Eric is a Senior Vice President for Product Development at Health Catalyst. His team is building the next generation of Health Catalyst products that leverage the awesome power of data to aid in clinical decision making. His team is responsible for a broad portfolio of applications including a patient registry platform, patient safety decision support, and new technologies like machine learning and natural language processing.
Eric has spent the majority of his career innovating and implementing technology to improve healthcare and health sciences. During the early part of his career, he built a genomics data resource to support a global research community at Northwestern University Feinberg School of Medicine. Then Eric transitioned to the clinical data warehouse team at Northwestern as one of the principal architects. His role then expanded to managing the research arm of the data warehouse. In this position, he ensured the data warehouse was effectively leveraged to power outcomes research, care improvement, and recruitment of patients into research studies.
Since joining Health Catalyst in 2011, Eric has enjoyed a variety of roles within Health Catalyst’s departments, including product development, business development, and client operations. Outside of work, he is a dedicated husband and dad and is involved in school, sports, and enjoying outdoor life in his adopted hometown of Salt Lake City.
12 – Machine Learning for Leaders: A Practical Guide to Implementing Machine Learning in Your Organization
Machine learning (ML) has the power to transform care delivery and help achieve the holy grail of providing better care at lower costs. ML algorithms uncover patterns in data that identify patient-specific risk factors that can be proactively managed to keep patients from developing costly and harmful conditions. It also has a litany of uses in driving more efficient healthcare operations. Despite these benefits, we hear of organizations that are so daunted or confused by this technology that they delay or neglect bringing this technology into their organizations. This presentation will bring a practical understanding of ML, which is a game-changer, and show both non-technical and technical leaders:
- How to disambiguate the buzzwords associated with ML: predictive analytics, artificial intelligence (AI), and deep learning.
- Use cases and benefits of real-world examples associated with these technologies.
- Personnel and technology requirements for using ML (hint: it may be less than you think!).
- How to break out of the “black box” and provide transparency to promote understanding and adoption, rather than fear and loathing.
- Questions to ask when purchasing ML models or technology.