Even if you're not a big e-sports fan, you'll likely be playing more video games yourself next year - you just might not realize it.
Mergers and acquisitions are continuing at an even faster clip in 2017 as healthcare providers seek to lower costs through economies of scale. Along with a 15 percent increase in activity, the number of transactions among organizations with $1 billion or more in revenue is also growing.
Keeping up with the vast store of new medical knowledge is a huge challenge for today’s busy physicians. When they can make time for continuing medical education (CME), it must be as effective as possible.
Source: “Selection and Pilot Implementation of a Mobile Image Viewer: A Case Study” (JMIR Mhealth Uhealth, May 2015)
The Centers for Medicare and Medicaid Services’ (CMS) goal, announced in 2015, to shift 50 percent of payments to value-based reimbursement in 2018 is looming on the horizon. To reduce unnecessary service and resource use that is central to value-based care, providers need integrated, 360-degree view of patient data, including images.
I’m on a train leaving FMX – a film, animation and special effects conference in Stuttgart, Germany. I have no experience or expertise in film, animation or special effects, so why did I cross an ocean for it?
Will radiologists soon become extinct because of AI and machine learning? Or will AI and machine learning enhance imaging for radiologists and patients, with better diagnosis decisions and improved predictions for patient prognoses?
The potential of artificial intelligence (AI) to transform population health management, patient diagnosis, clinical decision support and precision medicine has captured the attention of healthcare. About 35 percent of healthcare organizations plan to adopt AI systems within two years and 50 percent say they will do so within five years, according to a HIMSS Analytics survey.
New HIMSS Study Shows Other Providers Catching On To Benefits of Mobile Use in Clinical Settings
Since 2013, Mayo Clinic has provided its radiologists with the ability to view images from mobile devices using ResolutionMD® running on tablets and smartphones.
The urgency behind healthcare reform and the increasing capabilities of analytic algorithms are converging, creating a swell of activity in healthcare AI. The critical component driving machine learning and enabling the delivery of reliable, computer-aided decisions is the massive and ever-increasing amount of patient data. Using pattern finding and neural networks, machine learning holds the promise of uncovering value in that data for better patient care.
The role of patient imaging is shifting and changing in virtually every area of healthcare. At a very basic level, the sheer amount of patient data is growing and expanding across specialties. The way that providers access, share and analyze this data is also transforming with healthcare’s broad adoption of non-imaging technologies such as cloud computing, mobile devices and machine learning, as well as increased integration of patient image data with electronic health records. At this crossroads of change stand many benefits for hospitals and health systems, including support for value-based, patient-centered care and provider collaboration both of which have the power to reduce costs and improve outcomes.