Key takeaways
Clinical trial populations often don’t reflect the diversity or complexity of real-world patients, particularly in community care settings.
Real-world evidence (RWE) studies are essential for building physician confidence and expanding adoption post-approval, especially in underrepresented populations.
Access to representative datasets—beyond academic centers—is critical, and emerging technologies like federated data models and AI-driven harmonization are helping overcome current limitations.
New therapies often enter the market with strong clinical trial data, yet adoption in routine care can be slower than expected. The reasons vary, but one contributing factor is that trial populations often differ from those seen in routine care, particularly in community-based care. Trial cohorts may include more patients from academic institutions, with tighter eligibility criteria and more uniform delivery of care. In contrast, routine practice involves broader patient demographics, variable access to diagnostic testing, and differences in how care is delivered across providers and regions.
Expanding adoption requires more than just education or outreach. It requires generating evidence that supports the therapy’s effectiveness in real-world populations that better reflect day to day clinical practice. Unlike guideline inclusion, where formal committee review requires structured evidence, driving broader adoption often hinges on physicians seeing outcomes in patients who reflect their day-to-day practice.
Filling the gaps left by trial populations
Clinical trials are designed to demonstrate safety and efficacy under controlled conditions. But the same controls that make results statistically sound also limit the diversity of enrolled patients. Strict inclusion and exclusion criteria often leave outpatients with certain comorbidities, older adults, or those treated in community settings.
As a result, 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, mainly 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 turn to follow on studies using real-world data. A post approval study can demonstrate a therapy’s effectiveness in broader patient groups, especially those not well represented in the original trial. These studies are commonly led by principal investigators in partnership with community and academic research sites. Findings from these studies are commonly published in peer-reviewed journals to support physician confidence and 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 in 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. But the value of these studies depends on the quality and representativeness of the data used.
Data access challenges
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.
For teams focused on expanding therapy adoption, the challenge is less about acquiring data and more about accessing datasets that reflect real-world care. Many of the most 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. When datasets overrepresent one segment of the healthcare system, it becomes difficult to build evidence that supports broader clinical decision-making or addresses variation in real-world adoption.
Emerging technologies are helping overcome these limitations. Federated data models, artificial intelligence-driven data harmonization, and synthetic control arms allow researchers to generate robust, privacy-preserving insights across multiple care settings without centralizing sensitive patient data. These innovations enable studying therapy performance in truly diverse populations and unlocking broader clinical utility.
Closing the gap between trials and practice
Regulatory approval confirms that a therapy is safe and effective in a defined trial population. But 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 see every day.
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.
Noah Nasser, CEO, datma
