Sharing Our Expertise and Connecting With Our Customers at RSNA 2019

Puneet Pandit
Thursday, December 19, 2019

For Glassbeam, the RSNA Annual Meeting is the top conference on our field calendar that gets us the most excited. Once again, this year’s show, held during Dec 1-5 at McCormick Place in Chicago, lived up to that expectation. The showcase of innovations from technology companies, in-depth demos at the booths, an entire floor for Artificial Intelligence vendors, and a range of radiology topics focused on expert learning sessions, and so much more made RSNA Chicago a great way to end our roadshows for the year 2019.

This blog captures the thought-provoking bits from #RSNA2019 and our modest contributions.

Looking back, the 5-day event was staggering (over 50 thousand delegates) and it was the best place to hear from other visionary leaders in the radiology space. We were glad to make the most out of this year’s edition too; meeting old friends and making new ones, forging new alliances, exploring new opportunities, exchanging hundreds of ideas, engaging with our superstar partners, and showing off our most popular AI-powered asset utilization and service analytics solution – ClinsightsTM.

Related Reading: Download the Glassbeam for Healthcare guide to know about how our solutions optimize healthcare equipment performance and revenue.

If you missed us at RSNA, I’d like to share the most requested collateral at the booth to help you get started on our solution: IoT Blueprint for the Healthcare Industry.

RSNA Preview: Glassbeam Launches Ground Breaking Functionalities in Clinsights Service Analytics Application for the Healthcare Provider Market

On the eve of RSNA’s grand opening, we released a press statement on the advances we made with the Clinsights application. We announced the expansion of our Service Analytics platform that can now enable remote login, access, and management of several modalities such as MRs and CTs manufactured by GE and Siemens.  In addition, we announced much-awaited proactive and predictive support for Cathlab modalities.  For more details, here is the press release.

Related Reading: Blog:  Onsite to Online — Save Operational Costs and Improve Patient Care

Four Key Takeaways from RSNA 2019, Chicago:

#1 Re-imagining Ways of Working Together

President for RSNA, Valerie P. Jackson, MD said: “Value-based care, team-based delivery models and the rise of artificial intelligence each add, in their own ways, to a new rationale for radiologists to seek opportunities to reach out more directly to patients.” At the center of this call for change is data. Data surrounds the interactivity with patients, physicians, radiologists, and machines. This idea of team-based delivery resonates with Glassbeam. We believe that solutions that address the practical and technical challenges of interactivity between humans and machines, whether it is harnessing the power of PACS with log data to increase machine utilization or avoiding patient waiting times by analyzing HL7 data, AI will be that validating factor in knowing if a provider ecosystem has what it takes to deliver quality patient care.

#2 Reinvigorated Focus on Adoption of AI for Radiology

There is a lot going on in optimizing the workflow in the radiology departments and solutions are maturing in this space. In one such talk delivered by Dr. Steenburg, he said “If we aren't tracking what we do and how well we do it, we don't know what needs to be fixed," "Be your own case study.”  There are thousands of data points being tracked today in the radiology department today from the quality of scans to environment factors such as humidity levels in the machine room, helium levels that cool MRI machines, to radiologists’ schedules in RIS systems, to tracking change over time between exams and turn around time (TAT) for reading physicians, etc. Radiology heads are turning to solutions for mining all this disparate data to optimize their group’s performance, across different scanners, protocols, and patient demographics.  It was a great validation to note that Glassbeam core IP, as a data platform company, is well on its way to address several use cases in these areas.

To learn about our solutions for imaging equipment, download the Glassbeam for Imaging guide.

#3 Outbreak of New AI-enabled Imaging Modalities

All three top OEMs, Siemens, Philips Healthcare, GE Healthcare made announcements in bringing the latest advancements in diagnostic radiography. Revolution™ Maxima with AI-Based Auto Positioning was announced by GE, Siemens unveiled its new mobile head CT scanner, Somatom On.site, while Philips showcased CT 6000 iCT and the CT 5000 Ingenuity at RSNA. The focus of all three announcements was the Artificial Intelligence tech embedded in them to analyze data in real-time and make life easier for Radiologists. Whether it is AI-Rad Companion for Somatom or My Exam for the MRI machines, AI is at the front of analyzing exams with over 200,000 scan parameters. This could elevate the levels of quality at radiology departments’ manifold.  Can we extend the reach of AI from patient imaging data to machine data, and suggest recommendations to improve utilization of machines, technicians, and Radiologists?  Absolutely yes. That is what Glassbeam's solution is all about.

Here’s where you can start to know more about our Utilization Analytics offering.

#4 A Continuous Care Environment for Machines in Radiology

“The day we turned on AI, it found a bleed in the brain that the radiologist missed. It was small and subtle but it completely changed our interpretation of the study. That quickly spread throughout the department and now radiologists are looking for that result before they take a case.” A parallel world exists in the imaging maintenance space for high-end assets like MRI and CT Scanners. An X-ray tube ultimately fails due to an increasing number of neglected Arcs and Aborts occurring during regular Exams over several days (or weeks).  This may be happening due to a burnt-out pin in a high voltage tank, which if detected early can save expensive tube replacement and avoidable downtime.  All in all, AI/ML applications on machine performance can push hundreds of hours of unplanned downtime to planned maintenance windows.  For a multi-vendor multi-modality fleet, this single pane of glass view helps healthcare organizations improve overall productivity, reduce cost, and better leverage their capital investment.  

Visualize the Better, Faster, More Efficient Radiology Department

As I close my thoughts on our experiences at RSNA 2019, I would like to reinforce the idea of Artificial Intelligence being a pervasive technology for radiology professionals, from the perspective of the patient and machine care. It is here to stay and to help you unlock the hidden value in your machine data to increase the utilization of medical equipment through evidence-based proactive action.

Accelerated by machine data analytics, AI can speed up the acquisition and analysis of data to quickly spot critical issues in your imaging machines' performance. It can also provide support personnel insights that are too time-consuming to acquire using traditional troubleshooting methods. With AI, hospital providers get to boost the goal of providing the much-promised quality patient care that was the most discussed topic at RSNA2019 Chicago. More exciting times ahead, stay tuned to our blog updates.

See you at the next year’s edition of RSNA.

If you would like to get a quick demo of Glassbeam Clinsights, schedule a convenient time here