Machine Learning

Machine Learning is Transforming Healthcare

Issue link: https://read.uberflip.com/i/1203165

Contents of this Issue

Navigation

Page 2 of 5

The Rise of Machine Learning Algorithms in Healthcare 3 Machine Learning Algorithms Hamilton says now that machine learning has proven to be a viable resource for healthcare providers, the next step is scaling the creation of intelligence and its integration back into the workflow at the point of decision-making. Cerner itself is building complex analytical tools that draw on the volumes of secure, anonymized patient data it already has access to: medical diagnosis and treatment outcomes, financial outcomes from claims and coding, billing tools, predictive hospital staffing models, and more. Take, for example, an ER like the one the embolism patient visited. Facilities across the U.S. struggle with staffing challenges. With one of its machine learning algorithms, Cerner can draw on historical data to predict patient volumes and staff the ER accordingly, days in advance. This proactive algorithm helps ensure that doctors and nurses aren't stretched thin during their shifts, and that patients are seen more quickly and receive quality care. Building Blocks Cerner hopes to leverage rapidly advancing machine learning through Amazon SageMaker to explore additional applications, using its anonymized, HIPAA- compliant records. "AWS is giving us access to tools and techniques, whether they're basic building blocks or complex ecosystems, like SageMaker. Historically, that would have been things we had to invest in and invent on our own," Hamilton says. Still, Hamilton says, Cerner won't be able to build all the algorithms the market needs. The company already works with partners to build machine learning models within the Cerner ecosystem. To truly see impact at scale, however, he sees a need for a broader collaboration.

Articles in this issue

Links on this page

view archives of Machine Learning - Machine Learning is Transforming Healthcare