Can real-world data help close the information gap in rare disease research?

Depending on where you live, the term “rare disease” is defined not just by words, but by the numbers. For instance, in the United States alone, a disease is considered rare if it affects fewer than 200,000 individuals. In the European Union, a rare disease is classified as a condition that affects less than one person in 2,000.

The numbers may differ around the world, but one thing is undeniable: There are unique challenges to researching and developing treatments for rare diseases.

The barrier between rare disease research and meaningful results

The Orphan Drug Act of 1983 was enacted to provide incentives to companies to develop rare disease therapeutics. While there’s been progress, there’s still a huge deficit in the numbers of treatments needed. After all, there are 7,000 distinct types of rare and genetic diseases that are affecting more than 400 million people around the world and about one in 10 people in the U.S.¹

The barriers between research and the results needed to develop more treatments are daunting. Among the challenges:

• Poor understanding of the natural history of rare diseases.²
• Incomplete knowledge about the pathophysiology and clinical manifestations of these diseases over time.²
• Unavailable efficacy endpoints for many rare diseases.²
• Under-represented genetic diseases and other types of rare diseases in health care coding systems.²

Rare disease questions, real-world answers

Researchers need to bolster the information that’s currently available about rare diseases and the people they impact. They need effective answers to drive research toward greater understanding and fill the data gaps.

De-identified, real-world data (RWD) is proving to be a reliable way to fill those gaps. It can produce a remarkably clear picture of a treatment’s effectiveness and a patient’s experience.

What is real-world data?

RWD is data that’s gathered in real-world settings, unlike data that’s generated in controlled settings in clinical trials. Data from electronic health records (EHR) and claims are the foundation of RWD because they provide insight into a patient’s record of health care utilization and the associated costs.

RWD includes data from:

• Clinical appointments
• Labs
• Diagnostics and assessments
• Diagnosis and treatment encounters
• Post-surgical care data

EHR shows promise for real-world data

There’s a significant opportunity in the rare disease space for researchers and product developers who use RWD. For one thing, collecting and curating data is far less costly and time-consuming than executing organic, randomized control trials. But RWD can be leveraged in many ways, especially using Optum EHR data to provide insights.

EHR data promises to yield research-ready data that may support study and approval of drugs to treat rare diseases. It has the breadth, depth and longevity needed for outcomes research, patient journey maps and clinical patient profiling for commercial teams.

Real world data using EHR brings developers closer to producing even more effective drugs for the rare disease marketplace and the people who need them.

Read the Optum whitepaper to learn more about Using real-world data to close the rare disease data gap.

¹ FDA. Rare diseases: Natural history studies for drug development, guidance for industry. fda .gov/ media/122425/download. March 2019.
² FDA. Rare diseases: Common issues in drug development, guidance for industry. fda.gov/media/119757/ download. January 2019.