Real-Time Clinical Trials: What the FDA's Push Toward Continuous Oversight Means for Sponsors

The FDA's launch of proof-of-concept real-time clinical trials marks one of the most consequential shifts in regulatory oversight in decades. By enabling near real-time visibility into safety signals and endpoint data as trials progress, the agency is pushing the industry toward a continuous development model that most sponsors are not yet operationally equipped to support.

The gap between where the industry currently operates and where the FDA is heading is significant. Sponsors that invest now in unified infrastructure, artificial intelligence (AI)-enabled governance, and structured data architecture will be best positioned to meet continuous oversight requirements as they become standard across development phases.

Here are 10 questions addressing what the FDA's real-time clinical trial initiative means for sponsors, CROs, and clinical operations teams.

1. What is the FDA's real-time clinical trial initiative and what problem is it designed to solve?

Traditional clinical development relies on sequential data collection and periodic reporting, in which trial sites submit data to sponsors, sponsors conduct analyses, and regulatory filings follow.

The real-time clinical trial initiative seeks to address this by enabling near real-time visibility into safety signals and endpoint data as trials progress, supporting earlier signal detection and more dynamic regulatory oversight.

2. What are the two proof-of-concept trials and what do they demonstrate?

The FDA launched proof-of-concept studies with AstraZeneca and Amgen. AstraZeneca's Phase II TRAVERSE trial is evaluating acalabrutinib combined with venetoclax and rituximab in treatment-naïve mantle cell lymphoma across multiple academic sites. Amgen's Phase Ib STREAM-SCLC trial is investigating tarlatamab in patients with limited-stage small cell lung cancer.

The FDA reports having already received and validated data signals from AstraZeneca's trial through a digital infrastructure platform, supporting the technical feasibility of continuous data transmission and establishing a foundation for the broader pilot program to follow.

3. How ready is the industry to actually meet continuous data review requirements?

The honest answer is that most of the industry is not ready. As Raj Indupuri, CEO and co-founder of eClinical Solutions, put it in an interview with ACT: "The industry has been built on fragmented systems, different EDCs, different software vendors, delivering non-standardized data. To do continuous review, you have to bring all of this together, and it requires modern data infrastructure and advanced pipelines to support different users in the clinical value chain."

Milestone-based review and point solutions are structurally incompatible with real-time oversight, and the investment required to close that gap is substantial for most organizations.

4. What does unified data infrastructure actually require sponsors to build?

Continuous oversight demands more than technology upgrades; it requires a fundamental rethinking of how data flows across the clinical value chain. As Indupuri explained, "Sponsors cannot achieve real-time review until they invest in unified data infrastructure that harmonizes clinical data from all these fragmented sources and systems. And not only building that infrastructure, but also having a pipeline so that when data gets in, it can be ingested in real time and delivered in a highly governed way to users so they can act on it for immediate decision making."

That means replacing siloed point solutions with integrated systems that connect clinical operations, data management, safety, and analytics teams to a shared data environment.

5. How does real-time oversight change what data quality and governance require of trial teams?

The shift is from milestone-based quality checks to continuous governance embedded throughout the trial lifecycle. As Indupuri described: "The most important shift here is that quality has to be built into the entire process continuously, rather than waiting until the end. Right now it happens at certain milestones—database lock, interim analysis, submission preparation. But in a real-time oversight environment, governance and risk management become more critical and come under scrutiny across the entire life cycle of the trial."

That requires cross-functional alignment across departments that currently operate in silos and access to unified systems that support traceability and continuous monitoring.

6. What signal is the FDA sending about data quality expectations, and how should sponsors respond?

Regulators are moving away from an approach built on generating large volumes of data and cleaning it up afterward. As Kevin Bugin, head of global regulatory policy and intelligence at Amgen, explained in an interview ACT at DIA 2026: "What we're hearing from regulators—and I know we're hearing this—is no, you need to focus on the right data at the right time and in the right standard. That quality comes from quality-by-design thinking, not something you can build in after the study is over."

Through TransCelerate's interactions with the FDA and other global regulatory authorities on pragmatic trial designs, the message has been consistent: the focus is on operationalizing, not just conceptualizing, real-time evidence generation for regulatory-grade decision making.

7. How does protocol digitization enable real-time regulatory oversight?

Static protocol documents are incompatible with continuous review because they require manual interpretation and create cascading manual workflows every time an amendment occurs. As Angie Maurer, VP of AI-enabled clinical development at Medable, described it in another interview with ACT at DIA 2026: "Protocol digitization is the upstream enabler for everything downstream. The protocol defines what data is collected, when, where, at what intervals, what endpoints are critical—all of that important context."

When that definition is stored as structured data, it can automatically seed and feed study data collection schemas in CDISC-compliant formats that FDA review systems can consume, making continuous regulatory data review technically possible from the start of the trial.

8. How does AI fit into a continuous oversight model, and what governance guardrails are needed?

AI has significant potential to eliminate manual tasks, accelerate decision-making, and support integrated risk management across the trial lifecycle. But adoption depends on trust. As Indupuri noted: "AI in general is a black box. For sponsors to adopt it, it needs to be trustworthy. There needs to be governance and explainability to give confidence to different users in the value chain so they can trust the outputs and take action."

The approach he advocates is transparency: giving sponsors visibility into how AI agents are processing data, what context they are using, and how outputs can be evaluated and validated, so that decisions made on the basis of AI outputs remain auditable and defensible.

9. What role does real-world evidence play in the move toward continuous oversight?

The boundary between traditional clinical data and real-world data is blurring as continuous review models develop. As Indupuri explained: "Going back to the earlier point around continuous data review and decision making—you have data coming from clinical trials, but there's a huge opportunity to look at real-world data as well. We believe these boundaries will continue to blur, and if you have modern data infrastructure that can harmonize and bring these together, it can immensely support continuous oversight and give confidence to regulators and sponsors that the evidence is being produced from real-world data as well."

Organizations that build pipelines capable of integrating both data types will be significantly better positioned as this model matures.

10. What should clinical operations leaders be doing now to prepare for continuous oversight at scale?

The practical priorities are clear across all four dimensions of this shift:

• Invest in unified data infrastructure that eliminates silos and supports real-time ingestion and governance.
• Adopt quality-by-design thinking that builds data integrity into trial execution rather than correcting it afterward.
• Digitize protocols so that amendments and endpoint definitions become structured, versioned data objects that downstream systems can act on automatically.
• And as Maurer put it, recognize that "when sponsors digitize protocols, they're not just solving for a startup efficiency issue—they're creating a structured foundation that makes continuous regulatory data review possible."