By Jocelyn August, Vice President of Product Management for Life Sciences at PurpleLab

Clinical operations teams are under more pressure than ever to reduce risk earlier, enroll patients faster, and justify every dollar spent – and many are recognizing that real-world data and tokenization are essential tools to enable this.

However, without access to data isn’t the same as making it actionable. If data remains siloed and disconnected from the decisions that matter most, teams won’t see improvements across protocol design, patient recruitment and regulatory strategy.

But when applied effectively, real-world evidence can help clinical operations teams reduce risk earlier, improve recruitment strategies and build evidence assets that support an entire development portfolio. The key is ensuring that data is standardized and embedded into operational decision-making.

Below are three moments in the clinical trial lifecycle where real-world evidence can have the greatest impact.

1. De-Risking Protocol Design Before Trials Begin

One of the most important things clinical operations teams can do when launching a trial is ensure they deeply understand the patient population.

Teams will often design clinical protocols based on patient demographic information published in scientific literature. But these findings may not reflect the current patient populations. Real-world data allows organizations to conduct descriptive and characterization studies that reveal the landscape ahead of time.

Those that purely rely on studying the patient population through (potentially outdated) literature risk discovering halfway through a trial that the patient population is messy or extremely difficult to recruit – after they’ve already spent millions on recruitment.

Real-world data lets teams uncover essential and up-to-date patient population insights up front, including:

• Patient demographics
• Geographic distribution
• Where patients receive care
• Visit patterns
• Treatment pathways
• Care journeys

Eligibility criteria are another area where real-world evidence can add critical insight. Sensitivity analyses allow teams to test how inclusion and exclusion criteria will impact the eligible patient population ahead of time. In some cases, a single criterion can dramatically reduce the number of potential participants. With RWD, teams can plan eligibility criteria strategically.

Clinical considerations will always guide protocol design, but real-world data provides an evidence-based way to anticipate operational constraints before millions of dollars are spent on recruitment.

Sponsors are also increasingly adopting a “test before committing” approach to data strategy. Before selecting a real-world dataset for a study, teams conduct fit-for-purpose assessments to ensure the data can support their research questions.

In one example, a sponsor conducting a major depressive disorder study tokenized all trial participants – including enrolled patients and screen failures. Although the study ended early due to low enrollment, tokenized cohorts were later linked with real-world data, enabling analysis of baseline characteristics, comorbidities and treatment histories across both groups. These insights are now informing the design of upcoming studies and helping teams avoid similar recruitment challenges.

2. Strengthening Operational Execution During Trials

Real-world evidence continues to deliver value once a trial is underway. One of the most important enablers of operational RWE is data standardization. When healthcare datasets are harmonized into a common structure – such as the OMOP common data model – analyses can be run consistently across different systems and institutions.

This standardization separates the data from the analysis itself, therefore making studies easier to reproduce and more transparent for regulatory review.

The benefits of standardized real-world data became especially visible during the COVID-19 pandemic. Through the international OHDSI Evidence Network, researchers around the world used standardized datasets to rapidly generate insights on disease progression and treatment patterns.

Because each dataset followed the same structure, teams were able to run analyses across multiple countries and health systems without rewriting the underlying logic.

Real-world data can also improve recruitment during ongoing trials. In one healthcare system, investigators developed a recruitment process driven by electronic medical records. The system tracked the entire recruitment funnel, including:

• Patients invited to participate
• Patients who responded
• Pre-screening outcomes
• Screening outcomes
• Randomized participants

Using RWD, the researchers were able to refine predictive algorithms used to identify eligible patients. This feedback loop allowed recruitment strategies to improve throughout the study.

Other operational applications include generating recruitment lists for trial sites and alerting investigators when potentially eligible patients have upcoming appointments. In one year alone, nearly 1,500 recruitment lists were generated to support site-level trial enrollment.

Furthermore, real-world data enables more flexible trial models, including decentralized trials, centralized recruitment programs and automated alerts when patients receive qualifying diagnoses. In rare disease studies, these alerts can even trigger site activation or recruitment outreach as soon as eligible patients are identified.

3. Building Reusable Evidence Infrastructure

Perhaps the most important shift underway is how organizations think about real-world data itself. Historically, RWD was often treated as a one-time input used to answer a specific research question. Today, leading organizations are building connected data ecosystems that can support multiple studies across a portfolio.

These “living” evidence environments allow datasets to be reused and expanded over time. In one case, a sponsor had previously built linked datasets across multiple partners to support earlier research initiatives. Some internal teams were unaware those assets existed. Once they discovered the datasets, they were able to quickly launch new protocols using the existing infrastructure.

The ability to reuse evidence assets accelerates research timelines and improves cross-team collaboration. It also creates a foundation for emerging analytics technologies. Artificial intelligence is often discussed as the future of clinical development, but advanced models depend on high-quality, well-connected data infrastructure.

Behind every AI breakthrough are massive investments in data standardization and interoperability. Organizations that invest in connected real-world data infrastructure today will be best positioned to take advantage of advanced analytics in the future.

In short, the organizations that realize the greatest value from real-world evidence will be those that move beyond one-off analyses and treat data as operational infrastructure. When real-world data is standardized, connected and applied strategically, it becomes a foundation for faster, more effective clinical development – and ultimately, better outcomes for patients.