Clinical trials have grown more complex than ever before. Protocols are becoming more specialized, endpoints more refined, and eligibility criteria narrower and more precise. At the same time, development programs are expected to move faster and operate with greater efficiency. Despite this dual pressure, feasibility projections often rely on high-level estimates or investigator recall rather than validated assessments of patients who truly meet protocol criteria in a real-world clinical setting.
When projected patient populations fail to emerge or sites underperform, the consequences extend beyond delayed timelines. Protocol amendments, operational strain, stressed site relationships, and escalating costs follow. As protocol complexity increases, the risk of designing studies without a clear understanding of real-world care patterns becomes harder to ignore.
Real-world data (RWD) offers a meaningful opportunity to close this gap, but only when it is integrated thoughtfully and early in the development process.
Moving from Retrospective Insight to Proactive Design
For years, RWD has played an important role in post-marketing research and evidence generation. Increasingly, its greatest impact is realized earlier in development, where it can inform protocol design, feasibility planning, and evidence strategy before studies begin.
Real-world analyses are often introduced once new signals emerge during study execution, whether related to enrollment velocity, shifting standard of care, unexpected interim trends, competitive trial dynamics or the need for contextual evidence. At that point, the ability to influence study design is limited, and adjustments become tactical rather than strategic. A more effective approach is to embed real-world insights before protocol finalization and use it to stress-test assumptions around eligibility criteria, endpoint definitions, and projected enrollment rates under real-world conditions.
Longitudinal clinical data, particularly when electronic health records are combined with complementary data sources such as claims, can reveal insights that prevalence estimates alone may miss. Prior lines of therapy, laboratory trends, disease severity markers, referral pathways, and comorbidities all influence whether a patient is realistically eligible and likely to enroll. Viewing the full treatment journey allows teams to assess whether inclusion and exclusion criteria align with how disease is diagnosed and managed during routine clinical practice.
When applied early, these insights help reduce the risk of overestimating the truly eligible populations and help prevent downstream feasibility gaps during study execution.
Precision in Site and Patient Strategy
While historical enrollment performance remains a valuable indicator, past success does not guarantee that a site currently treats patients who meet highly specific eligibility requirements.
Real-world insight allows sponsors to evaluate feasibility at the patient-level. Rather than solely relying on aggregate performance metrics, teams can assess whether a site actively manages patients who match the study criteria. This distinction is crucial as competition for eligible participants intensifies.
Advanced modeling approaches allow teams to simulate enrollment scenarios before a study begins. By examining patient funnels, referral dynamics, and treatment pathways, sponsors can better anticipate how many patients are likely not only to qualify, but to enroll and remain on study. This represents a shift from directional forecasting to data-informed feasibility planning grounded in how care is delivered in practice.
“By scaling the site insights derived from real-world data, we’re better able to select sites that enroll patients aligned with our trial criteria,” Emily Carter, AbbVie
Data Quality, Infrastructure, and Global Realities
The promise of RWD is significant, but its value depends on the integrity of the underlying data. Incomplete documentation, inconsistent coding, limited linkage across datasets, and gaps in longitudinal continuity can restrict the reliability of insights. Advanced analytics and machine learning can enhance harmonization and help scale the curation of unstructured clinical information, but no methodology can overcome fundamentally poor data quality.
“Real world data is an essential part of our development lifecycle when it comes to generating evidence. One of the main challenges is data quality. You can have as much data as you want, but if quality is poor, you can apply your AI and everything to be garbage in, garbage out,” Alex Asiimwe, PhD, Gilead Sciences
Beyond quality considerations, infrastructure fragmentation remains a challenge. Many organizations operate across functional silos, license datasets independently, and lack standardized frameworks for sharing and integrating insights across teams. Global development further complicates the landscape. Robust data sources may be available in certain regions, while others lack comparable depth or accessibility. Matching feasibility modeling with geographic strategy requires careful coordination and a realistic assessment of data availability.
Speed is another critical factor. Insight generation must align with development timelines. If analyses take months to complete, their ability to shape protocol decisions diminishes. Scalable infrastructure, clear governance, and embedded workflows are essential to ensure that real-world insights inform decisions when they matter most.
Fit-for-Purpose Integration and Cross-Functional Alignment
Ultimately, the question is not whether to use real-world data, but when and how to apply it appropriately. While regulatory openness has grown, RWD should be integrated for clear, fit-for-purpose reasons and guided by scientific rationale, not momentum. That requires cross-functional integration across clinical, epidemiology, regulatory, and operations to weigh trade-offs, ethics, and feasibility.
RWD is not a replacement for randomized trials, but it can strengthen development by reducing uncertainty and supporting study design with clinical reality. As the industry evolves, designing trials for the real world is increasingly essential to generating evidence that is both rigorous and operationally achievable.
About Ashley Daigneau
Ashley Daigneau is head of clinical trials at Verana Health, where she oversees the strategy and execution of innovative clinical research solutions leveraging real-world data. Ashley has more than 15 years of experience supporting the development of real-world evidence strategies and overseeing clinical study execution.
