RSNA16: Top 3 trends improving radiology workflow

At the 102nd RSNA annual meeting, conversations focused on emerging technologies supporting Imaging 3.0 and radiologists’ value to the healthcare enterprise in the community of care. From payer and reimbursement shifts to advances in machine learning and predictive analytics, we break down the top trends from the radiology industry’s biggest event.
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Surgeon and radiologist viewing digital brain scan in hospital

This week, an estimated 50,000 healthcare professionals gathered at RSNA16 to collaborate and explore the latest in radiology. The theme for this year’s annual meeting was “Beyond Imaging,” which was aptly chosen given the innovative trends and technology that were discussed at the show. The buzz at this year’s conference focused on the future of radiology. Here are the top three trends from the industry’s biggest event:

 

Payers and Reimbursement Issues

It’s clear that 2016 is the year for physician frustrations to come to a head. Imaging has been under scrutiny for some time because of rising healthcare costs, but radiologists are caught in the crosshairs with only limited control. They will not be reimbursed for duplicate or unnecessary imaging costs, but ordering physicians are the ones triggering the work. Organizational factors often make prior patient tests hard for physicians to find and access. Physicians struggle to do the right thing for patients while navigating payer requirements.

For example, pre-authorization for imaging is time-consuming and costly for providers. Today physicians and support staff spend 5- 8 hours a week on prior authorization processing activities. A recent survey found that 80% of physicians confirm that prior authorization requests demand extra work, rework, and follow-up. I heard one practicing physician at RSNA say it costs him 1 FTE per provider to keep up with pre-authorization requirements. So what can you do when the manual process lacks consistency and centralization and can cost anywhere from $35 to $100 per occurrence? We are seeing the emergence of technology to help. This includes clinical decision support that supports evidence-based care, and image awareness at the point of care or interpretation made possible through the cloud. Exam intelligence, powered by machine learning, can also help automate the pre-authorization process.

 

Machine learning in Radiology

Machine learning introduces numerous opportunities to relieve radiologists of routine tasks and allow them to focus their time on higher level decision making. There were several discussions at RSNA16 on machine learning and artificial intelligence from industry thought leaders, such as Dr. Keith Dreyer and Dr. Eric Topol. There were signs of this evolution in the exhibit hall, including examples of machine learning in the Nuance booth featuring Zebra Med and RadLogic, two partners that use image characterization with PowerScribe 360 to streamline and speed radiology reporting. Machine learning technology can automatically compile data from third-party resources such as EMRs, PACS, and historical reports and use predictive analytics to create richer, more actionable reports for radiologists. For example, instead of manually combing through platforms to gather the appropriate information, radiologists can leverage the legwork already compiled from a “virtual resident,” to save up to 8 minutes per case, correlating clinical information quickly to arm the radiologists for fast, consistent decision making.

 

Voice Interface

Everywhere you looked at RSNA16 people were talking to devices from asking a smartphone for directions to searching the internet to hands-free commands of a virtual reality prototype. With the proliferation of the Internet of Things (IoT) across the globe and the maturity of cloud-based speech recognition technology, people expect to converse with devices naturally and be understood. We’re seeing this enter the next frontier in radiology as radiologists now see ubiquitous speech recognition as an answer for hands-free communication anywhere, anytime to help them read more than 100 images each day while delivering quality care.

These are just a few of the many things I noticed, but there was also 3-D printing and live models getting scanned in big diagnostic machines (which took some getting used to). Reflecting on this year’s meeting, I’m excited about the future of radiology and proud to be working for a company that is at the forefront of advancing many innovative trends in diagnostic imaging.

If you missed us at RSNA16, you can schedule a demo or learn more about Nuance’s suite of diagnostic solutions here.

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Karen Holzberger

About Karen Holzberger

Karen Holzberger is the vice president and general manager of Nuance’s Healthcare’s diagnostic solutions business. Karen joined Nuance in 2014 with more than 15 years of experience in the Healthcare industry. Prior to Nuance, she was the vice president and general manager of Global Radiology Workflow at GE Healthcare where she managed service, implementation, product management and development for mission critical healthcare IT software. Karen attended Stevens Institute of Technology where she earned a B.S in Mechanical Engineering.