Data integration is a top priority for the National Institutes of Health’s modernization efforts, with an emphasis on interoperability.
Susan Gregurick, the associate director for Data Science and Director of the Office of Data Science Strategy at the NIH, discussed her office’s initiatives on creating a solid infrastructure to share public health data.
“We need to think more in terms of data as an infrastructure and ways that we can provide the agnostic functionality and tools to mainly improve interoperability of data and those products,”
she said during a virtual conference.
Gregurick discussed the various collaborations and advanced in data science and technology prompted by COVID-19, and how the latest NIH initiatives have run on extracting and scaling data to share between offices and sites, including information on medical histories, demographics, COVID-19 cases and immunization records.
She added that the very large volumes of data and data sets make scalability between offices a critical part of business operations within the NIH.
“The overall goal is to provide a modernized, integrated biomedical data ecosystem, and that sounds super easy, but actually, it's really not,”
Gregurick said.
“It's quite challenging because of the diversity of science across NIH and the diversity of needs and capabilities. An overall one size fits all strategy is very, very challenging.”
Other public officials spoke to public health agencies’ prioritization of interoperability, particularly in regards to the electronic health care records deployment.
Mary Greene, the director of Office of Burden Reduction & Health Informatics at the Centers for Medicare and Medicaid Services, said that automation is her agency’s plan to help seamlessly shuffle data from one user interface to another.
“The smarter EHRs are, the more tools that they have within the EHR, to pre-populate whatever that data needs to be sent––whether that data is in the clinical systems or the administrative systems of that particular provider,”
Greene said.
“That is a huge, huge part of the potential reduction in burden for clinicians.”
Gregurick noted that the NIH has spent “an enormous amount of time and energy” creating datasets that are more ready to be integrated into systems bolstered by artificial intelligence. She added that scalability is still challenging, partially due to the current data governance in place regulating sharing and aggregation.
“It's more of a challenge to the policy side,”
she said.
“Policies are just not set up for this way of thinking about data and infrastructure and data aggregation, so we're working and much more work will need to be done to update in and think about data policies and governance in a world where data is the key factor.”
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