Siemens and SAS: Improving Patient Care with AI and Machine Learning

If you go to the doctor or
visit the hospital for treatment, you’ve generally got one goal
in mind: To get healthy again. Patients expect to be treated with
modern medical technologies and equipment. These days, patients are no longer
fearful of the digitization of healthcare. Providers must instead focus
on meeting patient expectations with respect to service and
diagnostic capabilities. Siemens Healthineers provides
modern diagnostic technologies that enable medical providers to
offer patients the best possible care. Let’s talk about Siemens
Healthineers first: The term consists of our company
name and the word “Healthineers”, which is a combination of the terms
“healthcare”, “engineer” and “pioneer”. And it’s our mission to pioneer
the healthcare industry. As Healthineers, we design our products to
provide a maximum of uptime and performance. This way, our customers can run their
operations and workflows smoothly and concentrate
on patient care. As one of the world’s leading
medical technology companies, Siemens Healthineers is helping
to shape a global market in which the Internet of Things, Machine Learning, and
Artificial Intelligence are already prerequisites. One of the things which excites me most
about modern developments in analytics is that it keeps getting easier to access
data, analytical tools and more advanced methodologies. When I look at new technologies like the
Internet of Things or Machine Learning, I’m really excited to apply these
capabilities to the healthcare industry. Global healthcare systems are struggling to
provide better clinical outcomes and lower costs. And the productivity
gains of Machine Learning are a great way to provide better healthcare to patients all around the globe. The main goals of predictive
maintenance are to provide our customers with a maximum of uptime and minimal
interruption of their workflows due to maintenance, especially
unplanned maintenance. For our customers, this would mean that
they would have to reschedule patients, because the computer tomography system
is not available, for example. Or they would have to delay
analyses of certain blood tests when a laboratory diagnostic
solution is not available. We produce a broad variety of products like
CT and MR scanners as well as lab instruments and there is a lot of physics and domain
know-how attached to these solutions. This means that our data scientists need to
interact very closely with the business areas to create new
predictive patterns. So, a close collaboration in an
agile environment is very important. In order to serve the users and
developers of analytical patterns, we need a platform which allows us to apply analytics
in an industrial style and in a deployable manner. So, on the one hand, we need something
which scales globally in a technical sense, but we also need a partner organization which allows
us to grow with our demands and offers global support. And this was one of the main reasons why
we chose to work with SAS in this field.

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