Artificial Intelligence and Machine Learning Made Simpler

From rumors of radiologists being replaced to Twitter bots going rogue, artificial intelligence (AI) and machine learning (ML) have become buzzwords we’ve all heard about. There has been much discussion on the breakthroughs both could bring to how fields traditionally function, like patient diagnosis in health care. Many have also warned of their potential and history of bias and abuse. Given the gravity surrounding AI and ML, and with much help from an excellent Ars Technica piece by Haomiao Huang, we’ve created a guide geared towards non-technical individuals to help them learn more about the basics of these technologies.

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Gaining Consumer Trust in a Digital World

Out with the fax machine, in with the smartphone. In early February, the Centers for Medicare and Medicaid Services (CMS) and the Office of the National Coordinator for Health Information Technology (ONC) released two proposed rules regarding interoperability. Much of the rules’ buzz centers on the standardization of APIs to make sharing patient data more useful and transferable. With Apple’s Health Records already having met these standards, one can imagine the number of apps and businesses eager to take advantage of the gap in interoperability and optimize personal health information (PHI).  

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How America is Leveraging Social Determinants of Health

Although Social Determinants of Health (SDoH) has become a buzzword in recent policy conversations, it is still a powerful tool to analyze the social, economic, and environmental factors that influence consumer activity and overall health. If used properly, SDoH have the potential to improve health outcomes and reduce health care utilization and spending.

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