In the evolving landscape of medical research, the disparity between clinical trial populations and real-world patients has become increasingly apparent. While clinical trials are essential for establishing the safety and efficacy of new therapies, they often fail to reflect the diversity and complexity of patients in everyday clinical settings. This gap poses significant challenges for healthcare providers, particularly in community care environments, where patient demographics and treatment conditions can vary widely.
Real-world evidence (RWE) studies have emerged as a crucial tool for bridging this divide, offering insights that can bolster physician confidence and promote the broader adoption of therapies post-approval. These studies are particularly vital for underrepresented populations who may not be adequately reflected in traditional clinical trials.
Understanding the Clinical Trial Divide
New therapies typically enter the market backed by robust clinical trial data. However, the adoption of these therapies in routine care often lags behind expectations. One contributing factor is the discrepancy between trial populations and those seen in everyday clinical practice. Trial cohorts frequently consist of patients from academic institutions, with strict eligibility criteria and uniform care delivery. In contrast, real-world practice involves a broader range of patient demographics, varying access to diagnostic testing, and differences in care delivery across providers and regions.
To expand the adoption of new therapies, it is not enough to rely solely on education or outreach efforts. There is a need for evidence that demonstrates the effectiveness of these therapies in real-world populations, which better reflect the day-to-day realities of clinical practice. Unlike guideline inclusion, which requires structured evidence through formal committee review, broader adoption often depends on physicians observing positive outcomes in patients who mirror their everyday practice.
Filling the Gaps with Real-World Evidence
Clinical trials are 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.
As a result, once a therapy enters the market, physicians 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 their patients, particularly 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 often conduct follow-on studies using real-world data. Post-approval studies can demonstrate a therapy’s effectiveness in broader patient groups, especially those not well represented in the original trial. These studies are typically conducted by principal investigators in collaboration with community and academic research sites, with findings published in peer-reviewed journals to support physician confidence and policy updates.
“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.”
When executed effectively, these studies address clinical questions that trials were not designed to answer. They may reveal whether a therapy performs consistently across different demographic groups, in non-academic settings, or when delivered alongside varying standards of care. In doing so, they can support more confident prescribing and inform broader inclusion in guidelines or coverage policies. However, the value of these studies hinges on the quality and representativeness of the data used.
Overcoming Data Access Challenges
For teams focused on expanding therapy adoption, the challenge is less about acquiring data and more about accessing datasets that accurately reflect real-world care. Many widely used platforms draw heavily from academic medical centers, where patient demographics, workflows, and diagnostic access differ significantly from those in community settings.
This overrepresentation limits the ability to study how therapies perform across diverse populations or care environments. When datasets disproportionately represent one segment of the healthcare system, it becomes difficult to build evidence that supports broader clinical decision-making or addresses variations in real-world adoption.
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 allow for the study of therapy performance in truly diverse populations, unlocking broader clinical utility.
Closing the Gap Between Trials and Practice
Regulatory approval confirms that a therapy is safe and effective within a defined trial population. However, translating that success into real-world adoption can be more complex. For therapies to reach broader patient populations, particularly 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 to ensure that innovations in care translate to real-world benefits. As precision therapies become 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 is a prerequisite for scalable innovation.