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The Cancer Letter – To expedite progress against COVID-19, a public-private “accelerator” taps into real-world data

The Cancer Letter – To expedite progress against COVID-19, a public-private “accelerator” taps into real-world data

Real-world data is everywhere. During the COVID-19 global pandemic, we are literally generating, and collecting, real-world data every single day—from electronic health records, insurance claims, patient registries, and a myriad of other sources. But the question remains: how do we use this data to better understand, prevent, and treat this disease?


Data by itself does not tell us much.  Real-world data comes from many different places in so many different formats that it can be like looking at a computerized image blown up so that all you see is pixels. But, if you scale that image and examine it from a different angle, a full picture begins to emerge. Real-world data needs that same refinement to illustrate the full picture of clinical and patient experience, creating real-world evidence that informs clinical practice and disease management.


The Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, has launched the COVID-19 Evidence Accelerator specifically to move real-world data into real-world evidence. Working closely with FDA, the Evidence Accelerator brings together top experts in healthcare data and analytics to share insights, compare results, and answer key questions to inform the nation’s COVID-19 response.


Through the Therapeutics Evidence Accelerator, which launched in May, and the Diagnostics Evidence Accelerator announced last week, the project supports the research community in not only addressing specific questions about preventing, testing and treating COVID-19, but also in understanding the natural history of the disease.


Participants include experts from FDA, major data organizations, patient advocacy, healthcare and hospital systems, clinical laboratories, insurers, academic research institutions as well as drug and device manufacturers and technology companies. The Evidence Accelerator is comprised of three main workstreams:

  • Therapeutics Evidence Accelerator Collaborative provides a venue for scientists across the country to discuss data generated from quick turnaround queries and share results with peers and experts from FDA, major data organizations, academic research institutions, professional societies, and health systems to help accelerate, and potentially confirm, findings from different data sources and leverage existing expertise.
  • Therapeutics Evidence Accelerator Parallel Analysis Workgroup invites multiple teams to simultaneously address key research questions developed with FDA. The Workgroup pools participant expertise to determine how data elements are being extracted and how they are being defined to be operationalized. Initial activities include (1) rapidly revising a list of core data elements; (2) identifying those elements critical to answering the primary question; and (3) establishing uniform collection parameters. Repeating analyses in parallel through collaborators using different analytical techniques and data sources helps strengthen findings and learnings.
  • Diagnostics Evidence Accelerator, a hybrid of the Therapeutics Collaborative and Parallel Analysis approaches, addresses the diagnostic and serological study space. The goal is to present and discuss information on recent analyses of real-world data related to diagnostic test performance, contemporaneous symptoms and presentation, surveillance trends and immunity.


Just as the best art often takes a combination of creativity and discipline, so does crafting real-world evidence now and in the future. The Evidence Accelerator is building a platform to answer current questions around COVID-19 that can also be used post-pandemic.


The public-private partnerships being built and the methodologies being employed help structure how real-world research activities could be conducted in the future, thus validating the role of real-world data as a tool for rapidly learning about patient characteristics, treatment patterns, and outcomes.


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