The Rise of Machine Learning
Algorithms in Healthcare
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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.