Real-World Data

High-quality, real-world data (RWD) can provide meaningful information for patient care and improve health treatments. Barriers to accessing data can limit the ability to conduct high-impact real-world studies.

What is real-world data?

Thanks to technology, health researchers now have enormous amounts of data available at their fingertips. Real-world data (RWD) is derived from sources such as medical claims, electronic health records (EHR) patient-reported data and registries, as well as mobile technologies, wearables and social media. It can capture factors that are not measured in clinical settings. This data can help researchers understand how treatments work in everyday situations and is used for developing real-world evidence.

Why is it needed?

RWD can help us understand disease burden, treatment patterns, patient behaviors, and product performance in settings that represent everyday clinical practice and cannot be captured through a randomized controlled trial (RCT). RWD can be used to address questions that are clinical (adherence, cure rates), economic (resources, utilization), and humanistic (health-related quality of life).

How can RWD enhance research?

Data are key to improving health outcomes and creating efficiencies in our health care system. Each additional data input has the potential to help researchers develop a more holistic understanding of the factors that impact health outcomes. With greater improvements in technology, data required for supporting real-world studies is becoming easier to collect and track from many sources.

RWD can offer the data points needed to help researchers identify and study patterns of health care services and the outcomes for patients in real-world settings. Insights drawn from RWD – including better understanding of disease burden, treatment patterns, patient behaviors, and the impact of treatments in everyday clinical practice – can complement what decision-makers learn from RCTs. RWD can also support the examination of clinical outcomes in larger and more diverse study populations and allow for comparison of groups and multiple alternative interventions. Depending on the dataset, sample sizes can reach tens of millions of data points or more.

Using these data, investigators can study patterns of health care resource utilization and health outcomes for individuals receiving health care services in the real-world environment. For health plan decision-makers, real-world studies can help identify cost-effective, evidence-based care, predict treatment response in different types of patients and evaluate innovative payment designs.

What are the challenges?

Realizing the full potential of RWD requires accessible and interoperable data, trusted curation methods, credible analytics, cutting-edge data skills, and cultural adoption in the health research and regulatory landscape.

For example, data are usually collected for specific administrative purposes, which may not align with pertinent research questions. Data may require a significant amount of time to harmonize different datasets to make them suitable for analysis. 

Good practices for evidence

Read about good practices for collecting and using RWD.

Read more.

What is the Food and Drug Administration's (FDA) impact?

The development of regulatory frameworks to support use of real-world data may serve to foster broadly accepted standards and adoption. The FDA is currently developing a framework for using RWD and real-world evidence (RWE) to support regulatory decision-making. While the FDA has extensive experience using RWD sources to monitor the safety of products, activities to incorporate these methods and tools to assess efficacy and effectiveness of treatments are less established.

In 2019, the FDA issued a draft framework for evaluating the potential use of RWE to help support the approval of a new indication for a pre-approved medicine and satisfy post-approval study requirements. Development of the framework was required under the 21st Century Cures Act. The framework has paved the way for the consideration of a broader set of data sources. These range from well-understood sources such as medical claims, electronic health records, and patient registries, to novel data sources such as mobile technologies, wearables, and patient-reported data.

Read NPC’s comments on the framework.