Real-World Evidence in Clinical Trials: Where the Industry Stands and Where It's Headed

Real-world evidence (RWE) has been moving steadily upstream in drug development. With the FDA now explicitly recognizing RWE as eligible confirmatory evidence under its updated single-trial standard, the question is no longer whether RWE belongs in clinical development, but how to build the infrastructure and organizational capabilities to use it effectively.

The evidence model in drug development is reorganizing around a more continuous, self-reinforcing system in which real-world data (RWD) informs and validates findings in real time rather than retrospectively. Understanding what that transition demands operationally is increasingly central to competitive drug development strategy.

Here are 10 questions addressing the current state and future direction of RWE in clinical trials.

1. What is RWE and how has its role in drug development evolved?

Real-world data, as defined by the FDA, is data relating to patient health status and healthcare delivery collected from sources such as electronic health records, claims data, and patient registries. Real-world evidence is the clinical evidence derived from analyzing that data. Historically used primarily for post-marketing surveillance, RWE has expanded significantly into earlier stages of development, including trial design, site selection, endpoint validation, and regulatory submissions. Its adoption reflects a broader recognition that controlled trial environments alone cannot capture the full complexity of how therapies perform in practice.

2. How is the FDA's updated approval standard changing the role of RWE in regulatory submissions?

The FDA's shift to a single adequate and well-controlled trial as the default approval standard, with confirmatory evidence eligible to fulfill the supporting role, explicitly names RWE as a qualifying source. As Christopher Boone of Oracle Health and Life Sciences observed in a recent article, this represents a fundamental reorganization of the evidence hierarchy: "The bar has shifted—and it has shifted in RWE's favor."

Organizations that arrive at the approval stage with RWE already running concurrently alongside trial data are better positioned than those assembling evidence under pressure at the end of development.

3. Where does RWE most realistically complement or replace traditional trial data?

Secondary RWD, collected for one primary purpose and later applied to research questions, is most valuable on the observational end of the clinical research spectrum: post-market surveillance, label expansion, and long-term safety assessments.

As Jen Lamppa of Inovalon explained in an interview with Applied Clinical Trials, "On the pragmatic spectrum, secondary data most realistically complements or replaces traditional trials on the observational side—post-market surveillance, label expansion, long-term safety assessments."

Rare disease is another high-impact area, where small patient populations make randomization difficult or infeasible, and RWE can fill critical evidence gaps.

4. What are the most common use cases for RWD across the drug development lifecycle?

Organizations are applying RWD across a broad range of functions: informing inclusion and exclusion criteria, selecting and identifying sites, estimating enrollment rates, contextualizing safety signals, and supporting regulatory submissions. In some cases, sponsors have used RWE as pivotal evidence for accelerated approval in rare disease or as an external control arm in hybrid trial designs. Post-approval, RWD informs treatment pattern analysis, market positioning, and label expansion strategies. Across therapeutic areas, oncology has the most developed evidence base, while urology remains in early piloting stages.

5. What does building RWE infrastructure actually require of sponsor organizations?

Effective RWE infrastructure requires more than data access. It demands systems capable of ingesting and analyzing EHR, claims, and patient-reported data on an ongoing basis rather than in discrete snapshots tied to trial milestones.

As Boone described it, "The organizations furthest along share a common trait. They have invested in unified data environments where clinical trial data and real-world data are managed as complementary inputs into a single evidentiary framework, not siloed assets that teams scramble to reconcile at the last minute."

Cloud-based platforms with standardized data models are increasingly the foundation for this kind of integration.

6. What is the operational significance of the FDA allowing RWE submissions without identifiable patient data?

The policy shift introduces an important distinction that clinical operations teams need to understand: the difference between pseudonymized and anonymized patient data.

As Lamppa explained, "Traditional clinical trial data are pseudonymized data...pseudonymized data can still be linked back to an individual patient and is therefore identifiable data."

De-identified datasets used in RWE cannot be re-identified at all. The implication, as Lamppa noted, is that "it's important for these teams to consider how anonymized patient data can and should be integrated across the spectrum of clinical development operations and applications, especially as part of the data package."

7. What are the most significant data quality challenges organizations face when using RWD?

Because RWD is often collected for medical reimbursement rather than research purposes, applying it to clinical questions introduces reliability concerns. Key challenges include lack of linkage between datasets, erroneous or incomplete data collection, and the inability to capture variables critical for research analysis, such as mutation-specific biomarkers. Sponsors also report difficulty following the full patient journey across different treatment settings. Building vendor qualification processes and normalizing data structure through cleaning and curation are among the most commonly cited practical solutions.

8. Where is RWE having the greatest measurable impact on trial performance?

The highest-impact applications center on trial design optimization and decision-making efficiency. RWD is being used to refine sample size estimates, identify efficacy and safety endpoints, evaluate the probability of trial success, and accelerate site and patient population identification. Organizations also report that RWD enables faster responses to regulatory inquiries by drawing on existing datasets rather than initiating new studies. While precise ROI is difficult to quantify, 10 of 14 interviewees in a Tufts CSDD and Verana Health study anticipated an increase in ROI over the next one to two years.

9. What organizational and change management barriers are slowing RWE adoption?

Structural challenges are as significant as technical ones. Company silos, fragmented ownership of RWD and RWE functions across departments, and inconsistent acceptance of RWE in internal decision-making all limit the potential of even well-resourced programs. Organizations that have made the most progress have addressed these barriers through cross-functional collaboration structures, workforce development, and strong leadership to guide adoption. Integrated Evidence Generation, a strategy that brings all stakeholders together and identifies data gaps early, has emerged as a practical framework for improving organizational alignment around RWD applications.

10. How is AI expected to shape the future of RWE in clinical development?

Artificial intelligence (AI) and machine learning are well positioned to extract and standardize insights from raw, unstructured clinical notes, incorporate data from wearables and mobile health applications, and map patient journeys with greater precision than traditional methods allow. Organizations anticipate that AI integration will reduce trial startup timelines, improve geographic diversity in site selection, and enable more complete longitudinal datasets that support precision medicine strategies.

As Boone framed the broader trajectory, organizations that build toward a concurrent RWE validation model will arrive at development decisions that are "not just more efficient, but more defensible, more credible, and better positioned for the scrutiny that follows approval."