Blogs

Recently I have covered several business topics for our largest market segment - healthcare providers and independent imaging centers - on how Glassbeam can help them improve patient care by improving machine uptime and utilization (by analyzing machine logs and related data such as DICOM, HL7, and so on).

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.

We at Glassbeam always strive to expand our portfolio of supported products by listening to our customers and understanding their areas of acute pain. As we rolled out our utilization analytics solution in Clinsights™, we spoke to various radiology groups to understand what gaps still need to be addressed. One recurring issue that came up is the ability to understand the reject ratio for technologists, particularly in the Digital Radiography department.

Our mission at Glassbeam is to equip our customers with the ability to predict equipment failures. Prevention is better than cure after all. And there’s that added incentive of saving dollars by proactive maintenance rather than adhoc, reactive ways.

The ground reality, however, is that not all incidents can be prevented. So what does a field service engineer do if they need to immediately react to a high priority incident?

We are now in an era where machine learning isn't just hype. In fact, it is absolutely real in its business impact as Glassbeam has recently demonstrated for its growing network of connected medical imaging equipment.

With Glassbeam's extensive experience in data engineering and analytics related to GE CT Scanner machine logs, we now have dozens of powerful use cases where automated anomaly detection via machine learning has been used to detect potentially expensive part failures well before the end-user even noticed an issue.  Here are 3 real-world examples:

Having met several C-suite executives in recent customer meetings, it has become increasingly clear to me the immense value and ROI we bring to the table for healthcare providers using our AI/ML analytics platform. 

Taking a few cues from our recently published e-Book for Healthcare Provider market, here is an example of how this successful discussion goes:

Are you attending the AHRA 2019 Event? If you are, you must plan on visiting our booth #205.  

This event is designed to keep up with the latest developments in the world of imaging, with a keen focus on using AI-driven analytics in the clinical engineering and radiology world.

We wrapped up Q2 and it has been full of changes.

Over the past 3 months, Glassbeam has:

In Part 1 of this blog series, I set the stage on the debate on who owns the machine data generated by medical devices such as CT, MRI, and so on. In Part 2 of this blog, I outlined an approach and my perspective on how this debate is being resolved between Providers and OEMs. 

For the first time in the Healthcare industry, two distinctly different groups in a healthcare provider organization come under the eye of a single pane of glass. The roles are Clinical Engineering practitioners responsible for machine uptime and other is the Radiology and Imaging groups responsible for maximizing machine utilization and therefore revenues.  The common goal is always improving patient care and clinical outcomes for the benefits of its customers.