Real world data examples: 10 Essential Types

What Real World Data Examples Are Changing Healthcare

Real world data examples go far beyond traditional electronic health records. From wearable sensors tracking heart rhythms to insurance claims revealing treatment patterns, these diverse data sources are revolutionizing our understanding of patient care and drug effectiveness.

Common real world data examples include:

• Electronic Health Records (EHRs) – Patient diagnoses, lab results, clinical notes
• Medical Claims Data – Insurance billing, Medicare records, treatment costs
• Patient Registries – Disease-specific databases, rare condition tracking
• Wearable Device Data – Heart rate, sleep patterns, activity levels
• Pharmacy Records – Prescription fills, medication adherence
• Medical Imaging – CT scans, MRIs, X-rays analyzed by AI
• Genomic Data – DNA sequencing, genetic variants
• Patient-Reported Outcomes – Symptom surveys, quality of life measures

The shift toward real-world evidence is significant. Over 90% of life science organizations now use real-world data in clinical development, and the FDA reviewed 90 examples of real-world evidence submissions from 2012-2019 alone, spanning medical devices to drug approvals.

This data is powerful because it captures what happens outside controlled clinical trials. While randomized controlled trials (RCTs) use selected populations in controlled settings, real-world data shows how treatments perform across diverse patient groups in everyday practice.

The 21st Century Cures Act of 2016 directed the FDA to develop frameworks for evaluating real-world evidence. Regulatory agencies now routinely accept this data to support drug approvals, expand indications, and monitor post-market safety.

I’m Maria Chatzou Dunford, CEO and Co-founder of Lifebit. With over 15 years in genomics and biomedical data, I’ve seen how analyzing real world data examples from federated environments can open up breakthrough insights for precision medicine.

Basic real world data examples glossary:

• real world data
• real world data definition
• real world data impact on price reimbursement

Unpacking the Sources: 10 Real World Data Examples

Real world data examples are snapshots of healthcare as it actually happens—messy, complex, and human. Unlike controlled clinical trials, this data captures the real story of how patients live and how treatments work in practice.

Each data type tells a different part of the patient journey, from the clinical picture to medication adherence and social determinants of health. Let’s explore ten powerful sources reshaping modern medicine.

Electronic Health Records (EHRs): A Foundational Real World Data Example

Digital medical charts, or Electronic Health Records (EHRs), are a goldmine of insights, capturing everything from diagnoses and lab results to doctors’ notes. Their value lies in their completeness, following patients through their healthcare journey to detail disease progression and treatment responses. For example, researchers use machine learning on EHRs to identify Type 2 Diabetes patients and predict complications.

However, EHRs were built for patient care, not research, so data can be inconsistent or incomplete. Recognizing this, the FDA provides guidance on using EHRs in clinical investigations, balancing their potential with the need for careful data handling.

Medical Claims and Billing Data

Every doctor visit or prescription creates a financial paper trail. This medical claims and billing data offers a window into healthcare patterns across entire populations. This administrative data captures services provided, medications dispensed, and costs. For instance, Medicare claims enabled a study of 180,000 cataract surgeries, revealing patterns impossible to capture otherwise.

The massive scale of claims data, covering millions of patients over years, provides insights into healthcare costs and long-term outcomes. Researchers use it to understand the economic burden of diseases like nonalcoholic fatty liver disease, informing both medical and policy decisions.

Patient and Disease Registries

Patient and disease registries are specialized databases that track people with specific conditions or treatments, creating detailed portraits of patient populations. They are especially valuable for studying rare diseases, where traditional trials are difficult. For example, the Dutch Scalp Cooling Registry tracked 1,411 cancer patients to provide real-world evidence on scalp cooling systems, a feat that would have been difficult with conventional trials.

Medical device registries often monitor products post-market, tracking performance in diverse populations. This surveillance helps spot potential issues and understand long-term outcomes. You can learn more about how registries help us understand patient outcomes and their importance in research.

