Applying AI in Healthcare: Opportunities & Emerging Applications

Applying AI in healthcare:  Challenges, opportunities, and emerging applications 

On Demand Webinar

Hosted by:  Health Data Management 



Advances in Artificial Intelligence (AI) are redefining healthcare and enabling improvements in healthcare quality, cost reduction, and population health management.


However, many problems remain in these early days of AI implementation, slowing progress and frustrating efforts to fully realize AI’s enormous potential.


Implementation challenges slowing AI advancement include many problems familiar to HIT professionals:  inaccurate, incomplete, inconsistent, and messy data; use of non-standard local terminologies; use of specialized terminologies without adequate mapping to industry standards; difficulties with context, disambiguation, and negation.


Learn how to use AI in your system to solve common healthcare IT challenges in the areas of quality measure reporting, predictive analytics, and clinical decision support.

New Improvements in Healthcare Quality, Cost Reduction & Management

In this session you will learn about: 

  • Common challenges in applying AI to healthcare IT.
  • How AI-powered software solutions, including natural language processing (NLP) and machine learning (ML), are being used in healthcare.
  • Emerging use cases for AI in healthcare that address quality measure reporting, predictive analytics, and clinical decision support.

Watch to learn how you can take advantage of AI today, and how to prepare for the potential of AI implementation in healthcare IT.



Chris Funk, Ph.D – Sr. Medical Informaticist
Wolters Kluwer Health – Health Language


Chris Funk graduated from the University of Colorado with a Ph.D in Computational Biology. His dissertation focused on natural language processing (NLP) and identification of concepts from standard terminologies within unstructured text. Chris provides support for Health Language healthcare content analysts by using his expertise in NLP and machine learning to automate workflows for more efficient content updates, as well as meeting other customer terminology and unstructured text processing needs.


Krishna Srihasam – Sr. Data Scientist
Wolters Kluwer Health

As a senior data scientist at Wolters Kluwer Health, Krishna Srihasam has been applying Machine Learning (ML) and Artificial Intelligence (AI) techniques to health and patient data for more than three years. Krishna holds a Ph.D in Computational Neuroscience and has published several articles on applying ML techniques to neuroscience research.


Sarah Bryan – Director, Product Management

Wolters Kluwer Health – Health Language

As Director of Product Management at Health Language, Sarah Bryan is passionate about bringing terminology management software solutions to the healthcare industry to enable accurate analytics and interoperability. With a degree in Economics from the University of Colorado and a master’s degree in Health and Medical Informatics (in process), Sarah applies her education, extensive work with customers, and software development background to build solutions that streamline and accelerate workflows related to terminology management.

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