MACHINE LEARNING & AI FOR HEALTHCARE: A HIMSS EVENT

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

Michael Schwarz

Executive Director - IS
Indiana University Health

As executive director of information services at Indiana University Health, Michael Schwarz is responsible for developing and leading the decision support and analytics organization. His teams engage with senior leaders, executives and clinicians, driving information delivery, self-service enablement, data governance, and the development of visual analysis solutions.

Prior to IU Health, Michael served as vice president – IS at Walker Information, a customer service experience consulting firm. At Walker, he was responsible for understanding client and market demands to drive information systems tool development and delivery.

Michael is currently involved with Connect2Help as immediate past board chair. Connect2Help is a statewide information and referral organization for social services. Prior roles at Connect2Help include technology committee, disaster response committee, finance committee and board chair.

Michael holds a BS in Information Systems, as well as an MBA.

June 13, 2019
9:40am - 10:20am
America Ballroom

This session kicks of a two-day journey for attendees from analytic basics to advanced uses of machine learning and AI, with multiple stops for lessons learned and best practices along the way.

Artificial intelligence and machine learning gather a lot of attention these days, and with good reason. Improved diagnosis, processing radiology images, precision medicine, and workflow efficiencies to reduce clinician burnout are just some of the areas either being impacted by AI/ML or will be in the near future. But as our speakers will discuss, a healthcare organization will not derive value from AI and ML unless it has achieved a level of capability with managing data and analytics.

In this session, attendees will learn how to set up analytics to prepare for AI and ML, how to embed AI in analytics, and better grasp that no matter how much ML and AI can help us predict the future, it can’t be done without first looking in the rearview mirror.

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