🌎 Utilising real world data for clinical evidence generation

Summary

• This guide introduces how real-world data—from EHRs to wearables—can be used to generate real-world evidence (RWE) for regulatory and clinical decision-making.
• It explains the benefits of RWD in filling evidence gaps where traditional trials are impractical, while also outlining challenges such as data bias, quality, and trustworthiness.
• The content draws on frameworks and tools from the FDA, NICE, and EU regulators to help innovators apply RWD meaningfully and responsibly in their evidence strategies.

What this carousel covers

• The definitions and distinctions between RWD and RWE, and why they matter in healthtech
• Key advantages of RWD (e.g., broader populations, longer follow-up, patient experience insights)
• Common sources of RWD: claims data, patient registries, EHRs, audits, wearables, and surveys
• Methodological considerations and regulatory expectations for using RWD effectively and ethically

Key takeaways

• RWD is a powerful tool for generating timely, cost-effective clinical evidence—but only if used with rigour
• Researchers must ensure data relevance, quality, and representativeness, and mitigate sources of bias
• Use of “target trial” approaches, sensitivity analyses, and bias assessments strengthens RWE credibility
• Regulatory bodies are increasingly supportive of RWE—provided study designs are transparent, structured, and fit-for-purpose

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