operational tax on the people closest to the patient.
And the complexity doesn’t stop there.
Reality: High Degree of Variability across Studies, Sponsors, Sites, CROs
Every trial brings variation in protocol design, visit schedules, workflows, and reporting requirements. Contracts and invoicing can vary just as much (anyone who has dealt with unit grids knows this pain). And operational best practices differ across sites, CROs, and sponsors.
Reality: Individuals work differently
And variability doesn’t just exist across studies and sites—it exists across the people doing the work. Some users live at a desk. Others are moving through patient visits all day. And computer literacy ranges widely. Any system that assumes a single user type or a single workflow will break down in the real world.
In other words: variability isn’t the exception—it’s the operating environment.
Yet most life science software is built for a single purpose. It tends to be rigid, slow to change, and difficult to adapt—whether to a sponsor’s uniqueness, a site’s reality, or a study’s evolving needs. In a world where other industries receive continuous improvements and new capabilities, life science technology often lags behind the pace of operational change.
Reality: AI is exciting and confusing organization
Now add one more layer: AI.
AI is moving quickly, and it’s creating both excitement and confusion. Many organizations feel pressure to “wait for the next thing,” worried that any investment today could become obsolete tomorrow. That hesitation is understandable—but it can also freeze progress at exactly the moment when sites and studies need relief the most.
The truth is: AI can be transformative, but only when it is surrounded by a well-engineered system that can scale, integrate, and evolve. AI is not a replacement for good operational design. It’s an accelerator—when the foundation is built correctly.
While we all want consolidation, standardization, and consistency across the ecosystem, the reality is that level of alignment isn’t coming anytime soon—so we have to build for the world as it actually operates today.
So if this is the reality—and it’s not changing anytime soon—what should sites, sponsors, and CROs do?
The answer is not to add yet another siloed tool.
How to Win Today
The answer is to partner with companies engineered for the world as it actually works:
Rapid to deploy, without months of setup and retraining
Easy to integrate into the ecosystem that exists today (and will continue to evolve)
Flexible enough to accommodate site-to-site and study-to-study variability
Designed around how people actually work, whether at a desk or on the go with patients
A single, intuitive point of interface across the ecosystem—so users can find, access, and navigate the tools and information they need without jumping between portals, logins, and workflows
Able to adopt AI thoughtfully over time, without forcing it into every study—and without creating a rip-and-replace situation later
Because in life sciences, the winners won’t be the organizations with the most software.
They’ll be the ones with the most adaptable operating model—and the technology partners built to support it.