Health Systems Put Machine Learning to Work to Increase Efficiency and Improve Outcomes
// By Lisa D. Ellis //
At Mount Sinai’s Institute for Next Generation Healthcare in New York City, artificial intelligence or AI (also referred to as machine learning) is helping researchers to gain new insight into genetic predispositions for cancer and other diseases.
Close to 2,000 miles across the country, Intermountain Healthcare in Salt Lake City, Utah, is using AI to understand and measure clinical variation, which can lead to more efficient and cost-effective hospital care.
And Novo Nordisk, a global healthcare company, relies on AI to better understand the course of diabetes and to develop more personalized treatment plans.
Putting Machine Learning to Work
What all three of these organizations have in common is that they are working with Ayasdi, Inc., a machine intelligence software company based in Menlo Park, California, that provides tools that help organizations better manage and understand their own data and apply it for the good of their operations and the good of the people they serve.
In layman’s terms, the concept of machine learning refers to creating computer programs that can change or respond to new data and can identify patterns in complex and multi-dimensional sets of information. This premise holds tremendous value for businesses of all types, and particularly for the healthcare sector. That’s why over the past few years, there has been a growing experience within many health systems on applying AI to their operations.
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