Kathryn Marchesini | April 13, 2023
This is part five of a blog series on predictive models, artificial intelligence (AI) & machine learning (ML) in health. We encourage readers to (re)visit the four previous blog posts for important context to what follows.
Through a series of blog posts over the last year, we’ve described our understanding of the current and potential uses of predictive models and machine learning algorithms in health care, and the role that ONC can play in shaping their development and use.
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Kathryn Marchesini | December 13, 2022
In the third blog in our series on artificial intelligence (AI) and machine learning (ML)-driven predictive models (data analytics tool or software) in health care, we discussed some potential risks (sometimes referred to as model harms) related to these emerging technologies and how these risks could lead to adverse impacts or negative outcomes. Given these potential risks, some have questioned whether they can trust the use of these technologies in health care.
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LaVerne Perlie | November 15, 2022
Hot off the presses, the Pediatric Health Information Technology: Neonatal Abstinence Syndrome Informational Resource (NAS IR) [PDF – 808 KB] is a new resource from ONC to support pediatric care and practice settings specific to neonatal abstinence syndrome. The NAS IR builds upon prior efforts included in the ONC Pediatric Health Information Technology Informational Resources (IR) for health IT developers and for health care providers, and includes information about the implementation of health IT and its use as part of delivering health care to infants experiencing withdrawal after maternal exposure to opioids and other substances during pregnancy.
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Ali Massihi | October 18, 2022
With a heightened focus on health equity throughout our work, ONC has adopted the concept of “health equity by design.” Along those lines, health IT can, and should, be used to better identify and mitigate disparities while enhancing opportunities for underrepresented populations. In 2019, under the Leading Edge Acceleration Projects (LEAP) for Health IT program, ONC funded the University of Texas at Austin’s Dell Medical School (Dell Med) to design,
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Kathryn Marchesini | September 7, 2022
When talking about artificial intelligence (AI) today, people are usually referring to predictive models—often driven by machine learning (ML) techniques—that “learn” from historic data and make predictions, recommendations, or classifications (outputs) which inform or drive decision making. The power of ML is in its enormous flexibility. You can build a model to predict or recommend just about anything, and we have seen it transform many sectors.
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