Artificial Intelligence and Deep Learning
More than ever before, the healthcare industry is forced to maintain spartan budgets — even as an unprecedented pandemic puts stress on all aspects of the industry. In a HIMSS20 digital presentation, Mark Morsch, vice president of technology at Optum360, cited data indicating a huge hole in efficiency — as much as $200 billion in administrative waste due to inefficient revenue cycle practices.
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It’s a safe bet to say that making a … safe bet during an unprecedented pandemic is not the easiest thing in the world. All the same, Becker’s Health IT got 20 healthcare executives and leaders to commit to posterity one health IT industry prediction for the next five years, and the answers are interesting,…
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Predicting an economic recovery in today’s COVID-19 environment is far more complex than reading traditional individual economic indicators. As seen by the recent run in the stock market, there are certain market components somewhat disassociated from traditional indicators, i.e. unemployment rate, GDP growth, etc.
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In a little over a month, OrboGraph will host the ORBOIMPACT Virtual Conference: AI Innovation for Check Payments, Check Fraud, and Healthcare Remittance Automation.
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In a little over a month, OrboGraph will be hosting it’s first virtual conference since 2014: ORBOIMPACT Virtual Conference: AI Innovation for Check Payments, Check Fraud, and Healthcare Remittance Automation.
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Revenue Cycle of the Future…something we have heard on several occasions. But what does it actually look like? In a recent article, HealthLeaders highlights a roundtable discussion sponsored by our friends at Waystar, bringing together a group of leaders in revenue cycle management to gather their thoughts on what they believe is important for the revenue cycle of the future.
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The healthcare industry is fraught with terminology that probably makes your head spin. Not only do you have to deal with the number of acronyms popping up each day, but for revenue cycle management, you also have to learn new terminology when it comes to technology. One of the questions we field on a regular basis: What is the difference between digitization and electronification?
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Alpha Health recently released results of a national survey designed to examine and assess the impact of COVID-19 on revenue cycle operations at health systems and hospitals across the U.S. The survey was fielded between May 19, 2020 and June 22, 2020 among 587 chief financial officers and revenue cycle leaders at hospitals and health systems across the United States. The survey has a confidence level of 95 percent with a margin of error of plus or minus 5 percent.
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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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