Real-world evidence (RWE) is transforming the landscape of clinical research. As sponsors, regulators, and payers seek data that reflects how treatments perform outside of controlled environments, RWE has become essential for designing trials, securing regulatory approvals, and making reimbursement decisions.

This guide explains what RWE is, where it comes from, how it’s used in clinical development, and what to consider when incorporating it into your research strategy.

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What Is Real-World Evidence (RWE)?

Real-world evidence refers to the clinical insights generated from real-world data (RWD)—information collected outside of traditional randomized controlled trials (RCTs). These data sources include electronic health records, claims data, patient registries, wearables, and more.

RWE helps answer questions about treatment effectiveness, safety, utilization, and patient outcomes in the context of routine clinical practice. It complements the internal validity of RCTs with the external validity of real-world application.

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Why RWE Is Important in Clinical Research

As healthcare systems move toward value-based care and personalized medicine, RWE has become a key component in:

• Regulatory decision-making: The FDA and EMA have frameworks to support the use of RWE in new indications and post-market studies
• Clinical development: RWE informs protocol design, eligibility criteria, and feasibility
• Market access: Payers require real-world effectiveness and cost data to support reimbursement
• Patient-centric research: RWE captures outcomes that matter to patients, such as quality of life and adherence

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Types of Real-World Data Sources

A wide range of sources can feed into RWE studies:

• Electronic Health Records (EHRs): Clinical notes, medications, lab results
• Claims and Billing Data: Utilization, procedures, diagnoses, and costs
• Patient Registries: Structured data collection for specific populations or products
• Wearables and Connected Devices: Continuous data on vitals, activity, and biometrics
• Patient-Reported Outcomes (PROs): Surveys capturing symptoms, quality of life, and preferences
• Social Determinants of Health: Environmental, socioeconomic, and behavioral data

Each data source has strengths and limitations in terms of completeness, structure, and accuracy.

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Use Cases for RWE in Clinical Development

RWE supports every phase of the product lifecycle:

• Protocol design: Use RWD to identify inclusion/exclusion criteria and optimize recruitment
• Natural history studies: Essential for rare disease research and trial readiness
• External control arms: Provide comparison groups for single-arm or early-phase trials
• Post-market surveillance: Track safety, adherence, and long-term outcomes after approval
• Health economics and outcomes research (HEOR): Evaluate cost-effectiveness and quality-adjusted life years (QALYs)

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Key Considerations When Using RWE

Working with RWD requires thoughtful planning to ensure the resulting evidence is credible and actionable:

• Data quality: Is the data complete, current, and consistent?
• Standardization: Are data elements coded using accepted standards like ICD, SNOMED, or LOINC?
• Privacy and compliance: Are data collection and sharing compliant with HIPAA, GDPR, and informed consent requirements?
• Study design: Are confounding factors and bias accounted for in the analysis plan?

Transparent methodology and robust statistical techniques are essential for regulatory-grade RWE.

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Tools and Technologies That Enable RWE

• Data lakes and integration platforms to aggregate RWD from disparate sources
• EDC + EHR integration for combining trial and real-world data
• ePRO and wearable integrations to collect patient-generated data
• AI/ML-powered analytics platforms to detect patterns, cohorts, and outcomes
• Patient registries designed to support long-term real-world studies

A strong digital infrastructure is key to leveraging RWE effectively.

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Regulatory and Ethical Frameworks

Both U.S. and international regulators are increasingly accepting RWE—when designed properly:

• FDA’s RWE Program Framework outlines how RWE can support regulatory decisions, especially for label expansions
• EMA guidance focuses on the qualification of RWD sources and transparency in study design
• Ethical considerations include informed consent, data use agreements, and patient privacy
• IRB review may be needed for studies using identifiable or sensitive data

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Challenges and Limitations

Despite its promise, RWE comes with hurdles:

• Data fragmentation: Inconsistent formats and data silos across systems
• Unstructured data: Clinical notes or imaging that require NLP or manual review
• Bias and confounding: Difficult to eliminate without randomization
• Skepticism in regulatory settings: Especially when RWE is used as the primary basis for claims

Mitigating these challenges requires collaboration between data scientists, clinicians, and regulatory experts.

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Case Examples and Applications

• Biopharma label expansion: A drug company used EHR and claims data to support a supplemental indication for an oncology drug.
• Digital therapeutics trial: A digital health startup ran a decentralized trial using wearables and created a virtual control group from RWD.
• Health system research: A provider network combined registry and claims data to study outcomes of diabetes patients under new care models.

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Key Takeaways

• RWE is a valuable complement to clinical trials, enabling broader, longer-term insights
• Successful RWE strategies depend on data quality, interoperability, and regulatory alignment
• As adoption grows, RWE will continue to shape drug development, approval, and access

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Frequently Asked Questions (FAQs)

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1. What’s the difference between RWE and RWD?
RWD is the raw data collected in routine settings; RWE is the analysis and interpretation of that data.

2. Can RWE be used for FDA approval?
Yes—especially for label expansions and post-market decisions, when supported by high-quality data and methods.

3. How is RWE different from Phase IV trials?
Phase IV trials are controlled studies after approval. RWE often involves observational studies using RWD sources.

4. What kinds of studies qualify as RWE?
Registries, retrospective chart reviews, observational cohorts, and analyses of claims or EHR data are all common forms.