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The N3C, meanwhile, is overseen by thousands of researchers from hundreds of participating organizations and holds them accountable for transparency and reproducibility. Everything they do within the user interface Palantir-en GovCloud the platform is carefully preserved so that anyone with access can retrace their steps.
“This is not rocket science, and it’s not really new. It’s just hard work. It’s tiring, it has to be done carefully and we have to validate all the steps,” says Christopher Chute, a medical professor at Johns Hopkins and also head of N3C. “The worst thing we could do is turn it into junk that would give the data the wrong way.”
Gross strength
Handel noted that these efforts have not been easy. “The diversity of specialization, perseverance, dedication and, in fact, the gross strength that was required to make that happen is unprecedented,” he says.
This raw force has come from many areas, many of which have traditionally not been part of medical research.
“Having everyone on board from all walks of life really helped. In the meantime, there were a lot more people collaborating, ”says Mary Boland, a computer science professor at the University of Pennsylvania. “You can have engineers, computer scientists, physicists, all those people who don’t normally get involved in public health research.”
Boland is a member of a group that uses N3C data to find out whether cobido increases irregular bleeding among women with polycystic ovary syndrome. Out of the blue, most researchers need to use insurance claims data to get enough databases for population-level analyzes, he says.
Claims data, for example, can answer some questions about how drugs work in the real world. But these databases lack a large amount of information, including lab results, what symptoms people report, and even when patients die.
Collection and cleaning
Outside of the insurance claims database, most U.S. health care partnerships use a federated model. All those involved in these studies agree to format their data sets in a common format and then make inquiries about the collective, such as the proportion of serious cases by age group. Several international secret collective research, among them Observation Health Data Science and Computer Science (OHDSI, pronounced “Odyssey”) works this way, avoiding legal and political issues with cross-border patient data.
Founded in 2014, OHDSI has researchers from 30 countries with records of 600 million patients.
“This allows each organization to store its data behind its own firewalls, protecting its data in its own right. It doesn’t need patient data to move back and forth,” says Boland. “That’s reassuring in many places, especially with all the hacking that’s been going on lately.”
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