Clinical trials have become more complex than ever. Protocols are becoming more specialized, endpoints are more refined, and eligibility criteria are narrower and more precise. At the same time, development programs are expected to move faster and operate more efficiently. Despite this double pressure, feasibility projections are often based on high-level estimates or investigators’ recall rather than validated assessments of patients who actually meet protocol criteria in a real-world clinical setting.
When projected patient populations fail to emerge or sites perform poorly, the consequences extend beyond delayed timelines. This is followed by protocol modifications, operational stress, strained relationships between sites, and rising costs. As protocol complexity increases, the risk of designing studies without a clear understanding of real-world patterns of care becomes more difficult to ignore.
Real-world data (RWD) offers a significant opportunity to close this gap, but only when integrated carefully and early in the development process.
Moving from hindsight to proactive design
For years, RWD has played an important role in post-marketing research and evidence generation. Increasingly, its greatest impact is achieved at an earlier stage of development, where it can inform protocol design, feasibility planning, and evidence strategy before studies begin.
Real-world analyzes are often introduced once new signals emerge during study execution, whether related to enrollment speed, changing standard of care, unexpected interim trends, the dynamics of competing trials, 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 incorporate real-world insights prior to protocol completion and use them to 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, markers of disease severity, referral pathways, and comorbidities influence whether a patient is truly eligible and likely to enroll. Viewing the full treatment journey allows teams to assess whether inclusion and exclusion criteria align with how the disease is diagnosed and managed during routine clinical practice.
When applied early, these insights help reduce the risk of overestimating truly eligible populations and help prevent feasibility gaps later during study execution.
Accuracy in site and patient strategy
While historical enrollment performance remains a valuable indicator, past success does not guarantee that a site is currently treating patients who meet very specific eligibility requirements.
The real-world view allows sponsors to assess feasibility at the patient level. Instead of relying solely on aggregated performance metrics, teams can evaluate whether a site is actively managing patients who match 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 to not only qualify, but also enroll and remain in the study. This represents a shift from directional forecasting to data-driven feasibility planning based on how care is delivered in practice.
“By expanding site information derived from real-world data, we can better select sites that enroll patients according to our testing 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 between data sets, and gaps in longitudinal continuity can restrict the reliability of insights. Advanced analytics and machine learning can improve harmonization and help scale 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 the quality is bad, you can apply your AI and everything to garbage in, garbage out.” Alex Asiimwe, PhD, Gilead Sciences
Beyond quality considerations, infrastructure fragmentation remains a challenge. Many organizations operate across functional silos, license data sets independently, and lack standardized frameworks for sharing and integrating knowledge across teams. Global development further complicates the picture. Robust data sources may be available in certain regions, while others lack comparable depth or accessibility. Matching feasibility modeling to geographic strategy requires careful coordination and realistic assessment of data availability.
Speed is another critical factor. Knowledge generation must be aligned with development schedules. If analyzes take months to complete, their ability to shape protocol decisions is diminished. Scalable infrastructure, clear governance, and integrated workflows are essential to ensure 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 appropriately apply it. While regulatory openness has increased, RWD must be integrated for clear, fit-for-purpose reasons and guided by scientific rationale, not impulse. That requires cross-functional integration across clinical, epidemiological, regulatory and operational aspects to weigh trade-offs, ethics and feasibility.
RWD does not replace randomized trials, but 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 generate evidence that is both rigorous and operationally achievable.
About Ashley Daigneau
Ashley Daigneau He is head of clinical trials at Summer Healthwhere he oversees the strategy and execution of innovative clinical research solutions that leverage real-world data. Ashley has over 15 years of experience supporting the development of real-world evidence strategies and overseeing the execution of clinical studies.
