Artificial intelligence (AI) and machine learning (ML) have gained an extraordinary amount of attention over the last year. Many organizations have been perplexed, wondering where and how to best apply AI in a complex, adaptive system like healthcare, and what it takes to implement AI/ML solutions.
There are numerous healthcare applications where ML insights and the data driven-automation of ML can help optimize workflows and decision-making, efficiently allocating resources. Workflow integration is made more meaningful by additional tools in healthcare.ai. For example, these tools provide insights into identifying the individuals whose health will be improved and the next best interventions to improve patient outcomes and minimize financial and operational risks.
Levi Thatcher, PhD, VP of Data Science at Health Catalyst will share practical AI use cases and distill the lessons into a framework you can use when evaluating AI healthcare projects. Specifically, Levi will answer these questions:
We hope you will join us.
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, creating software improvements to the company’s core ETL offering. Since he moved 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.