Drug development and clinical trials are the mainstay of introducing new therapeutic interventions into the healthcare industry. And they, in turn, are based on Real World Evidence. So, Real-world evidence (RWE) isn’t exactly new to the clinical research world, but its role has certainly evolved. Traditionally, data collection is a routine procedure during drug development. “However, structured data collection from the real-world usage of the drug after marketing approval is largely restricted to regulatory safety data collection in the form of pharmacovigilance”. For years, regulatory bodies such as the FDA have relied on real-world data (RWD) to monitor the safety of approved drugs in everyday use. However, recently, a noticeable shift has occurred: researchers and regulators alike are now utilizing RWE not only for safety tracking but also for informing efficacy, guiding clinical decisions, and even supporting regulatory approvals.
So, what’s changed? A lot. Advances in health technology, ranging from electronic health records and mobile apps to biosensors and wearable devices, have generated a wealth of real-world data. These data, while rich in clinical relevance, are often messy, unstructured, and challenging to interpret without the right tools. However, with the integration of technologies such as AI, data analytics, and connected platforms, clinical researchers are now better equipped to transform this information into actionable insights.
In this guide, we’ll explore how RWE fits into the clinical trial ecosystem, what it takes to collect and analyze RWD effectively, and how researchers can harness its potential to design smarter, more inclusive, and more responsive studies. Whether you are a sponsor, investigator, or simply curious about the future of evidence generation, this practical guide will help you navigate the evolving landscape of RWE with clarity and confidence.
Building on what we discussed earlier, real-world evidence (RWE) refers to clinical insights drawn from data collected outside the controlled environment of randomized clinical trials. Unlike RCTs, which operate under strict protocols and select patient groups, RWE is rooted in routine healthcare settings. These reflect how treatments work across diverse populations in everyday life.
The RWE is generated from real-world data (RWD), which comes from various sources. Electronic health records (EHRs), for instance, capture patient histories, diagnoses, and treatments administered in actual clinical practice. Claims databases provide information on healthcare services used and medications prescribed, giving insight into real-world treatment patterns and costs. Patient registries, often disease-specific, collect long-term data that can help evaluate outcomes over time. Then there are mobile health apps and wearable devices, which enable individuals to monitor their symptoms and chronic conditions, generating patient-reported data that adds valuable behavioral and adherence insights. Even social media is being increasingly used to understand patient experiences, including treatment satisfaction and adverse events, shared in real time.
Together, these data sources create a more complete picture of how healthcare interventions perform beyond the structured walls of clinical trials. RWE does not replace RCTs - it complements them by revealing the effectiveness, safety, and usage patterns of therapies in real-world settings.
In the next section, we’ll explore how this type of evidence is being applied and why it is becoming increasingly vital to modern clinical trials.
Having explored what real-world evidence is and where it comes from, the next question is: why does it matter so much in clinical research today?
Randomized controlled trials (RCTs) have long been the gold standard in testing the safety and efficacy of new treatments. But while they offer scientific precision, they do so in a tightly controlled setting that often enrolls a narrow and homogenous group of participants. The reality, however, is that real-world patients do not come in one size or shape - they span all ages, ethnicities, comorbidities, and life circumstances. This is the gap that RWE fills in.
RWE helps researchers understand how a treatment performs across broader, more diverse populations by tapping into data from real clinical settings, including those traditionally excluded from trials, such as pregnant women or underrepresented ethnic groups. This type of diversity is crucial for understanding how therapies function outside the idealized conditions of a clinical trial.
Beyond inclusivity, RWE also addresses other limitations of RCTs. While trials can take years and substantial funding to complete, RWE can deliver timely insights on effectiveness, adherence, and safety in real-world usage. It can detect safety signals that small trial populations might miss, help identify which patients are most likely to benefit from a treatment, and even guide clinicians in tailoring care to individual needs.
Moreover, as healthcare systems shift toward value-based care and personalized medicine, RWE has taken on a central role in four key areas. In regulatory decision-making, agencies like the FDA and EMA now have frameworks in place to use RWE for new indications and post-market studies. In clinical development, RWE supports smarter protocol design and more feasible eligibility criteria. For market access, payers are increasingly demanding evidence of real-world effectiveness and cost-effectiveness to justify reimbursement. In patient-centric research, RWE helps capture outcomes that truly matter to patients, such as quality of life, functional status, and treatment adherence.
