OrboNation Blog
A common misconception, especially in revenue cycle management (RCM), is that automating processes by integrating technologies like Artificial Intelligence and Machine Learning technologies will replace employees and eliminate jobs. This perception can be traced all the way back to when machines replaced labor in many manufacturing jobs like the automotive industry and is continually being fueled by the media with the story lines such as AI-powered trucks will replace truck drivers.
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By now, many of you have heard of the arguments between utilize GPU vs CPU. towardsdatascience.com provides a simple explanation on the reasoning behind the need for GPUs for machine learning:
GPUs are optimized for training artificial intelligence and deep learning models as they can process multiple computations simultaneously.
They have a large number of cores, which allows for better computation of multiple parallel processes. Additionally, computations in deep learning need to handle huge amounts of data — this makes a GPU’s memory bandwidth most suitable.
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Technology just keeps getting better and better, right? Not always. A recent survey of health IT leaders reveals that they are most often unhappy with their electronic health record (EHR) packages. Healthcare IT Today reports on a survey 108 healthcare leaders about their use of analytics platforms. It starts out with some fairly positive data:
90% of these organizations reported using the analytics in their EHRs.
Check writers and depositors have become accustomed to a next day or two day check clearing process for the majority of items. But when an extended clearing process does happen, i.e. 2-3 days, it becomes a real inconvenience, especially for millennials accustomed to instant digital payments, or for those folks who still get payroll checks. There are many other examples.
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The New Yorker Magazine takes a close look (via Clifford Marks, an emergency-medicine resident at the Icahn School of Medicine at Mount Sinai) at how primary care practices and physicians are taking it on the chin due to the effects of the Coronavirus pandemic.
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Pymnts.com explores the various hurdles banks face when taking that big — and increasingly necessary — step. They note that migrating to the cloud is a vital tool traditional financial institutions (FIs’) will need in order to compete with digital-native FinTechs.
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To the layman, much of the language in the AI space can be mystifying, particularly in deep learning. Take for example one of the core elements; the node. A deep learning node is “a computational unit that has one or more weighted input connections, a transfer function that combines the inputs in some way, and an output connection. Nodes are then organized into layers to comprise a network.”
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A new podcast at PYMNTS.com features a conversation with Jennifer Barker, Managing Director and Head of Wholesale Payments Product Delivery at JPMorgan Chase & Co., on the topic of business resiliency in this unprecedented pandemic environment.
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Ensenta/EPS has put together an excellent report exploring NINE DRIVERS OF MOBILE DEPOSIT GROWTH (You can download a copy HERE). They come out of the gate by noting that large financial institutions continue to invest in increasingly better mobile deposit user experiences for two reasons: Improving return on investment (ROI) Growing their deposit base Given…
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Modern bank consumers are more and more comfortable interacting with their accounts online, often from a portable device. It’s never been easier to make deposits — using an app on an always-handy phone — and see the “funds available” right in an account, reassuring the user that the money is indeed deposited and… available. MyBankTracker…
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