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MedInformatix Summit 2014
2014-07-22 - 2014-07-25    
All Day
MedInformatix is excited to present this year’s meeting! 07/22 Tuesday Focus: Product Development Highlights:Latest Updates in Product Development, Interactive Roundtables, and More. 07/23 Wednesday Focus: Healthcare Trends [...]
MMGMA 2014 Summer Conference
2014-07-23 - 2014-07-25    
All Day
Mark your calendar for Wednesday - Friday, July 23-25, and join your colleagues and business partners in Duluth for our MMGMA Summer Conference: Delivering Superior [...]
This is it: The Last Chance for EHR Stimulus Funds! Webinar
2014-07-31    
10:00 am - 11:00 am
Contact: Robert Moberg ChiroTouch 9265 Sky Park Court Suite 200 San Diego, CA 92123 Phone: 619-528-0040 ChiroTouch to Host This is it: The Last Chance [...]
RCM Best Practices
2014-07-31    
2:00 pm - 3:00 pm
In today’s cost-conscious healthcare environment every dollar counts. Yet, inefficient billing processes are costing practices up to 15% of their revenue annually. The areas of [...]
Events on 2014-07-22
MedInformatix Summit 2014
22 Jul 14
New Orleans
Events on 2014-07-23
MMGMA 2014 Summer Conference
23 Jul 14
Duluth
Events on 2014-07-31
Articles

Microsoft, 16 health systems shape AI

Sixteen health systems, in collaboration with Microsoft and other healthcare technology organizations, have established the Trustworthy & Responsible AI Network (TRAIN). This initiative focuses on defining best practices and standards for AI in healthcare to improve the quality and trustworthiness of AI applications. TRAIN aims to ensure responsible development and evaluation standards for technologies like patient screening and administrative task automation. Major health systems including Mercy, Mass General Brigham, Providence, Cleveland Clinic, and AdventHealth are part of TRAIN, along with partners OCHIN and TruBridge. The goal is to make TRAIN’s benefits accessible to organizations of all sizes. Founding members of TRAIN were also part of the Coalition for Health AI (CHAI), and TRAIN will build upon CHAI’s work to operationalize principles for trustworthy healthcare AI.