MACHINE LEARNING

Onsite to Online — Save Operational Costs and Improve Patient Care

Vivek Sundaram
Nov 04, 2019

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?

Real Life Business Impact of AI/ML on GE CT Scanners Using Glassbeam Clinsights™

Vivek Sundaram
Sep 25, 2019

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:

Who Owns the Data – Part 3

Puneet Pandit
Jul 05, 2019

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. 

5 Big Reasons to Switch to Artificial Intelligence-enabled Predictive Maintenance

Puneet Pandit
May 31, 2019

Next week, Glassbeam is gearing up to participate at AMMI Exchange 2019 conference in Columbus, Ohio. The event promises fantastic insights into the concerns and questions of Radiology and Clinical Engineering professionals and some great line up of talks from all walks of Healthcare Technology Management (HTM).

Who Owns the Data – Part 2

Puneet Pandit
May 16, 2019

In Part 1 of this blog series, I set the stage to understand who owns the machine data generated by medical devices such as CT, MRI, and so on. We also discussed how the restrictions on device data evolved over time and the implications on healthcare providers’ maintenance programs.

Expanding Customer Base and Thought Leadership Conversations That Make Us Proud of Q1 FY2019

Puneet Pandit
Apr 16, 2019

Welcome to the first newsletter of 2019! As always, we present some of the key milestones we have achieved last quarter. This quarterly recap highlights the ways we are bringing all our business functions to make a positive impact on our customers and our partner ecosystem.

Growth Momentum in Our Customer Base Continues

2018 Recap and Looking Forward to Growth in 2019

Puneet Pandit
Jan 30, 2019

2018 was a defining year for Glassbeam. We blazed the trail through the year signing up 15 customers and pilot sites located at Scripps Health, Grossmont Imaging, Eisenhower Health, and several more. As we head into the second month of 2019, we just announced a key partnership that is all set to take the VA Healthcare market by storm.

The Impact of Machine Data Analytics, Artificial Intelligence and Machine Learning on Healthcare Technology

Puneet Pandit
Sep 05, 2018

On the heels of a great event and presentation along with Rick Gaylord, our healthcare solution specialist, at the 2018 CEAI Conference, I want to continue the conversation about the far-reaching impacts of machine data and artificial intelligence for healthcare technology.

Data Doesn’t Lie: 5 Ways Hospitals Can Use Machine Log Data

Pawan Jheeta
Jul 04, 2018

Maximizing uptime of diagnostic equipment is vital to both patients and healthcare organizations. As medical imaging equipment becomes more sophisticated and the need for healthcare organizations to improve their availability becomes more acute, so does the value of machine log data and advanced analytics. Here, we’ve listed several important ways that hospitals can use machine log data and predictive/prescriptive analytics to optimize operational efficiency and revenues.

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