Understanding the potential, challenges and use cases for real-world evidence

provider and patient

by Jared Kimble

Efforts to properly understand and deploy real-world evidence (RWE) are under way across the life sciences industry. Regulatory authorities and companies are assessing how best to make use of RWE and how to deal with some of the challenges that arise when managing large amounts of dispersed data.

During the Drug Information Association Real World Evidence Conference, held in Cambridge, Mass., in November 2019, representatives from the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA) and Health Canada discussed their RWE projects. The FDA noted it has three main goals for RWE. First, it must be fit for purpose, meaning the data should be assessed for completeness, consistency, accuracy and whether it contains all the critical data elements needed to evaluate a medical product and its claims. Second, can the trial or study design used to generate RWE provide adequate scientific evidence to fulfill regulatory requirements? Lastly, a study involving RWE must meet the FDA’s study standards and conditions.

The agencies also talked about the challenges presented by RWE. One challenge is the fact that data often originates from electronic health records (EHRs), where the data may be unstructured, poor quality and/or in inconsistent formats, such as doctors’ notes that aren’t entered in a standardized way. Another challenge is that during clinical trials, a patient’s daily activities are monitored, but in the real world, patients don’t go to the doctor frequently, and therefore the EHR data is less regular and consistent.

The issue of data quality, or FAIR data, was also a major theme at the BioData World Congress in Basel, Switzerland, in December 2019. FAIR data refers to findable, accessible, interoperable and reusable data as the key elements required to improve the chances of handling data successfully for various uses.

Use cases for RWE

Both conferences offered examples RWE usage. For example, data is collected during decentralized trials, either from forms filled out by patients or from wearables that feed data about the patient back to the trial organizers.

RWE might also be leveraged for observational purposes. For example, researchers from academia working with the FDA are replicating clinical trials by drawing on RWE to make comparisons, starting with replicating randomized cardiovascular trials using insurance claims and applying epidemiological techniques. However, if a trial is several years old, accurate comparisons may be harder to make since health standards — including standards of care and advanced treatments — and data standards will have changed. In 2017, it was estimated that only 15 percent of U.S.-based trials could have been replicated using real-world data (RWD); however, this doesn’t account for the value RWD provides in conjunction with randomized clinical trials.

One of the most valuable uses of RWE is in advancing patient-centered care. During her presentation at BioData, Catherine Barras, director of Global Healthcare Industry Advisory Services at DXC Technology, spoke about a data-enabled health economy, where information is continuously generated from various sources: mobile devices, the internet of things, augmented reality, patient surveys and so on. All that data is fed into the RWE platform from which insights are generated, which in turn generates additional data that is fed back into the platform and back into the health ecosystem to ultimately drive patient-centered care.

Making RWE in studies a reality

Given the challenges and potential, the priority for the life sciences industry must be to manage the quality of the data that’s used. The emphasis should be on data cleansing, fixing and quality checks as the data is collected, because once it has been aggregated, it becomes much harder to do those things. Importantly, creating standardized datasets is essential to avoid errors when interpreting the information.

The greatest potential lies in RWE platforms that are built on top of common data models — Fast Healthcare Interoperability Resources (FHIR) and Observational Medical Outcomes Partnership (OMOP) — because the data is standardized, which makes it easy to share and understand. DXC Technology’s Open Health Connect platform allows users to ingest both FHIR and OMOP data to create a patient-centered data-collection point for gaining insights into real-world data.


Jared Kimble has over 14 years of experience in the life sciences industry. His expertise ranges from software design and development to solution architecture where he is currently the offering lead for Life Sciences Regulatory Transformation Services. He leads the management and development effort for many key projects and was involved in several on-site engagements. Before joining DXC, Jared worked as a software engineer developing applications dedicated to providing financial exchange services for banks and financial institutions.

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