Oh Medicare, you sure don’t like to make things easy for do you? Thanks to everything from payment cuts to slow-moving legislation, PTs have started venturing beyond the traditional reimbursement models and adding cash-based services to their repertoire.
In the spotlight as the 2020 presidential election cycle approaches, “Medicare for All” (M4A) is one of the most defining and contentious issues in politics. According to an August 2018 Reuters/Ipsos poll, 70% of people surveyed support M4A. Another survey indicates the underlying reason for Americans’ interest in healthcare reform: 77% are concerned that rising healthcare costs will cause significant and lasting damage to the U.S. economy, and 45% believe a major health event could leave them bankrupt, according to a 2019 Westhealth/Gallup survey.
Through its structure and scalability, the cloud makes data usable, even when there are volumes of it. A modern form of artificial intelligence (AI), exemplified by deep learning (DL) algorithms, crunches this data so people can make sense of it.
By processing data in the cloud, DL algorithms provide the information that people need to improve workflow and image quality and to optimize patient radiation dose, which directly impacts patient safety. Improvements can elevate the standard of care.
Sometimes, no matter how calm and collected a radiologist’s demeanor might be, a patient is going to get upset.
Various infractions can set off a healthcare consumer at any time. The authors of a new commentary published in the Journal of the American College of Radiology spelled out some of the common triggers for irate patients and some possible ways to address this behavior.
Ryan Lee, MD, section chief of neuroradiology at Philadelphia’s Einstein Healthcare Network, remembers the first time he heard about the role of a radiologist. The father of three says he first got interested in radiology during his college years when he was working with his family doctor who had an x-ray machine.
Kaiser Health News is reporting: “The use of Groupon and other pricing tools is symptomatic of a health care market where patients desperately want a deal — or at least tools that better nail down their costs before they get care.”
While most of the uses of Big Data have been coupled with AI/machine learning algorithms so companies can understand their customer's choices and improve their overall experience (think about recommendation engines, chatbots, navigation apps and digital assistants among others) there are uses that are truly industry transforming.
Many hospitals and health providers are failing to meet consumers’ needs and expectations in healthcare — and it’s a key reason why they’re losing market share to non-hospital competitors, including patient-empowerment company Devoted Health, retail guru Amazon, and industry behemoths United Health Group/Optum.
In recent years, technology companies, developers, investors, and others have turned their attention to Artificial Intelligence (“AI”). Although little consensus exists regarding its definition, AI solutions generally leverage powerful computing algorithms to analyze data and produce outputs that mimic human intelligence at greater speed and scale than humanly possible. The speed at which companies have been able to develop and implement AI is due, in part, to a lack of laws and regulations governing its development and use. Yet without much legal guidance, companies are assuming great risk by adopting AI at such a rapid clip.