Kelsey Jakee, Senior Director, Portfolio Strategy & Innovation at TransCelerate BioPharma, argues why advances in clinical R&D won’t solely be dependent on technology. 

Every few years, the clinical research industry rallies around a new wave of innovation. From decentralised trials and digital biomarkers to AI-driven automation, each wave brings genuine capability and promise. Yet foundational problems like complex protocols, slow enrollment, constrained site capacity and fragmented data tend to persist through each cycle, largely unchanged. The tools evolve, but the operating blueprint doesn’t. 

Well-intentioned attempts to fix old problems often lack the systems thinking—a holistic examination of interdependencies and human touchpoints—required to address them sufficiently. New technology is layered on top of existing processes and we miss an opportunity to reimagine our approach altogether. And it is understandable why we opt out. The time and cost to bring a drug to market, not to mention the cost of failure, is enormous. In this context, there is a gravitational pull toward familiar approaches with new band-aids: doubling down on more data, more technology, more complexity.  

Innovation also tends to be reactive. The pandemic is the clearest example: The industry had been discussing decentralised trials for years, but it took a crisis to engage in the kind of problem solving that made decentralisation possible on a broader scale. We innovated because we had no choice. And when the acute pressure subsided, much of the industry reverted to old ways of working. 

This pattern has downstream effects across the clinical trial enterprise, posing a risk to patient and site participation, missing an opportunity to better predict quality, and most importantly, limiting how quickly we can deliver better answers to patients. 

Why the operating blueprint matters more than the technology 

At the site level, the effects are tangible. Every new trial is its own monumental effort. Each one comes with a new protocol, a one-off set of systems and logins and unique training requirements that must somehow be cast onto a site’s established systems and workflow. For sites running multiple studies concurrently, the cumulative weight of this variability adds up study after study, day after day. 

That weight would be easier to accept if all of it were necessary, but some of it isn’t. There is an important difference between burden that contributes to the quality and integrity of a study and burden that doesn’t. Investigators building relationships with the patient community, ensuring adherence and retention throughout the life of a trial: that work is demanding, but it serves a clear purpose. Spending evenings signing off on CRFs in bulk, taking redundant training, following through on administrative requirements that exist because they’ve always existed: that’s burden without purpose. And it is unsustainable. 

Data fragmentation follows a similar logic. Many studies capture source data in multiple places. Each one defines and stores data according to its own internal logic (in some cases, still using paper). Moving it from one system to another requires significant effort, translation and cost. The technology to enable standards-based data capture and exchange across systems exists today. Adopting it at scale is a different challenge, one that requires organisations to shift toward new infrastructure while simultaneously operating within the default landscape. This requires decision-makers to dedicate investment and trailblazers to stick their necks out to execute. It’s fundamentally driven by people feeling comfortable taking a calculated risk. 

AI is another area where the picture is more complicated than the headlines suggest. It can help locate a patient who may be eligible for a trial. It cannot build the trust that convinces them to enroll or sustain the relationship that keeps them engaged when the burden feels too high. Behind every trial are human beings making personal decisions about their care, and staff making authentic connections with trial participants. As investment in AI-enabled tools accelerates, we should ask whether equal attention is being paid to the people who will continue to make trials work on the ground. 

Principles for moving beyond the status quo 

If the goal is to move from optimising within the current state to more disruptive, structural improvement, we must be willing to prioritise several principles: 

• Human-first: Trial participants have made a significant personal decision to contribute to research, and the quality of their experience, from first contact through to return of results, is shaped far more by the people they interact with than by the platforms they use. Any modernisation effort that treats the human side of clinical research as secondary is solving only half the problem. 

• Site-centric: Following the human-first principle, site-centricity works in concert with patient centricity, as the two reinforce each other. When we seek patient input on design without also understanding the operational implications for sites, patients are likely to feel the effects: lack of preparedness, visible friction, and a perception of chaos. The goal is to design studies that are realistic to deliver and that don’t compete with sites’ existing workflows. This means early collaboration and two-way transparency.

• Invisible foundation: Some of the most transformative forces underpinning clinical research will also be the dullest. This includes standards for interoperability. These are the workhorses that do heavy lifting behind the scenes, providing the logic needed to reliably and accurately power studies across systems. TransCelerate’s Digital Data Flow (DDF) initiative offers one example. Rather than trying to improve upon the document-based paradigm for protocols, DDF supported CDISC’s work to develop a machine-readable study specification standard that is system-agnostic. An interoperability standard, even when it isn’t the most urgent item on anyone’s agenda, compounds in value over time as more systems adopt it. We didn’t develop a flashy hammer in search of a few nails, we developed plumbing that people forget about after it’s up and running. And that is the point.
  

The Case for Curiosity

There is a thread connecting these priorities, and it comes down to how the industry confronts its own assumptions. “The regulator won’t allow it.” “Sites won’t accept it.” “It will delay my timeline.” These refrains are familiar in clinical development, and while they’re sometimes grounded in legitimate constraints, they often go unchallenged and become accepted as settled conclusions rather than hypotheses worth testing. 

What if, instead of defaulting to why something can’t be done, we asked what would need to be true for it to be possible? The question forces us to identify the specific constraints and the stakeholders whose partnership we would need to address collaboratively. The answer almost always involves collaboration with regulators, investigative sites, patients and technology providers, pulling us out of our silos by design. 

The clinical research industry is not short on capability. Science is advancing rapidly, technology is increasingly sophisticated and there is no shortage of talented people working to improve how trials are designed and run. What would accelerate progress is a greater willingness to revisit long-held assumptions about how the work gets done, and the curiosity to pursue better answers, even when they require us to do things differently than we always have. 

About the author  

Kelsey Jakee is Senior Director of Portfolio Strategy & Innovation at TransCelerate BioPharma, where she leads efforts to advance clinical research through collaboration and strategic innovation. Prior to TransCelerate, she led consulting teams to support clients in designing and deploying new sources of innovation and building operating models to enable future growth. 

From DDW Volume 27 – Issue 3, Summer 2026 – Read the digital issue here