The strength of real-world evidence lies in the diversity of its underlying data. These sources are varied, each offering unique insights into treatment outcomes, patient experiences, and healthcare delivery.
Here are the key types of real-world data sources:
Healthcare providers widely use these databases to document patient visits, diagnoses, lab results, medications, and clinical outcomes during daily practice. EHRs are among the most robust and structured sources of RWD. Initiatives like the USFDA’s Sentinel Initiative and the EU’s EHDEN project exemplify large-scale efforts to link and standardize such data for real-time analysis and monitoring.
Registries are organized systems designed to collect prospective, observational data on patients with shared conditions or characteristics. They often follow patients over time to evaluate disease progression, treatment responses, or outcomes.
These include billing and insurance records from pharmacies, providers, and payers. While administrative claims data can reveal real-world treatment patterns, healthcare utilization, adherence, and costs at scale.
Platforms like Facebook, Twitter, and PatientsLikeMe are increasingly recognized as rich sources of patient-reported information. Patients often discuss side effects, treatment changes, and their lived experiences, offering insights that are often overlooked in clinical settings. These platforms generate vast, unstructured datasets that can support considerable data research.
These are digital platforms created and led by patients or advocacy groups to promote collaboration among patients, caregivers, clinicians, and researchers. PPRNs emphasize patient engagement and often serve as a channel for real-time, person-centered data collection.
Devices like fitness trackers, smartwatches, and biosensors continuously collect data on physical activity, sleep, heart rate, and other biometrics. These sources support real-time monitoring and personalized insights.
These are direct reports from patients about their symptoms, quality of life, and treatment preferences—usually collected through surveys or mobile apps. PROs add depth to clinical data by highlighting what matters most to patients.
Together, these data sources offer a 360-degree view of healthcare as it unfolds in the real world.
As we have seen, real-world evidence (RWE) draws from a variety of data sources. It captures how healthcare unfolds beyond the confines of clinical trials. But how does this evidence get used in clinical development?
In practice, RWE supports nearly every phase of the product lifecycle, beginning from early trial design to post-market monitoring. One of its earliest applications is in protocol development, where real-world data (RWD) helps identify realistic inclusion and exclusion criteria based on existing patient populations. This ensures that trial participants better reflect the patients likely to use the treatment in practice.
RWE plays a foundational role through natural history studies. This is particularly applicable for rare diseases, where patient numbers are limited and disease progression is often poorly understood. These studies document how a condition unfolds without intervention, thus helping researchers design more informed and feasible trials.
RWE also enables the creation of external control arms. This is particularly useful for single-arm or early-phase trials where a traditional placebo group may not be feasible or ethical. These comparator datasets are drawn from real-world patients who received standard care, offering a valuable benchmark for evaluating treatment effectiveness.
Following regulatory approval, RWE assumes new roles in post-market surveillance, tracking long-term safety, real-world adherence, and outcomes across broad and diverse populations. This ongoing monitoring helps flag safety signals early and supports product refinement.
RWE becomes the base in health economics and outcomes research (HEOR). It offers insights into cost-effectiveness, quality-adjusted life years (QALYs), and overall value in real-world clinical and economic settings.
RWE is, therefore, a strategic tool integrated throughout the clinical development journey, bringing real-world relevance to evidence generation and decision-making.
As the importance of real-world evidence (RWE) continues to grow, it is essential to remember that its value hinges on how thoughtfully that data is managed and interpreted. Generating credible, regulatory-grade RWE requires rigor, transparency, and careful planning.
One of the foundational considerations is data quality. Are the data complete, up-to-date, and reliable? Gaps or inconsistencies in data can undermine findings, particularly when attempting to determine the effectiveness or safety of a treatment.
Standardization is equally critical. To ensure that data from different sources can be meaningfully integrated and compared, key elements such as diagnoses, procedures, and laboratory values must be coded using established frameworks, including ICD, SNOMED CT, or LOINC. Without this uniformity, analysis can quickly become fragmented or misleading.