Patient-Generated Data: More Real World Data Examples from Wearables and Apps

Patient-generated data from smartwatches, health apps, and symptom surveys are revolutionizing our understanding of health. Digital Health Technologies (DHTs) have turned patients into active data generators. For example, data from over 15,000 women using a mobile contraception app supported its FDA approval.

Wearables and apps offer continuous monitoring of daily life, which is impossible during clinical visits. For conditions like Parkinson’s disease, this data reveals patterns that sporadic appointments would miss. The FDA recognizes the importance of the patient perspective, developing specific guidance on Patient-Reported Outcomes to help evaluate treatment effectiveness.

Radiographic and Medical Imaging Data

CT scans, MRIs, and X-rays create visual records of the human body. This radiographic and medical imaging data is now more powerful with AI, which can see patterns that radiologists might miss. Deep learning algorithms analyze images to classify lung patterns, detect cancers, and track disease progression. Studies show that combining imaging with other real-world data sources yields valuable insights.

AI transforms these static images into dynamic predictions about disease trajectories and treatment responses, advancing personalized medicine.

In Vitro Diagnostics (IVD) Data

Lab tests, from blood panels to biomarker analyses, generate In Vitro Diagnostics (IVD) data, revealing what’s happening at a cellular level. The FDA has used real-world evidence from IVD data in eight regulatory decisions, showing how routine lab results can support new diagnostic approvals and monitor their performance.

Biomarkers and genetic markers from IVD tests are essential for understanding disease prevalence, guiding treatment, and monitoring patient response to therapy.

Genomic Data

Your DNA is a unique real world data example. Genomic data from DNA sequencing is enabling new approaches to personalized medicine and pharmacogenomics. By linking genetic variants to disease and drug responses, doctors can tailor treatments. Large-scale biobanks are building massive genomic datasets that reveal population-level patterns with individual precision.

This genetic information helps predict medication responses, adverse reactions, and disease risk, acting as a personalized healthcare instruction manual.

Data from Pragmatic Clinical Trials (PCTs)

While traditional trials occur in perfect conditions, Pragmatic Clinical Trials (PCTs) bring research into the real world. These hybrid studies blend scientific rigor with everyday clinical practice, testing treatments in typical healthcare settings with broader patient populations. For example, a trial across 40 U.S. dialysis centers used real-world data to compare treatment strategies, providing evidence directly applicable to routine care.

PCTs bridge the gap between controlled research and real-world healthcare, generating real world data examples that are immediately relevant to physicians and patients.

Pharmacy Data

Every filled prescription contributes to the network of pharmacy data. These records reveal medication adherence, treatment trends, and how patients use medications outside of clinical supervision. Dispensing and refill histories show the gap between what’s prescribed and what’s taken. For instance, studies on psoriasis therapies used pharmacy data to understand real-world adherence and its impact on outcomes.

This information is crucial, as a medication is ineffective if not taken correctly. Pharmacy data helps explain why treatments succeed or fail in the real world.

Socioeconomic and Environmental Data

Health is influenced by more than biology; socioeconomic and environmental data captures these social determinants of health. Geographic data can reveal environmental hazards, while socioeconomic information highlights barriers to healthcare access. For example, researchers used real-world data during COVID-19 to study how lockdowns affected mental health across communities.

Factors like food insecurity, transportation, and air quality influence health. Linking this non-clinical data with medical records helps researchers identify at-risk communities and develop targeted interventions to address health disparities.

How the FDA Leverages Real-World Evidence

The U.S. Food and Drug Administration (FDA) has been a significant driver in the adoption and integration of Real-World Evidence (RWE) into regulatory decision-making. They’re increasingly recognizing that RWE can provide crucial insights that complement, and in some cases even supplement, traditional clinical trial data, ultimately accelerating patient access to new treatments and ensuring product safety in the long run.

The FDA’s Framework for RWE

The push for greater use of RWE gained significant momentum with the 21st Century Cures Act of 2016. This legislation specifically called for the FDA to establish a framework for evaluating the use of RWE to support regulatory decisions for medical products, including drugs, biologics, and medical devices.

