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A strong clinical development plan for a medical device should extend beyond the pivotal trial to anticipate the evidence needs that emerge across the product lifecycle. While pivotal studies remain essential for demonstrating safety and effectiveness under defined conditions, sponsors increasingly need post market evidence to support payer expectations, inform clinical adoption, monitor long-term performance, and enable future indication expansion. With expanded access to real-world data, rapid advances in analytics and AI, and clearer regulatory direction on the appropriate use of real-world evidence (RWE), sponsors have a timely opportunity to integrate RWE into their evidence-generation strategies. HCC brings an established network, operational expertise, and Biometrics capabilities to help sponsors design and execute these plans. To understand where RWE fits within a pivotal trial strategy, itis helpful to begin with what RWE is, how it is generated, and how regulators evaluate its use.
An Overview of Real-World Evidence
Real-world evidence (RWE) is best understood in relation to real-world data (RWD). Pursuant to the 21st Century Cures Act, the FDA published the Framework for FDA’s Real-World Evidence Program in December 2018, establishing the core definitions now widely used across U.S. regulation. The FDA has also issued medical device-specific guidance on the use of RWE to support regulatory decision-making, reinforcing the growing role of RWE in the device evidence landscape.
- Real-World Data (RWD) are “data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources.” In practice, RWD includes the raw input generated during routine care, such as patient demographics, diagnoses, treatments, laboratory results, and outcomes collected outside the controlled setting of a randomized trial.
- Real-World Evidence (RWE) is “the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.” RWE is therefore the analytic output: the evidence produced when RWD is curated, transformed, and rigorously analyzed to answer a question about a product’s safety, effectiveness, or use in practice.
This distinction matters because RWD alone is not evidence. It becomes RWE only when the data are fit for the question, appropriately curated, and analyzed using a study design capable of producing credible clinical conclusions.
The Framework identifies three central considerations that guide the FDA’s evaluation of any RWE submission:
- Whether the RWD is fit for use, meaning relevant and reliable for the regulatory question at hand;
- Whether the study design used to generate the RWE can provide scientifically adequate evidence to answer that question;
- Whether the conduct of the study meets applicable FDA regulatory requirements.
Common sources of RWD, as recognized by the FDA and the broader literature, include:
- Electronic health records (EHRs) — clinical notes, diagnoses, procedures, laboratory and imaging results; rich in detail, but often unstructured and not originally collected for research.
- Administrative claims and billing data — procedures, diagnoses, and costs submitted to payers; large and longitudinal, though often limited in clinical granularity.
- Product and disease registries — structured, standardized datasets collected across sites for a specific device, drug, or condition.
- Patient-generated data — data from wearables, mobile or digital health technologies, and sensors that capture activity, heart rate, symptoms, and related measures between visits.
- Patient-reported outcomes (PROs) — symptoms, quality of life, and treatment burden reported directly by patients.
- Pharmacy and dispensing data — prescription fills, adherence patterns, and treatment switching.
- Other emerging sources — laboratory and imaging repositories, vital records and death registries, and linked environmental or social datasets that enrich the understanding of patient outcomes.
How RWE differs from randomized controlled trials (RCTs) and why both matter
RCTs remain the gold standard for establishing efficacy and safety under controlled conditions. RWE is particularly valuable for understanding how products perform in routine clinical practice across broader patient populations, including post-market evidence generation, long-term outcomes, comparative effectiveness, reimbursement or policy decisions, and circumstances where an RCT may be impractical or unethical.
The comparison below highlights how RWE and RCTs answer different but complementary evidence questions.
Why RWE matters in MedTech
In MedTech, RWE is especially important because device performance is shaped not only by the technology itself, but also by operator technique, workflow integration, patient characteristics, and clinical setting. For sponsors, this means RWE can help demonstrate whether a device performs consistently, safely, and effectively across the variability of everyday care.
- Real-world performance: Shows how devices perform in routine clinical practice.
- Broader applicability: Captures variation across sites, users, and patient population.
- Operational insight: Reflects the impact of operator technique and workflow integration.
- Lifecycle evaluation: Supports assessment after device updates and broader adoption.
- Post-market oversight: Strengthens safety and effectiveness monitoring after launch.
- Decision support: Can inform regulatory and market access decisions for medical devices.
Why we are seeing more RWE recently
The recent rise in RWE reflects a convergence of technological, regulatory, economic, and practical factors. Together, these forces have moved RWE from a supplemental consideration to a more central part of evidence planning, particularly when sponsors need to support regulatory, clinical, payer, and post market objectives with a coordinated evidence strategy.
• Digitization of health data and EHR adoption - Widespread EHR adoption has created large volumes of machine-readable clinical data that can now be analyzed at scale.
• Advances in analytics, AI, and machine learning - Modern analytic methods can extract insights from unstructured text, link records across sources, model disease progression, and evaluate large, complex datasets more efficiently.
• Regulatory encouragement and legislative mandates - The 21st Century Cures Act and FDORA signaled a clear regulatory expectation that RWE will play a larger role in decision-making.
• Cost pressures and payer or HTA demands - Rising development costs and value-based payment models have increased demand for evidence of real-world effectiveness and value.
• COVID-19 acceleration - The pandemic accelerated adoption of decentralized trial methods, telemedicine, wearables, and EHR-based outcome capture, making real-world approaches more familiar and feasible.
• Interoperability improvements - Standards such as HL7 FHIR, OMOP, and CDISC are gradually improving the ability to aggregate and analyze data across systems, even though implementation remains uneven.
Caveats
- Confounding and internal validity remain major limitations - Because most RWE is observational, it cannot fully replicate the balancing effect of randomization; causal conclusions therefore require careful interpretation.
- Data quality is often the limiting factor - Because RWD is collected primarily for care delivery or billing rather than research, it may be incomplete, inconsistently coded, or insufficiently validated.
Regulatory acceptance also depends on whether the RWD source, study design, endpoint definitions, data provenance, and analytic methods are appropriate for the specific question being asked. Early planning is therefore essential: RWE is most persuasive when the evidence objective, data source, protocol, statistical approach, and regulatory use case are aligned before data extraction and analysis begin.
An HCC Case Study
At HCC, the Biometrics team is currently supporting RWE studies through a partnership with Celéri Health, a company specializing in pain-focused real-world evidence research. Through this partnership, sponsors gain support for designing, managing, and analyzing real-world studies that can generate meaningful evidence on patient outcomes, treatment patterns, and product performance in routine clinical settings.
As RWE becomes more central to MedTech evidence planning, sponsors need partners who can translate regulatory expectations, real-world data sources, and analytic methods into practical study execution. HCC is positioned to support that work by combining operational execution, Biometrics expertise, and an evidence-focused approach across the product lifecycle.
