DCT Benefits Are Proven, Yet Progress Is Slow
By Dan Schell, Chief Editor, Clinical Leader
When you look strictly at the data, decentralized clinical trials (DCTs) should already be the industry’s default setting. They bring in more participants, keep them around longer, and widen the door for populations we have historically under‑enrolled. As Sunny Kumar, MD, partner at Informed Ventures, reminded me, “Decentralized trials are able to recruit more patients, they’re able to recruit more diverse patients, and that’s absolutely been proven out in the literature [see the bottom of the page for a list of some recent reports Kumar referenced].” The Tufts CSDD study he and I discussed even pegs the return on investment at up to 13‑to‑1 for phase 3 studies. Despite those eye‑popping numbers, only a thin sliver of sponsors run more than a handful of protocols with meaningful decentralized components.
Cost And Change Management Keep Sponsors On The Fence
Kumar’s blunt diagnosis is that money and mindset are the twin handbrakes. Transforming a top‑10 pharma’s legacy stack into a DCT‑ready engine means wiring together eConsent, eCOA, ePRO, home nursing, and tele‑visits, then training every department to use them. That exercise carries “tens of millions” in upfront spend before the first virtual visit ever happens. Even companies that can afford the invoice still have to push the cultural boulder: therapeutic‑area heads must believe a more digital model will not torpedo timelines, compliance, or their own comfort level. Until that conviction spreads, DCT deployment will stay lumpy — heavy in a few forward‑leaning programs, spotty everywhere else.
AI Applications & TAs Matter
In February of last year, I talked with Kumar about AI’s role in advancing clinical trials. For this DCT discussion, he cited AI as one of the keys to unlocking the real potential of these decentralized components. “AI is reducing that up-front cost and commitment by reducing the number of folks you need to implement these trials, and the time needed to build and construct these trials,” he said. Study builds that once took a season can now wrap in two weeks, and automated translations slash a translation budget that used to rival a small country’s GDP. With those kinds of savings, expect the traditional “too expensive” argument to fall apart fast.
But Therapeutic area still matters. Straightforward, widely distributed conditions such as obesity trials with GLP‑1s are tailor‑made for virtual follow‑ups and home‑reported outcomes. On the opposite end, complex oncology biologics lean on DCT tools to trim, not replace, on‑site visits. Fewer trips for labs and imaging still translate into happier patients and longer retention. Phase makes a difference, too. DCTs tend to make more economic sense in later-phase trials, where large patient numbers and longer timelines help offset the initial digital investment. Kumar thinks AI’s efficiency gains could nudge Phase 1 trials into the viable column within two years, but we are not there yet.
More Diversity May Mean More Work
DCT evangelists love the diversity upside of these trials, yet there is a caveat. Shipping a provisioned smartphone to a rural participant is easy. Ensuring that same volunteer stays engaged for 18 months while juggling spotty broadband and limited clinic access is not. Kumar worries that if sponsors retreat from DCTs because of cost jitters, rural and lower‑income communities will be the first to lose their newfound seat at the research table. Continuous investment in patient education, community health partners, and bandwidth solutions has to run parallel with the shiny tech stack, or the equity gains we keep citing will evaporate.
The Regulatory And Investor Weathervane
Everyone is waiting for the FDA to settle on a clear posture toward AI‑assisted trial design, synthetic control arms, and other shortcuts that could super‑charge DCT ROI. Guidance documents appear and mutate faster than a pharma Reddit rumor, so most sponsors have chosen the time‑honored strategy of “call me when the dust clears.” Investors are equally cautious; venture money is still flowing, but the check writers want evidence that their shiny platform will survive whatever policy lands next. Kumar’s read is that the agency’s long‑term direction still favors faster, smarter trials, yet the short‑term noise keeps capital on a shorter leash.
If history is our guide, the combination of ROI pressure, maturing AI tools, and FDA nudges will push decentralized elements into the clinical mainstream — just not overnight. Think incremental wins: a Phase 3 diabetes study that drops half its scheduled site visits or an oncology trial that swaps half its data captures for eCOA. As those examples pile up, TA leaders will have fewer reasons to cling to the “old way” of doing things. And once CFOs see a double‑digit multiple on saved costs, the “optional” label on DCT infrastructure will disappear.
Do I expect every trial to be fully decentralized? No. Do I expect the line between a traditional and a hybrid study to blur so badly that the distinction stops mattering? Absolutely. I think we see plenty of this already. The key variable is whether sponsors embrace the tech — and the change management — that lets DCTs deliver on the promise we have been writing about since 2020. And thanks to AI’s rapid‑fire progress, that future may finally show up on time.
References provided by Sunny Kumar, MD: