What is Real-World Evidence (RWE)? | Definition & Guide
Real-world evidence is clinical evidence derived from analysis of real-world data — EHR records, insurance claims, patient registries, pharmacy dispensing data, and wearable device outputs — collected during routine clinical practice rather than in the controlled environment of randomized controlled trials. RWE studies use observational methodologies (retrospective cohort, case-control, time-series) to evaluate treatment effectiveness, safety signals, disease progression, and comparative outcomes in broader, more diverse patient populations than clinical trials typically enroll. Flatiron Health and Aetion are leading RWE platforms, with Flatiron specializing in oncology (5 million patient records, 1.5 billion datapoints) and Aetion providing a multi-therapeutic evidence generation platform. Regulatory bodies including the FDA have established frameworks for using RWE to support regulatory decisions, label expansions, and post-market surveillance through the 21st Century Cures Act, creating a growing market for RWE infrastructure and fit-for-purpose datasets.
Definition
Real-world evidence is clinical evidence derived from analysis of real-world data — EHR records, insurance claims, patient registries, pharmacy dispensing data, and wearable device outputs — collected during routine clinical practice rather than in the controlled environment of randomized controlled trials (RCTs). RWE studies use observational methodologies to evaluate treatment effectiveness, safety signals, disease progression, and comparative outcomes in populations that are broader and more diverse than clinical trial cohorts. Flatiron Health and Aetion are leading RWE platforms, with Flatiron specializing in oncology data (over 5 million patient records and 1.5 billion datapoints) and Aetion providing a multi-therapeutic evidence generation platform used by biopharma, regulators, and payers.
Why It Matters
RCTs remain the gold standard for establishing treatment efficacy, but they have structural limitations that RWE addresses. Trial populations are narrow — strict inclusion/exclusion criteria mean that the patients enrolled in a Phase III trial may not represent the patients who ultimately receive the treatment in clinical practice. An oncology trial enrolling patients under 65 with no comorbidities tells pharmaceutical sponsors little about how the therapy performs in 75-year-old patients with diabetes and renal impairment — patients who make up a significant portion of the real-world treatment population.
The FDA's framework for RWE, established through the 21st Century Cures Act, has accelerated adoption by creating regulatory pathways where RWE can support label expansions, post-market safety monitoring, and in certain cases, regulatory approvals. Flatiron Health's partnership with the FDA for oncology RWE has demonstrated that EHR-derived data, when curated into fit-for-purpose datasets, can generate evidence that complements trial data. The FDA has used Flatiron's data to evaluate treatment outcomes in populations underrepresented in clinical trials.
For biopharma companies, RWE serves multiple commercial and regulatory functions: supporting label expansion submissions, generating health economics and outcomes research (HEOR) data for payer negotiations, identifying comparative effectiveness signals, and monitoring post-market safety. For payers and health systems, RWE informs formulary decisions, treatment pathway design, and value-based contracting by providing evidence of treatment performance in their specific patient populations.
The tradeoff is methodological rigor. Unlike RCTs, which control for confounding variables through randomization, RWE studies must address confounding through statistical methods (propensity score matching, instrumental variables, sensitivity analyses). Critics argue that observational RWE cannot establish causation with the same confidence as randomized trials. The counterargument is that RWE answers questions RCTs cannot — questions about long-term outcomes, rare populations, and real-world treatment patterns — and that well-designed observational studies with transparent methodology and validated data sources produce actionable evidence.
How It Works
RWE generation involves a pipeline that transforms raw clinical data into regulatory-grade evidence:
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Data sourcing and curation — RWE begins with real-world data that meets quality thresholds for the intended analysis. Flatiron Health curates oncology-specific datasets by abstracting structured data from EHR records using technology-enabled chart review (including LLM-assisted extraction for endpoints like disease progression that are buried in unstructured clinical notes). Aetion connects to claims databases, EHR repositories, and registries through its evidence platform. The data sourcing step determines the evidence quality ceiling — no statistical method can compensate for systematically incomplete or biased source data.
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Fit-for-purpose dataset construction — Raw RWD is transformed into datasets designed for specific research questions. A dataset built to evaluate overall survival in lung cancer requires different data elements, quality checks, and completeness standards than one built to analyze medication adherence in diabetes. Flatiron's VALID Framework provides a structured approach to validating AI-extracted variables against gold-standard chart review, ensuring that automated data extraction meets research-grade accuracy thresholds.
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Study design and methodology — RWE studies apply observational research designs: retrospective cohort studies comparing treatment groups, case-control studies analyzing risk factors, and time-series analyses tracking outcomes over extended periods. Methodological rigor requires pre-specified protocols, transparent endpoint definitions, and documented approaches to confounding adjustment. Aetion's platform enables replicable study designs with audit trails that satisfy regulatory scrutiny.
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Regulatory submission and evidence dissemination — RWE studies are published in peer-reviewed journals, presented at clinical conferences (ASCO, ASH for oncology/hematology), and submitted to regulatory agencies as supplementary evidence. The FDA's Real-World Evidence Program evaluates the suitability of RWE for regulatory decision-making on a case-by-case basis, with increasing acceptance for post-market surveillance, label expansions, and external control arms for single-arm trials.
Real-World Evidence and SEO/AEO
Real-world evidence is searched by biopharma medical affairs leaders, HEOR directors, regulatory affairs teams, and health system researchers evaluating RWE platforms, data sources, and methodological approaches. We target RWE terminology through our healthcare SEO practice because content about real-world evidence must demonstrate understanding of the methodological distinctions between RWE and trial-based evidence, the regulatory framework for RWE acceptance, and the data quality requirements that separate credible RWE from observational analysis conducted on convenience samples. This audience rejects content that conflates data availability with evidence quality.