The FDA’s commitment to RWE is clear: they aim to realize the full potential of “fit-for-purpose” RWD to advance therapeutic product development and strengthen regulatory oversight. Their framework outlines how RWE can be used to support approvals of new indications for already approved medical products, or to satisfy post-approval study requirements. This means that data collected outside of traditional trials can now directly influence what treatments are available to us and how they are used. You can explore the details of the FDA’s Framework for its Real-World Evidence Program to understand their comprehensive approach.

RWE in Action: Regulatory Approvals

The FDA’s accept of RWE isn’t just theoretical; it’s actively shaping regulatory decisions. From fiscal years 2012 through 2019, the FDA reviewed 90 examples of submissions illustrating RWE usage in regulatory decision-making. These included:

These examples highlight the diverse ways RWE is being used. For instance, a pediatric contact lens study aimed to demonstrate that the rate of Microbial Keratitis (MK) is no higher than 0.4% per patient-year, leveraging real-world data from actual patient use. In another case, the drug Blinatumomab received accelerated approval for a new indication in leukemia, partly supported by an external control arm derived from RWD. This shows how RWE can help bring life-saving treatments to patients faster, especially for conditions where large, traditional trials are challenging.

The Role of Coordinated Registry Networks

To effectively leverage RWD, robust data infrastructure is essential. This is where coordinated registry networks play a pivotal role. Initiatives like the National Evaluation System for health Technology (NEST) and the Sentinel Initiative are designed to integrate data from various sources – including clinical registries, EHRs, and medical billing claims – to gather comprehensive evidence on medical product safety and effectiveness, particularly for postmarket device evaluation.

These networks allow for the pooling and analysis of vast amounts of real world data examples, enabling us to monitor the long-term performance of medical devices and drugs in diverse patient populations. They are crucial for identifying safety signals that might not appear in smaller, pre-market trials. We encourage you to learn about the NEST Coordinating Center to understand how these collaborative efforts are building a stronger, more efficient system for health technology evaluation.

Navigating the Challenges of Real-World Data

While real world data examples offer incredible potential, working with them presents unique challenges. RWD wasn’t collected for research; it was gathered during routine care, billing, or daily life. This authenticity is its value, but it also means the data can be messy, incomplete, or inconsistent. We must address these issues to ensure the evidence we generate is reliable and robust.

Data Quality, Integrity, and Fitness-for-Use

Ensuring RWD quality and integrity is the biggest hurdle. Unlike data from controlled trials, real world data examples are often imperfect.

Missing data is a common issue, as busy clinicians may not complete every field, or patients may miss appointments. These gaps complicate analysis. Data standardization is also crucial when combining data from multiple sources that use different formats or coding systems.

The concept of fitness-for-use is central: is a dataset suitable for a specific research question? A dataset for studying medication adherence might not work for analyzing rare side effects due to a lack of detail.

Data cleansing, curation, and improving interoperability (the ability of systems to share data) are essential steps. The goal is to create regulatory-grade data that meets strict standards for healthcare decision-making.

Analytical and Methodological Problems

After data preparation, analytical challenges begin. RWD is observational, meaning we can see correlations but not definitively prove causation. This leads to the risk of confounding bias, where an unmeasured factor, not the treatment, is responsible for an outcome. For example, patients on a certain drug might have better outcomes because they are more health-conscious, not because of the drug itself.

To address this, researchers use sophisticated study design and methods like target trial emulation, which mimics the structure of a randomized trial using observational data. The rise of machine learning in RWD analysis offers new opportunities but also requires careful validation to ensure reliability, especially for AI/ML-based Software as a Medical Device (SaMD).

Privacy and Governance

Data security and privacy are non-negotiable when handling sensitive patient information. Regulations like HIPAA mandate strict compliance, which often involves de-identification to remove personal identifiers. However, even de-identified data requires robust governance frameworks.

Secure data access has been revolutionized by federated learning. Instead of moving data to a central location, which creates security risks, federated learning trains AI models on data where it resides—in secure hospital or research environments. The models learn without the data ever leaving its secure location.

This approach improves security, enables large-scale analysis, and addresses patient consent concerns, as data is not transferred between organizations. The insights flow, but the data stays put.

