MACHINE LEARNING

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.

Machine Learning Based Anomaly Detection: Driving Proactive Machine Maintenance

Mohammed Guller
Jun 20, 2018

Medical imaging or diagnostic equipment such as Computed Tomography (CT), Ultrasound, and Magnetic Resonance Imaging (MRI) devices play a critical role in modern healthcare. But while these devices enable healthcare providers to better diagnosis their patients' and provide an optimal treatment plan, they are also very expensive to maintain.

New FDA Report Highlights the Importance of Servicing Medical Devices

Puneet Pandit
Jun 05, 2018

This month, the FDA issued a report that focused on the quality, safety, and effectiveness of servicing of medical devices.

If It’s Not Broken You Can Still Fix It: ISOs See Value of Predictive Analytics

David Sawatzke
May 24, 2018

Capital expenditures for healthcare equipment totaled more than $350 billion in 2016, according to Harbor Research. Healthcare organizations and Independent Service Organizations (ISOs) are now turning to AI and machine learning to predict and prevent equipment failures and reduce operational costs.

Machine Learning and Predictive Maintenance Maximizes Healthcare ROI

David Sawatzke
May 08, 2018

Predictive analytics can be used to reclaim millions of dollars in operational costs for healthcare organizations.

As pressure mounts to lower healthcare costs, healthcare delivery organizations are taking a closer look at costs in all aspects of their business, particularly operations. More organizations are realizing there is a huge opportunity to lower operational costs by leveraging machine data and machine learning.

Integrating Machine Log Data with CMMS Solutions. An Exciting Opportunity for Glassbeam!

Pawan Jheeta
Apr 12, 2018

Glassbeam loves its customers. Customer development is an integral part of Glassbeam. Our healthcare/clinical customers are telling us how the newly launched Glassbeam CLinical Engineering ANalytics (CLEAN) Blueprint is creating immense value by analyzing machine log data and presenting deep insights about machines in their clinical environment.

Glassbeam to Demo Its Rx for Healthcare Equipment and Systems at MD Expo Next Week in Nashville

David Sawatzke
Mar 30, 2018

We are exhibiting at MD Expo April 4-6, a leading high tech medicine (HTM) trade show and conference. It will be held at the Renaissance Nashville Hotel, 611 Commerce Street. We hope you will stop and visit us at booth #119.

Glassbeam for Glassbeam –Dogfooding and Loving it!

Pramod Sridharamurthy
Jul 10, 2017

At Glassbeam, we have always believed in eating our own dog food and why not! We have the same use cases that our customers use for our platform. But, before I go deep into the internal use cases that we use Glassbeam for, let me explain how we collect our infrastructure logs and the types of logs we collect.

Medical Device Usability is a Dire Need, Not a Nice-to-Have Option

Vijay Vasudevan
Jul 10, 2017

The connected medical equipment is here and the possibilities of a fresher, richer future are staggering. Imagine CAT scanners talking back to technicians, initiating reports on its profitability, or does a self-diagnosis and tells product management they ought to replace it. These possibilities are not too far into the future but we’re almost there.

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