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Ep. 89: Geisinger Health's Elizabeth Clements on Managing Machine Learning

Published on 8/21/2018

When the term “machine learning” was originally coined in the late 1950s, the field was little more than a computer science theory being applied to rudimentary data processors that filled entire rooms. Today, machine learning is one of the most exciting and innovative areas of health care, with the potential to make sweeping improvements to clinical decision support, imaging analytics, precision medicine and more.

While the health care industry has a great deal of work ahead in designing and implementing applications for machine learning, it is easy to forget that many organizations have already implemented advanced analytics tools that are making a difference in hospitals, labs and clinics.

In this episode of The Cerner Podcast, we’re joined by Elizabeth Clements, a business architect at Geisinger Health, a Pennsylvania-based health care system widely recognized for its innovative use of the electronic health record and the development of innovative care delivery models.

  • In the health care industry, many consider machine learning to still be in its infancy. Let’s set the stage a little bit: Can you describe how we have gotten to where we are today in machine learning? 
  • Machine learning and artificial intelligence already have some real-world applications in health care. Can you discuss where machine learning is making an impact today and provide a framework for how you have seen it add value to care? 
  • For hospital and health care leadership, incorporating machine learning into the organization might seem like a challenging task. In your view, what are some of the steps leadership should take to integrate machine learning in the clinical setting?
  • When properly implemented, machine learning has operational and clinical benefits. Where have you seen the greatest success with machine learning at Geisinger Health, and what would you consider the greatest opportunity for its application in the future?
  • Finally, let’s talk about the broader implications of machine learning. How do you see this area evolving over the next 3-5 years in terms of innovative use cases?

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