What is the NC3 and the technology that powers it?

What is N3C and what is the technology behind it?

Dr. Kenneth Gersing, chief information officer for NCATS, says the organization gets groups to work together and turns data silos into a network through shared services. To make a program like N3C possible, it is important for NCATS to find economies of scale.

The organization also leads the Rare Diseases Clinical Research Network, a precursor to N3C, and was able to use some of the same technology and processes around data cleaning, analysis, and output to support the N3C initiative. In 2017, NCATS began testing instances of Palantir in the cloud using Amazon Web Services to create a secure analytics environment. The organization implemented Google Workspace in 2019 to enable the research community to easily share findings.

The organization also harmonizes all COVID-19 data collected from health care institutions, which use a variety of common data models. NCATS provides all the tools researchers need to access and analyze data while ensuring site security and patient privacy protection.

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Gersing explains that NCATS handles identity authentication, cloud deployment, security, software-as-a-service support, single sign-on, ticketing, and compliance concerns so researchers can focus on the science. .

N3C’s goal is to share information with the community. If an investigator brings an algorithm to run against the enclave, it becomes part of the assets available to the community. The algorithm would have to be evaluated for safety reasons before approved use.

How do researchers access N3C data?

The N3C enclave includes data from at least 5.9 million positive patients for COVID-19, plus data from two controls for every positive patient. New data is collected, harmonized with the Observational Medical Outcomes Partnership common data model, and published weekly.

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“We have almost 4,000 volunteers. There is no way this is possible without a community coming together and helping,” says Gersing.

Some of the collected patient data dates back to January 1, 2018, giving researchers a more complete picture of patient journeys.

N3C uses a centralized model instead of a federated one. In federated models, researchers can ask a question such as “How many of the patients over the age of 60 have hypertension?” They would receive a number but would not have access to row level data. Using a centralized model removes that limitation.

“We wanted researchers to be able to access the data directly and iterate on it,” says Gersing. “In particular, we wanted to be able to use technologies like machine learning, which is difficult to do in a federated model.”

N3C also ensures that definitions are consistent, which is important in harmonizing data between models. Different organizations must agree on the definition of a visit, for example, explains Gersing.

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