Jordan Everson | June 14, 2023
In a recent study in the Journal of the American Medical Informatics Association (JAMIA), we leveraged data from the 2020 American Hospital Association (AHA) Information Technology Supplement gathered from April-June 2021, shortly after the initial applicability date of the information blocking regulations (April 5, 2021). We found that 42% of hospitals perceived that at least one type of information blocking “actor” (health care provider, health information network/health information exchange, or health IT developer of certified health IT) engaged in practices that may constitute information blocking.
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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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Robert Anthony | February 10, 2023
ONC is delighted to report that on December 31, 2022, the health IT community passed a major milestone on the road to improving health IT interoperability. More than 95 percent of Certified Health IT developers met the compliance deadline to update and provide their customers with new technology including requirements to enable access to information through application programming interfaces (APIs) “without special effort.”
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Steven Posnack | December 15, 2022
Don’t get me wrong, the information blocking regulations are important, but let’s not forget that the 21st Century Cures Act (Cures Act) and our implementing regulations (Cures Act Final Rule) had a few other impactful provisions. In particular, certain changes to the ONC Health IT Certification Program may seem like “more of the same” for health IT developers, but in reality they are really important and beneficial to clinicians, researchers, and the public alike.
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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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