Next comes privacy and compliance. In an era of increasing data sensitivity, adhering to regulations such as HIPAA and GDPR is essential. Any use of patient data must respect informed consent and protect individual privacy throughout the research lifecycle.
Then there is study design. Just like with randomized controlled trials, observational studies using RWD must account for potential biases and confounding variables. Without a robust analytical framework, results may reflect correlation rather than causation.
Ultimately, what transforms raw data into reliable evidence is transparent methodology and rigorous statistical analysis. These elements are what elevate RWE to a standard that regulators, clinicians, and patients can trust.
Real-world evidence (RWE) is only as powerful as the infrastructure that supports it. As data sources multiply and become more complex, modern tools and technologies have become essential in collecting, integrating, and analyzing real-world data (RWD) at scale.
To begin with, data lakes and integration platforms serve as foundational tools by aggregating RWD from disparate sources, like EHRs, claims data, registries, or patient-reported outcomes. These platforms help break down data silos, enabling a unified view of patient information across the healthcare ecosystem.
An important advancement is the integration of electronic data capture (EDC) systems with electronic health records (EHRs). By linking structured trial data with real-world clinical records, researchers can bridge the gap between study protocols and everyday practice. This provides a richer context and continuity for clinical research.
On the patient side, tools like electronic patient-reported outcomes (ePRO) systems and wearables are transforming how data is collected. These technologies capture vital signs, symptoms, behaviors, and quality-of-life metrics directly from patients. ePRO occurs in real-time and provides a voice to the lived patient experience.
The rise of AI and machine learning (ML) has enabled deeper, faster, and more precise analysis of vast datasets. These tools can uncover hidden patterns, identify treatment-responsive subgroups, and even predict outcomes, making RWE more actionable than ever.
Finally, digitally enabled patient registries are being designed with long-term, real-world studies in mind. These platforms not only collect structured observational data but also integrate seamlessly with other digital tools to support scalable, ongoing research.
Therefore, RWE, powered by intelligent tools and interoperable systems, is crucial for transforming complex data into actionable evidence. As technology continues to evolve, so too does the potential of RWE to inform and transform clinical research.
As the use of real-world evidence (RWE) expands in clinical research, regulatory decision-making, and ethical frameworks are essential to ensure data reliability, patient privacy, and responsible conduct.
On the regulatory front, agencies are developing structured approaches to guide the use of real-world data (RWD). In the U.S., the FDA’s RWE Program, shaped by the 21st Century Cures Act, outlines how RWD can support regulatory decisions. This framework includes demonstration projects, guidance documents, and mechanisms for internal review and stakeholder engagement. Similarly, in Europe, the EMA and the European Commission are advancing strategies to integrate RWE into medicinal product development and monitoring, with a focus on data quality, interoperability, and transparency. In the same breath, we can seek EMA guidance or an IRB review to qualify RWD sources and use sensitive data, respectively.
Equally important are the ethical frameworks that govern RWE studies. Ensuring informed consent is fundamental, as it requires patients to understand how their data will be used and any associated risks and benefits. Data privacy and security are non-negotiable, guided by laws like GDPR and HIPAA, and reinforced by de-identification practices. Ethical oversight also encompasses scientific validity, which requires rigorous study designs and methods to ensure credible outcomes, as well as transparency through the disclosure of methodology and potential conflicts of interest. Ultimately, ethics committee reviews help safeguard participant rights and ensure that studies adhere to ethical standards.
Together, these frameworks provide the guardrails necessary for using RWE responsibly. They ensure that innovation in evidence generation does not come at the expense of trust, privacy, or scientific rigor.
While real-world evidence (RWE) has opened exciting possibilities in clinical development, it is not without its challenges. The very nature of real-world data (RWD) is diverse, unstructured, and collected outside controlled settings. This can create hurdles that researchers must navigate carefully.
One major issue is data fragmentation. RWD is often scattered across multiple platforms like EHRs, claims systems, and registries, each using different formats and standards. This lack of interoperability complicates data aggregation and analysis, making it harder to draw cohesive insights.
