Clinical Trial Sites Don't Need More Tech, They Need Fewer Silos

By John Oncea, Chief Editor, Clinical Tech Leader

For two decades, clinical research has treated new technology as the fix for what ails trial execution. EDC, eSource, CTMS platforms, IVRS systems, patient engagement apps, and sponsor portals have all been added to the site’s toolkit with the promise of faster, cleaner, more efficient studies.

Unfortunately, according to Nick Spittal, Chief Operations Officer, and Raghu Punnamraju, Chief Technology Officer, both of Velocity Clinical Research, that promise has only been half-delivered. The industry doesn’t have a shortage of technology. It has a shortage of technology that talks to itself.

“There’s a longstanding mantra in the industry about the proliferation of tech,” Spittal said. Rather than framing that proliferation as a burden, he sees it as a fact of doing business in clinical research today, one that puts the responsibility on organizations like Velocity to make the resulting complexity invisible to staff and patients alike.

It’s a familiar tension for anyone who has worked at a site: EDC systems have been standard practice for more than 20 years, yet a coordinator can still spend a meaningful part of their day moving the same piece of information from one screen to another by hand. The tools have multiplied faster than the connections between them.

When A Coordinator Becomes The Integration Layer

Punnamraju offered the clearest illustration of the problem: a single day spent shadowing a research coordinator during a routine screening visit.

The coordinator logged into 12 separate systems to complete that one visit. Punnamraju counted roughly 29 discrete steps across the encounter, about 17 of them system-facing (logging in, transcribing, uploading, cross-checking) and 12 of them participant-facing.

What struck him wasn’t the number of systems. It was how the coordinator handled it. “She really made sure the participant was engaged,” Punnamraju said, describing how she maintained eye contact and rapport with the patient while moving between a laptop, a scanner, and an eSource system that required real-time data entry. By the end of the visit, she showed no frustration. It was, in her words, simply “part of the day.”

That normalization is the real story, according to Punnamraju. Coordinators have become so accustomed to manually translating information between platforms – a CTMS here, an IVRS system there, a sponsor’s own eSource tool layered on top – that the workaround has become invisible even to the people doing it every day. “There is an opportunity there where technology can aid that translation,” he said, “and maybe even take over some of it in a controlled manner.”

Why Interoperability Still Isn’t Solved

Sites aren’t the bottleneck, Spittal argued. If anything, he said he’s been surprised by how technologically capable research staff have become, given how little standardization exists across the systems they’re asked to use.

The deeper issue, he said, is that most clinical trial technology was built to solve a single organization’s problem rather than to function as part of a shared ecosystem. A sponsor or CRO introducing a new eSource product may have every intention of improving data quality and reducing entry time. But that product doesn’t arrive in a vacuum; it lands on top of a site’s existing CTMS, its own eSource platform, and years of built-up procedure.

“The data we put in our eSource system are informing our CTMS, which is allowing us to recruit patients for the very study that you’re coming and asking us to execute,” Spittal said, describing the tension sites face when a well-intentioned new tool doesn’t account for what it’s replacing or duplicating.

The fragmentation extends beyond any single site’s workflow. Spittal pointed to a second, related problem: sites routinely get pushed on performance metrics – enrollment pace, retention, protocol deviation rates – without having visibility into how they compare to other sites working the same trial, or the same therapeutic area more broadly. “It’s one of the rare places I can come up with in any industry where you don’t actually know how you’re doing a lot of the time,” he said.

The Cost Of Fragmentation

Every additional login, every duplicate data entry, and every manual handoff between systems is a small tax on a coordinator’s day. Multiplied across a site’s patient volume and a network’s site count, that tax becomes a meaningful drag on execution speed, and on the time investigators and coordinators have available for direct patient care.

Both Spittal and Punnamraju returned repeatedly to that framing throughout the conversation: technology’s job is to protect the time staff spends with patients, not compete for it. Every minute spent translating data between systems is a minute not spent on the human side of the work, the side neither executive believes AI or automation will ever fully replace.

The Future Is Invisible Technology

Notably, neither Spittal nor Punnamraju argued for less technology. They argued for technology that disappears.

The vision both described is one where systems exchange data automatically, and coordinators stop functioning as translators between platforms; where an EDC talks directly to an IVRS system, or an eSource tool feeds a CTMS without a human retyping the same values twice. Punnamraju was direct about where that leaves the industry’s collective task: sites, sponsors, CROs, and technology vendors need to solve interoperability together, rather than each building point solutions that solve their own piece of the problem while adding a new login to everyone else’s day.

That’s a harder problem than adding another tool to the stack, and it’s not one single sponsor, CRO, or site can solve alone. But for an industry that has spent 20-plus years digitizing trial execution one system at a time, it may be the only problem left worth solving.

For sponsors and CROs evaluating site networks, Spittal and Punnamraju’s framing offers a useful diagnostic question: not “which technologies does this site use,” but “how much of a coordinator’s day is spent making those technologies talk to each other.” Clinical research doesn’t need fewer digital tools. It needs a technology ecosystem that behaves as though there’s only one.

Once that data begins flowing across systems instead of getting re-typed between them, a further question follows: can that same information be used not just to describe what already happened at a site, but to predict how a study will perform before it even starts?