Lifebit’s platform is built on these principles. Our Trusted Research Environment (TRE) allows researchers to analyze real world data examples while upholding the highest standards of privacy and compliance, open uping the potential of RWD while respecting patient trust.

Frequently Asked Questions about Real World Data

Diving into real world data examples can feel like learning a new language. With terms like RWD and RWE, it’s easy to get lost. Let’s clear up some common questions.

What is the main difference between Real-World Data (RWD) and Real-World Evidence (RWE)?

Many people get tangled up with these terms. Think of it like this:

• Real-World Data (RWD) is the raw ingredient. It’s the “what.” It includes data from EHRs, claims, fitness trackers, and disease registries—information about patient health collected during routine care, outside of a formal research study.

• Real-World Evidence (RWE) is the finished product. It’s the “so what.” RWE is the clinical insight about a medical product’s use, effectiveness, or risks that is generated from analyzing RWD.

As the FDA explains, RWE is “the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.” You can find the FDA explanation of RWD and RWE on their site. In short, RWD is the raw material, and RWE is the knowledge gained from it.

Can Real-World Evidence replace Randomized Controlled Trials (RCTs)?

Generally, no, Real-World Evidence (RWE) is not meant to replace Randomized Controlled Trials (RCTs). RCTs remain the “gold standard” for determining if a new treatment is effective and safe because their controlled design minimizes bias.

However, RWE plays a crucial complementary role. While RCTs show efficacy in a controlled setting, RWE demonstrates effectiveness in the diverse real world. RWE offers insights RCTs often can’t, including:

• Broader populations: RWE reflects treatment performance across a wider variety of people.
• Long-term outcomes: RWE can track patients for far longer than most trials.
• Real-world effectiveness: RWE shows how treatments work in patients with other health issues or medications.

In some situations, RWE can be the primary evidence, especially for:

• Rare diseases: Where large RCTs are impossible.
• Ethical reasons: When withholding a treatment is unethical.
• External control arms: RWE can create comparison groups for single-arm studies.

Instead of rivals, RWE and RCTs are complementary tools that together provide a fuller picture of a treatment’s value.

Why is real-world data so important for rare diseases?

Real world data examples are vital for advancing rare disease treatments for several reasons:

• Small patient populations: Rare diseases affect few people, making large, traditional RCTs nearly impossible. RWD allows researchers to gather information from every available patient.
• Data pooling: RWD from sources like disease registries and EHRs can be combined to create larger, more meaningful datasets.
• Understanding disease progression: RWD helps map the natural history of a rare disease, providing a baseline to evaluate new treatments.
• Supporting drug approval: Regulatory bodies like the FDA are more open to using high-quality RWE to support approvals for rare disease therapies, accelerating patient access.

RWD is a lifeline for rare disease research, enabling evidence generation and speeding up the development of needed treatments.

Conclusion

These real world data examples show a dramatic shift in healthcare research, where the complex reality of patient care is now being analyzed at an unprecedented scale. Every doctor’s visit, prescription, and smartwatch reading contributes to a data ecosystem that helps researchers understand what truly works. Diverse sources, from structured insights of EHRs and claims data to the granular detail from wearables and the biological complexities of genomic information, are creating new opportunities.

The FDA’s acceptance of this evidence accelerates medical innovation. The 90 RWE submissions mentioned earlier represent real treatments reaching patients faster because effectiveness can be shown using data from everyday practice.

This regulatory acceptance of real-world evidence leads to more personalized and effective patient care. We can now see how treatments perform across diverse populations in real time, rather than just hoping trial results translate.

Challenges like data quality, privacy, and analytical complexity remain, but advancements in federated learning, AI, and secure governance are providing solutions. We are improving our ability to turn raw data into reliable insights.

The key is to securely access, harmonize, and analyze these datasets. Our federated AI platform is designed for this purpose, enabling secure access to global biomedical data while ensuring privacy and compliance.

We are committed to empowering researchers to harness these valuable real world data examples safely and effectively. Every data point represents a person, and every insight brings us closer to better treatments for patients everywhere.

Find how to securely leverage Real-World Data for your research