MACHINE LEARNING & AI FOR HEALTHCARE: A HIMSS EVENT

Boston, MA
Westin Copley Place
June 13-14, 2019

Cheong Ang

AI Workflow Lead
University of California, San Francisco

Cheong Ang has been a hands-on leader in web, data, and healthcare IT for two decades. His work on web interactivity at UCSF Medical Center resulted in patented technologies licensed to leading firms, including Microsoft, Oracle, and Adobe. At IBM, he led teams in developing software for predictive analytics in e-commerce and knowledge management, and implementing data projects for clients in healthcare and financial industries. 

He has been in director positions in charge of operating a targeted advertising system, and building and marketing a broadband video delivery system. He has also been engaged for business and innovation strategy consulting at Konica Minolta Business Innovation Center, and San Francisco General Hospital.  Most recently, he brought together a team to bridge the gap between AI and the frontline workers with a SaaS system that enables smart collaboration and continuous improvements toward organizational goals. The system is currently serving tens of thousands of patients at multiple healthcare organizations.

June 14, 2019
9:35am - 10:15am
America Ballroom

Improving the joy of practice for clinicians is a top priority for all health systems, and machine learning and AI promise to do that, while also personalizing care and improving outcomes. All well and good, but implementing these technologies into clinician workflows is easier said than done.

In this session, speakers identify the considerations and challenges to implementing AI and ML into clinical decision-making. How do we balance the need to provide enough information to point-of-care decision making without overwhelming the physician with too much information to review? How do we measure the validity of algorithms and their impact on decision-making? How do we train physicians to understand how to leverage AI to enable decision-making?

Speakers will address these and other questions and, importantly, they'll make clear that to optimize success, AI and machine learning must add value to clinical workflows and reduce the time physicians spend searching for relevant information to a patient’s care.

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