Levi Thatcher

VP, Data Science

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.

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Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it Effectively in Their Organizations

Machine learning (ML) is gaining in popularity throughout healthcare. ML’s far-reaching benefits, from automating routine clinical tasks to providing visibility into which appointments are likely to no-show, make it a must-have in an industry that’s hyper focused on improving patient and operational outcomes.

This executive report—co-written by Microsoft Worldwide Health and Health Catalyst—is a basic guide to training machine learning algorithms and applying machine learning models to clinical and operational use case. This report shares practical, proven techniques healthcare organizations can use to improve their performance on a range of issues.

How Healthcare Text Analytics and Machine Learning Work Together to Improve Patient Outcomes

Healthcare organizations that leverage both text analytics and machine learning are better positioned to improve patient outcomes.

Used in tandem, text analytics and machine learning can significantly improve the accuracy of risk scores, used widely in healthcare to help clinicians identify patients at high risk for certain conditions and, therefore, intervene.

Health systems can run machine learning models with input from text analytics to provide tailored risk predictions on both unstructured and structured data. The result? More accurate risk scores and the ability to identify every patient’s level of risk in time to inform decisions about their care.

The Dangers of Commoditized Machine Learning in Healthcare: 5 Key Differentiators that Lead to Success

Many vendors deliver machine learning models with different applications in healthcare. But they don’t all deliver accurate models that are easy to implement, targeted to a specific use case, connected to actionable interventions, and surrounded by a machine learning community and support team with extensive, exclusive healthcare experience.

These machine learning qualities are possible only through a machine learning model delivered by a vendor with a unique set of capabilities. There are five differentiators behind effective machine learning models and vendors:

1. Vendor’s expertise and exclusive focus on healthcare.
2. Machine learning model’s access to extensive data sources.
3. Machine learning model’s ease of implementation.
4. Machine learning model’s interpretability and buy-in.
5. Machine learning model’s conformance with privacy standards.

These five factors separate the high-value vendors and models from the crowd, so healthcare systems can quickly implement machine learning and start seeing improvement results.

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