Events Calendar

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CHIME College of Healthcare Information Management Executives
2014-10-28 - 2014-10-31    
All Day
The Premier Event for Healthcare CIOs Hotel Accomodations JW Marriott San Antonio Hill Country 23808 Resort Parkway San Antonio, Texas 78761 Telephone: 210-276-2500 Guest Fax: [...]
The Myth of the Paperless EMR
2014-10-29    
2:00 pm - 3:00 pm
Is Paper Eluding Your Current Technologies; The Myth of the Paperless EMR Please join Intellect Resources as we present Is Paper Eluding Your Current Technologies; The Myth [...]
The New York eHealth Collaborative Digital Health Conference
2014-11-17    
All Day
 Showcasing Innovation Join a dynamic community of innovators and thought leaders who are shaping the future of healthcare through technology. The New York eHealth Collaborative [...]
Big Data Healthcare Analytics Forum
2014-11-20    
All Day
The Big Data & Healthcare Analytics Forum Cuts Through the Hype When it comes to big data, the healthcare industry is flooded with hype and [...]
Events on 2014-10-28
Events on 2014-10-29
Events on 2014-11-17
Events on 2014-11-20
Articles News

Hospitals are now aware of the development process for several health AI technologies. Will anything change as a result?

EMR Industry

A new federal regulation mandates that certain health AI makers reveal information about bias, testing, and other topics.

They know what the ones and zeroes buzzing away in the background are up to, don’t they? Clinicians click away at workstations in hospitals.

In actuality, physicians and health systems frequently lack critical knowledge about the algorithms they use for tasks like anticipating the start of serious illnesses. Federal regulators now mandate that electronic health record (EHR) businesses provide clients with a wide range of information regarding artificial intelligence tools in their software, which proponents say is a positive start.

Clinicians should have been able to see a model card, often known as a “nutrition label,” since the beginning of January. This label should include information on the variables that go into a prediction, whether a tool has been evaluated in the real world, how the tool’s authors addressed potential bias, cautions about improper use, and more.