Clinical trials, while crucial for demonstrating the safety and efficacy of new therapies, often fall short in representing the diversity and complexity of real-world patients. This discrepancy is particularly evident in community care settings, where the patient demographics can significantly differ from those typically enrolled in clinical studies. As a result, the adoption of new therapies in routine care can be slower than anticipated, creating a divide between clinical trial outcomes and real-world effectiveness.
Real-world evidence (RWE) studies have emerged as essential tools for bridging this gap. They play a pivotal role in building physician confidence and expanding the adoption of therapies post-approval, especially among underrepresented populations. Access to representative datasets is critical in this endeavor, and emerging technologies such as federated data models and AI-driven harmonization are helping to overcome current limitations.
Understanding the Clinical Trial Divide
New therapies often enter the market backed by robust clinical trial data. However, the transition from trial to routine care is not always seamless. Trial populations frequently differ from those seen in everyday clinical practice, particularly in community-based care. While trial cohorts may include more patients from academic institutions with tighter eligibility criteria, routine practice involves a broader range of patient demographics, variable access to diagnostic testing, and differences in care delivery across providers and regions.
Expanding the adoption of new therapies requires more than just education or outreach. It necessitates generating evidence that supports the therapy’s effectiveness in real-world populations, which better reflect day-to-day clinical practice. Unlike guideline inclusion, which requires structured evidence through formal committee review, broader adoption often hinges on physicians observing outcomes in patients who reflect their everyday practice.
Filling the Gaps with Real-World Evidence
Clinical trials are meticulously designed to demonstrate safety and efficacy under controlled conditions. However, the same controls that ensure statistical soundness also limit the diversity of enrolled patients. Strict inclusion and exclusion criteria often exclude patients with certain comorbidities, older adults, or those treated in community settings.
Consequently, once a therapy enters the market, physicians practicing outside large academic medical centers may not see their patient populations reflected in the published trial results. This can create uncertainty about whether the same outcomes apply to patients in their care, especially when clinical presentations are more complex or diagnostic workflows differ from those in the trial.
The Role of Representative Real-World Evidence
To build confidence beyond the trial setting, pharmaceutical companies frequently conduct follow-on studies using real-world data. A post-approval study can demonstrate a therapy’s effectiveness in broader patient groups, particularly those not well represented in the original trial. These studies are often led by principal investigators in partnership with community and academic research sites, with findings published in peer-reviewed journals to bolster physician confidence and inform policy updates.
“When done well, these studies address the clinical questions that trials were not set up to answer. They may show whether a therapy performs consistently across different demographic groups, in non-academic settings, or when delivered alongside varying standards of care.”
The value of these studies hinges on the quality and representativeness of the data used. Up to 80% of oncology patients in the US are treated outside academic centers. If datasets overlook these environments, they risk leaving behind the majority of real-world patient experiences.
Challenges and Technological Solutions
For teams focused on expanding therapy adoption, the challenge lies not in acquiring data but in accessing datasets that accurately reflect real-world care. Many widely used platforms draw heavily from academic medical centers, where patients, workflows, and diagnostic access differ from those in community settings. This limits the ability to study how therapies perform across diverse populations or care environments.
Emerging technologies are helping to overcome these limitations. Federated data models, artificial intelligence-driven data harmonization, and synthetic control arms enable researchers to generate robust, privacy-preserving insights across multiple care settings without centralizing sensitive patient data. These innovations facilitate the study of therapy performance in truly diverse populations, unlocking broader clinical utility.
Bridging the Divide: A Strategic Imperative
Regulatory approval confirms that a therapy is safe and effective in a defined trial population. However, translating that success into real-world adoption can be more complex. For therapies to reach broader patient populations, especially those underrepresented in trials, pharmaceutical teams often need to invest in generating evidence that mirrors real-world care. These studies play a critical role in filling the gaps left by clinical trials, helping physicians understand how a therapy performs in settings and patient groups they encounter daily.
“As the oncology landscape continues to evolve, the ability to assess performance across diverse clinical environments is becoming a key factor in driving adoption. Generating this type of evidence is a strategic investment for ensuring that innovations in care translate to real-world benefit.”
As precision therapies grow more targeted and complex, the need for population-level, representative evidence will only increase. Bridging the divide between clinical trials and the real world is no longer a post-market task—it’s a prerequisite for scalable innovation.
