MACHINE LEARNING & AI: A HIMSS EVENT

LAS VEGAS, NV - MARCH 5, 2018

HIMSS18 Annual Conference
Wynn Las Vegas
Mar. 5, 2018

Lynda Chin

Associate Vice Chancellor for Health Transformation and Chief Innovation Officer for Health Affairs
University of Texas System’s Institute for Health Transformation

Renowned physician and researcher Lynda Chin, MD, is The University of Texas System’s associate vice chancellor for health transformation and chief innovation officer for health affairs. Chin leads the UT System’s Institute for Health Transformation, which leverages, develops and deploys innovative, technology-enabled solutions to improve access and affordability of quality health care in Texas and beyond.

The Institute for Health Transformation is initially focused on Project DOC – Diabetes Obesity Control – an initiative to improve diabetes care and management in South Texas.

Prior to joining the UT System in 2015, Chin was the founding chair of Genomic Medicine and scientific director of the Institute for Applied Cancer Science at UT MD Anderson Cancer Center. Chin has made multiple scientific discoveries spanning the fields of transcription, telomere biology, and mouse models of human cancer and cancer genomics. She has won numerous distinguished honors for her contributions, including election to the prestigious Institute of Medicine (IOM) of the National Academies in 2012.

March 5, 2018
8:35am - 9:05am
Lafleur

One of the biggest mistakes healthcare stakeholders make is believing that technology, include machine learning and AI, solves problems. It does not. Technology is a tool, part of the solution. And the real challenge is implementing that tool into workflows – existing or new – in a way that makes sense, and most importantly, drives the desired results.

In this opening keynote, renowned physician and scientist Lynda Chin takes a hard look at what must happen for machine learning and AI to move beyond hype and deliver on its promise to transform healthcare.

At the end of the day, it boils down to:

  • Trust & transparency: Has the algorithm withstood peer review. Is the source code trustworthy, and does it perform as well or better than a human?
  • Quality data: Without good data, machine learning is worthless.
  • Leadership: Machine learning requires domain expertise, not technical.
  • Training: How do you assure the algorithm is being used as intended?

These days, it seems, everyone wants to jump on the machine learning bandwagon. But real success, real change, as Chin will explain, depends on healthcare laying the proper foundation.

March 5, 2018
12:40pm - 1:00pm
Lafleur

One of the best ways learn is to network with your peers. This session will provide an opportunity to meet speakers and attendees who have similar privacy and security challenges and discuss solutions to those challenges.

Here's how it works:

Speakers will be stationed at different tables in the ballroom, and attendees can circulate and speak one-on-one or in groups with individual speakers. The speakers have been assigned different topics, but other topics can also be addressed.

Mingle, share and learn in this interactive environment.

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