The technology required to make clinical trials more connected is advancing quickly. Data can increasingly move directly from patients, sites, sensors, and other sources into the systems where research teams use them. The FDA’s Real-Time Clinical Trials initiative is now beginning to explore how more real-time access to clinical trial data can accelerate clinical development.

But speed does not equate value.

Across DiMe’s work in digital measurement and technology-enabled clinical research, some of the hardest problems begin after the technology has matured. These implementation and value questions were also at the center of a recent DPHARM discussion convened by DiMe and Paradigm Health, which brought together perspectives from biopharma, research sites and health systems, patient advocacy, and technology developers. Paradigm Health’s experience working within the FDA Real-Time Clinical Trials proof-of-concept phase provided a practical lens into how some of these questions are beginning to be worked through. What emerged was less a debate about whether trials can become more real-time and more a discussion about what has to be true for doing so to actually improve research.

Start with what gets better

The easiest way to describe a real-time clinical trial is through its technical capabilities. Data becomes available sooner. Systems become more connected. Monitoring can happen more continuously. Information that once arrived periodically may be visible more continuously throughout the study.

The starting point is what value those capabilities can create.

For research sites and healthcare providers, the answer depends partly on implementation. New technologies enter environments that are already managing multiple systems, workflows, protocols, and data requirements. A connected trial that creates another technology stack to navigate may solve a data access problem while creating an operational one.

For patients, the same design decisions show up differently. Participants are not experiencing “digital transformation.” They experience the individual requirements of a trial: visits, devices, surveys, assessments, follow-up, and everything else they’re asked to do.

That perspective came through clearly at DPHARM. Patient advocates were interested in the possibilities of more technology-enabled research, but they also brought the conversation back to the experience of participating. If technology makes a trial more sophisticated without making participation meaningfully better, an opportunity was clearly missed.

The opportunity for real-time trials is therefore not maximum digitization. It is more intentional access to data, using technology that makes participation lighter, execution easier, or the evidence more timely.

The “data firehose” needs a destination

When more information becomes available, it can quickly overload the recipient. The phrase used during the DPHARM discussion was the “data firehose,” and it captures an important tension. Turning on the firehose because we can create little value if the people receiving the information do not know what they are looking for or what they can do differently because they have it.

There are meaningful possibilities. Participants discussed earlier development decisions, better patient stratification, and opportunities to understand disease differently. They also raised questions about whether more timely data could inform protocol amendments and how data generated through these models can align with regulatory needs.

Those are different use cases, but they point toward the same principle: start with the data-driven decision, not the ability to capture the data.

In digital measurement, DiMe has long emphasized the importance of defining what matters, how it will be measured, and what evidence is required for the intended use.

Without that discipline, a real-time trial risks becoming a conventional trial with substantially more information moving through it. With it, more timely data could allow research teams to do something more consequential than make the same decisions faster. They may be able to make better decisions because they can see signals, patient differences, or patterns in disease and treatment response while there is still time to learn from them.

Real-time is an operating model, not a feature

The harder implication is that none of this can be solved by technology alone.

Sponsors, sites, patients, technology partners, and regulators do not experience a clinical trial in the same way. Each sees a different part of the system, and each has requirements that affect the others. A sponsor’s evidence strategy shapes what sites are asked to implement. Technology choices shape what research teams and patients are asked to do. Data collection decisions shape what drug developers can interpret and what regulators may ultimately be asked to evaluate.

One of the most valuable aspects of the DPHARM discussion was simply putting those perspectives in the same room.

Dr. Ivy Altomare, Vice President of Clinical Research at Paradigm Health, noted afterward that these stakeholder groups are often engaged separately. At DPHARM, bringing them together exposed the dependencies between their perspectives, particularly when patient advocates raised considerations that may be less visible in conversations centered on sponsors, sites, or technology.

That is an important lesson as the field experiments with new trial models. Technical interoperability is necessary, but real-time clinical trials also require operational interoperability. The technology, evidence strategy, workflows, participant experience, and regulatory purpose have to be compatible and aligned.

Paradigm’s experience in the FDA Real-Time Clinical Trials proof-of-concept phase offers one practical lens into how those dependencies are beginning to be worked through. Proof-of-concept efforts matter because they create an opportunity to test not only whether the technology functions, but what needs to surround it for the model to function in clinical research.

What better looks like

There is unlikely to be a universal blueprint for a real-time clinical trial. Nor should there be. A model designed around long-term safety follow-up will solve a different problem than one intended to support earlier insight during active treatment. The technology, data, and workflows should reflect that purpose.

The more useful standard is whether the pieces have been designed as one system.

Better means knowing what decision the data need to support before deciding how and when to collect it. It means considering patient and site burden while the operating model is being designed, rather than after the technology has been selected. It means thinking about eventual evidence needs before a new stream of data has already been generated. And it means bringing the stakeholders who will participate in, operate, interpret, and evaluate the trial into the conversation early enough to shape it.

The promise of real-time clinical trials is to make clinical research more responsive to what is happening in a study while it is happening. Realizing that opportunity will depend on how well the technology, data, and operating model work together to generate better evidence, support better decisions, and improve the experience of the people the research depends on.