Learn more about Levi Thatcher

Author Bio

Levi Thatcher

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.

Read articles by Levi Thatcher


Eric Just
Levi Thatcher
Tom Lawry

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.

Mike Dow
Levi Thatcher

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.

Levi Thatcher

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:

Vendor’s expertise and exclusive focus on healthcare.
Machine learning model’s access to extensive data sources.
Machine learning model’s ease of implementation.
Machine learning model’s interpretability and buy-in.
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.

Levi Thatcher

How Machine Learning in Healthcare Saves Lives

There are limitless opportunities for machine learning in healthcare. Defined as, “a field of computer science that uses pattern recognition to identify historical relationships in large data sets using an algorithm to create a generalized model of behavior,” machine learning is one of the most important life-saving technologies ever introduced to healthcare for several reasons:

Reduces readmissions.
Prevents hospital acquired infections (HAIs).
Reduces hospital length of stay (LOS).
Predicts chronic disease.
Reduces one-year mortality.
Predicts propensity-to-pay.
Predicts no-shows.
Improves sepsis outcomes.

It’s no wonder health systems are eager to start leveraging machine learning to save lives, improve outcomes, and make systemwide enhancements.
They can do so by understanding machine learning basics, the importance of customized machine learning (not one-size-fits-all models) and historical data (a requirement for answering basic machine learning questions), and how machine learning helps their patients, clinical teams, and bottom lines.

Levi Thatcher

How Healthcare AI Makes Machine Learning Accessible to Everyone in Healthcare

Before the introduction of healthcare.ai, an open source, healthcare-specific machine learning software, only a small subset of healthcare staff (primarily data scientists) had the ability to leverage predictive analytics to improve outcomes.
Healthcare.ai will democratize machine learning by empowering everyone in healthcare with the appropriate technical skills (BI developers, project managers, data architects, etc.) to download the healthcare.ai tools (packages for R and Python), request features, ask questions, and contribute code.
What sets healthcare.ai apart from other machine learning tools is its healthcare-specific functionality:

Pays attention to longitudinal questions.
Offers an easy way to do risk-adjusted comparisons.
Provides easy connections and deployment to databases.

Healthcare.ai will do more than just democratize machine learning—it will transform healthcare.