On-Demand Webinar

Using Analytics to Predict Wound Healing and Improve Clinical Outcomes

See how machine learning algorithms can empower clinicians to provide better care for their patients.

Hear from the experts on...

How machine learning algorithms can be used by clinicians in real-time to identify patients with wounds that are at risk of not healing or healing after an abnormal amount of time. These insights empower clinicians to provide better care for their patients.

In this webinar you'll learn how to:

  • Apply accurate predictions that forecast chronic wound healing time
  • Identify chronic wounds that are at risk of not healing
  • Improve decisions on the best clinical pathway to boost outcomes of care
  • Reduce costs associated with nonhealing or slow-healing wounds

 

Joshua Budman

VP, Analytics | Net Health

Joshua Budman is a Baltimore-based technologist specializing in machine learning, computer vision and the development of integrated healthcare applications. He holds a B.S. in biomedical engineering from John Hopkins University and received his MSE in biomedical engineering from the Johns Hopkins the Center for Bioengineering Innovation and Design. Josh currently serves as the VP of Analytics at Net Health. Prior to this Josh served as the CTO of the digital health startup, Tissue Analytics, where he helped integrate his company's AI-based digital imaging product with many of the major electronic medical record systems. Tissue Analytics was acquired by Net Health in the spring of 2020.

Matt Berezo

Data Science Manager | Net Health

Matt Berezo is a Pittsburgh-based technologist specializing in machine learning and development of integrated healthcare applications. He holds a B.A. in Public Policy from Duke University and a Master's degree form the Fuqua School of Business. Matt currently serves as a Data Scientist for Net Health, and is also an Adjunct Graduate-Level Professor in Data Mining for the University of Pittsburgh. Prior to joining Net Health and the University of Pittsburgh, Matt served as a Data Scientist for the University of Pittsburgh's Clinical Analytics Team for 2.5 years, focusing on improving patient outcomes with machine learning. Prior to UPMC, Matt spent 2 years as a Senior Predictive Analytics Consultant for IBM.

 

Leverage the vast and growing amount of data from over 1.1 million wounds, within electronic health record (EHR) learning models

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