Levi Thatcher - Keynote Speaker

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Synopsis

Practical lessons in healthcare artificial intelligence

Artificial intelligence (AI) and machine learning are popular buzzwords, but what can they actually do improve the cost and quality of patient care? Drawing on lessons from his work in machine learning across US health systems, Levi will discuss lessons learned; the best place for quick efficiency gains; and how machine learning will eventually lead to optimized treatments, population health, and effective personal responsibility.

Technical Q&A with Levi Thatcher: A deeper dive into Artificial Intelligence and Machine Learning

What’s the difference between AI and machine learning
How can AI/ML improve healthcare in a practical way
What tools are needed to get started
How does one build a machine learning model
What data is needed for AI/ML
How to get clinicians excited about decision support

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Biography

Levi Thatcher
VP, Data Science
Health Catalyst

Levi did his graduate work at the University of Utah, focusing on atmospheric predictability. There he used ensemble methods to improve numerical models, in terms of both the lead time and estimated intensity of hurricane development.

At Health Catalyst, Levi started out on the platform engineering team, writing software for the company’s core ETL offering. After moving internally to lead the data science team, Levi founded healthcare.ai, the first open-source machine learning project focused on healthcare outcomes.

He’s now working to integrate healthcare.ai into each of Health Catalyst’s products and make healthcare.ai the international center of collaboration for healthcare machine learning.