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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Yuriy Pylypchuk | March 9, 2023
In 2020, as the U.S. health care system faced the unprecedented challenge of combating the COVID-19 pandemic, telemedicine emerged as an invaluable care delivery tool; both providers and patients heavily relied on it for providing and receiving care. Through an analysis of National Electronic Health Record Survey (NEHRS) 2021 data and from in-house data collected from electronic health record (EHR) developers’ app galleries, we have an opportunity to inform the public about physicians’ experiences with telemedicine during the pandemic.
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Chelsea Richwine | February 6, 2023
Chelsea Richwine | January 19, 2023
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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