Join us at HAS 17 – September 12-14, 2017 in Salt Lake City

Eric Just

Senior VP, Product Development, Health Catalyst

Eric joined Health Catalyst in 2011 from Northwestern University. At Northwestern, he led the research arm of a late binding data warehouse that served to integrate clinical, operational, financial, and research data. Prior to that, he led the development of a research-focused genome database. Upon joining Catalyst, Eric led some of our earliest clients in implementing the data warehouse and achieving outcomes improvement. Additionally, he provided important vision to our platform team as the first versions of Catalyst’s Source Mart Designer were being developed. Since then, Eric has enjoyed a variety of roles within the company including product development, business development, and client operations. He currently leads our Clinical Analytics and Decision Support product line which includes clinical analytics, closed loop analytics, data science, and research. Outside of work, he is a dedicated husband and dad involved in school, sports, and enjoying outdoor life in his adopted home town of Salt Lake City.

24 - Deploying Predictive Analytics: A Practitioner’s Guide (Technical Session)

Eric Just (Senior VP, Product Development, Health Catalyst)

Session Overview

This session will focus on the technical and practical aspects of creating and deploying predictive analytics. We have seen an emerging need for predictive analytics across clinical, operational, and financial domains. One pitfall we’ve seen with predictive analytics is that while many people with access to free tools can develop predictive models, many organizations fail to provide a sufficient infrastructure in which the models are deployed in a consistent, reliable way and truly embedded into the analytics environment. We will survey techniques that are used to get better predictions at scale. This session won’t be an intense mathematical treatment of the latest predictive algorithms, but will rather be a guide for organizations that want to embed predictive analytics into their technical and operational workflows.

Topics will include:

  • Reducing the time it takes to develop a model
  • Automating model training and retraining
  • Feature engineering
  • Deploying the model in the analytics environment
  • Deploying the model in the clinical environment