Then there is the challenge of unstructured data. Clinical notes, diagnostic imaging, and narrative reports can hold valuable information but require advanced tools, such as natural language processing (NLP) or manual review, to make them usable.
Bias and confounding are inherent risks in observational data. Without the safeguards of randomization, it is difficult to fully eliminate underlying factors that can skew results, affecting the reliability of causal interpretations.
Moreover, skepticism in regulatory settings remains a barrier, especially when RWE is used as the primary source of evidence. Regulators demand rigorous study design, data integrity, and transparent methodologies to accept RWE for approvals or label expansions.
Addressing these limitations requires more than just better technology. It calls for collaboration among clinicians, data scientists, and regulatory experts. Together, they can build the standards, tools, and trust needed to transform real-world data into high-quality evidence that meets scientific and regulatory expectations.
The promise of real-world evidence (RWE) becomes most compelling when it is seen in action. Across sectors like biopharma, digital health, and healthcare delivery, RWE is no longer theoretical. It is actively shaping decisions, accelerating innovation, and improving outcomes.
In the biopharmaceutical space, a major drug company leveraged EHR and claims data to support a label expansion for an oncology drug. By analyzing treatment patterns and patient outcomes outside of a traditional trial, they provided regulators with real-world validation of the drug’s effectiveness in a broader patient population. The supplemental indication was approved, demonstrating how RWE can complement clinical trial findings and expedite access to therapies.
Another example is a digital therapeutics startup that conducted a decentralized clinical trial using wearable devices to collect continuous health data from patients in their environments. Rather than recruit a traditional control group, they created a virtual comparator arm using real-world data. This not only saved time and cost but also increased the relevance of their findings by mirroring real-world conditions.
In yet another example, an extensive healthcare system integrated registry and claims data to evaluate outcomes among patients with diabetes enrolled in a new care model. The findings helped refine clinical protocols and informed value-based care strategies. All were using evidence generated outside the boundaries of a controlled trial.
These examples reflect a broader trend of RWE being applied with purpose, creativity, and scientific rigor. It is not replacing traditional research, but it is expanding what is possible within it.
As we have seen throughout this guide, real-world evidence (RWE) is no longer a concept for the future. It has become a vital and growing force in the contemporary clinical research landscape. While randomized controlled trials (RCTs) remain the cornerstone of regulatory science, RWE offers the ability to look beyond the confines of controlled environments and capture how treatments perform in the complexity of real-life care.
When thoughtfully applied, RWE serves as a powerful complement to clinical trials. It offers broader population insights, longer-term outcomes, and real-time feedback that can shape everything from trial design to regulatory approval and post-market strategy. But the quality of that evidence depends on the foundation it is built upon. Data must be clean, consistent, and interoperable. Studies must be both ethically sound and methodologically rigorous, and they must also align with evolving regulatory expectations.
As more stakeholders, ranging from biopharma companies to regulators and healthcare providers, embrace RWE, its influence will continue to grow. Whether it is about accelerating drug development, supporting supplemental indications, informing health policy, or guiding value-based care, RWE will continue to help bridge the gap between research and reality.
Real-world data (RWD) refers to health-related information collected outside of traditional clinical trials, such as from electronic health records, claims databases, or patient registries. Real-world evidence (RWE), on the other hand, is the clinical insight or conclusions drawn from analyzing and interpreting RWD using sound research methods.
Yes, the FDA increasingly supports the use of real-world evidence (RWE) for regulatory decisions, especially in label expansions, post-market surveillance, and rare disease treatments. However, the RWE must be generated using reliable, high-quality real-world data (RWD) and rigorous methodologies to ensure validity and compliance with regulatory standards.
Phase IV trials are formal, controlled studies conducted after a drug’s market approval to monitor safety and effectiveness. Real-world evidence (RWE), by contrast, typically arises from observational analyses of real-world data (RWD) gathered in routine clinical settings, offering broader insights into a treatment’s real-life performance and outcomes.
Studies that generate real-world evidence (RWE) include patient registries, retrospective chart reviews, observational cohort studies, and analyses of large healthcare datasets such as insurance claims or electronic health records (EHRs). These studies are conducted outside of controlled clinical trial settings and help assess treatment effectiveness, safety, and usage patterns.
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