Traditional randomized controlled trials (RCTs) have long been the gold standard for testing new therapies, but they are increasingly strained by today’s rapidly evolving clinical research landscape. Classical clinical trial design has several limitations, such as high costs, long duration, large sample sizes and sample requirement versus available population, particularly for rare diseases, and gauging efficacy in subgroups. These factors have only become more challenging as clinical trial designs grow more complex to address new therapies and personalized treatments.
However, through the interrogation of real-world data (RWD), real-world evidence (RWE) can be used to optimize clinical trials and help identify populations that have the greatest potential to benefit from a new treatment, enabling more efficient trial designs.
Real-world data (RWD) is health and social data collected outside randomized controlled trials, such as from Electronic Health Records (EHRs), medical insurance claims, registries, and even wearables, such as BP monitors. This information can be used in clinical trial design to understand patient populations, define eligibility, model external control arms, and assess feasibility. Real-world evidence (RWE) is an analysis generated using rigorous statistical methods to answer specific clinical questions about effectiveness, safety, or use patterns. In practice, RWD informs and structures trial design, while RWE provides validated insights that support hypotheses, refine endpoints and guide regulatory submissions.
According to GlobalData, clinical trials using RWD/RWE elements reached a peak in 2024, when they accounted for 16% of all initiated studies, up from 13% in 2023. Oncology is the main therapy area to use RWD and RWE elements, with 34% of studies, followed by the central nervous system at 12%, and cardiovascular at 10%[i].
While some concerns around data quality and interpretability persist, technological advances are helping to overcome these challenges, with RWD/RWE increasingly being seen as a tool to speed up the drug development process. With delays in drug development estimated to cost around US$800,000 in lost prescription sales per day, even small inefficiencies in the process can have major financial consequences[ii].
While they will never replace randomized controlled trials, RWD-based controls are a valuable tool to accelerate the drug development process, particularly in rare diseases where recruitment is challenging.
What are the limitations of classical clinical trial design?
The number of new drugs approved each year has grown substantially over the last decade. From 2010 to 2019, the FDA approved an average of 38 new drugs per year – 60% more than the previous decade[iii], and continuing from 2020 to 2024, the FDA is now approving an average of 52 novel drugs per year[iv]. However, according to the FDA, only about 12% of drugs entering clinical trials are ultimately approved and estimates for the average R&D cost per new drug range from under $1 billion to over $2 billion, after factoring in failed drugs and capital costs[v].
Recruiting large, diverse patient populations can also be problematic, particularly for rare diseases where eligible patients are few and dispersed. Indeed, some diseases are so rare that traditional RCTs are seen as impractical or even unethical. Additionally, classical trial designs are often inefficient for evaluating treatment effects in specific subgroups, such as those defined by genetic markers, because they require large and often unrealistic sample sizes to achieve sufficient statistical power.
Where does RWD/RWE show the greatest potential?
Utilizing RWD makes it feasible to run studies when randomized trials are not possible due to small or highly dispersed patient populations. Synthetic or external control arms powered by such data can supplement or even replace traditional placebo groups, helping sponsors gather meaningful data with smaller samples and often at lower cost.
When it comes to patient selection and recruitment, RWD analytics can help accelerate patient identification and trial enrollment. The goal is to protect internal validity on the few truly critical factors while relaxing non-critical criteria that slow recruitment. RWD/RWE can support the analysis of highly specific subpopulations defined by biomarkers, comorbidities, or other characteristics. This allows for more granular and relevant assessment of therapeutic effects in the real world and better matching in personalized medicines. To limit bias, subgroups are defined prospectively where possible, using robust adjustment methods, with findings replicated in independent datasets.
Overall, by augmenting control arms and patient cohorts with robust RWD, sponsors can potentially reduce overall trial costs, maintain statistical validity, and shorten overall development timelines.
Veradigm and RWE
Veradigm is a real-world evidence partner operating at the intersection of clinical care and research, with its network covering more than 152 million unique patients and feeds into seven cardiometabolic registries developed with the American College of Cardiology, spanning heart failure, atrial fibrillation, atherosclerotic cardiovascular disease, hypertension, type 1 and type 2 diabetes, and chronic kidney disease. These registries integrate data from over 80 EHR platforms into a single common data model, capturing detailed information on disease burden, treatment patterns, clinical measures, and outcomes, creating large, research-ready datasets.
For providers, Veradigm aims to make research participation completely frictionless, allowing them to keep their existing EHR systems while gaining access to research opportunities, patient recruitment tools, and insights that support programs such as Medicare’s Merit-Based Incentive Payment System. For sponsors, Veradigm’s tokenized data environment enables identification of eligible patients, linkage of clinical and claims data, and use of unstructured information (via natural language processing) to answer complex questions about effectiveness, safety, and cost.
Such datasets help narrow the gap between everyday clinical care and formal research, while supporting a new generation of RWE studies that inform regulatory, reimbursement, and value-based decision-making. Veradigm specializes in transforming RWD into meaningful RWE, generating deeper insights to strengthen regulatory submissions, payer value propositions, and shorten time to market.
For more on how Veradigm can help with your real-world data requirements, download the free paper below.
[i] https://www.clinicaltrialsarena.com/features/accurate-data-interpretation-key-expanding-rwe-trials/
[ii] https://csdd.tufts.edu/sites/default/files/2025-02/Aug2024%20Day%20of%20Delay%20White%20Paper%20Final.pdf?1744237947=
[iii] https://www.cbo.gov/publication/57126
[iv] https://pmc.ncbi.nlm.nih.gov/articles/PMC10856271/
[v] https://www.cbo.gov/publication/57126
