News

Winners of the 2018 IoT Evolution Product of the Year Awards Announced

TMC, a global, integrated media company helping clients build communities in print, in person and online, in conjunction with its partner Crossfire Media, today announced the winners of the 2018 IoT Product of the Year  Award, presented by IoT Evolution Magazine. The award honors the best, most innovative products and solutions powering the Internet of Things. Nominated solutions must have been available for deployment within the past twelve months as judged by the editors of TMC’s IoT Evolution World magazine. Read more about what IoTEvolutionWorld News has to say here.

Advanced Analytics Drive an $11.6 Billion Health Care Market Opportunity

It is not news that healthcare delivery organizations face enormous challenges. Principal among these is keeping up with advances in capital equipment such as MRI machines and CT scanners. Manufacturers have integrated ever-increasing IoT and Smart Systems technologies into these machines that enable functions such as automated diagnostics, remote patient management and performance monitoring. Medical professionals and IT teams may view the mountains of data these machines produce as overwhelming. However, with a new generation of analytics incorporating artificial intelligence (AI) and machine learning (ML), these data can become a critical solution for increasing machine uptime, improving patient care and even training of medical staff. Effectively managing complex machine data from healthcare imaging equipment will produce $11.6 billion of potential revenue value (increased uptime and reduced downtime) by 2022, notes Harbor Research. Read more about what Glen Allmendinger and Puneet Pandit have to say here. 

Building a Platform for Success: Puneet Pandit

In a recent industry report, Market Study researchers estimate the global market for artificial intelligence (AI) in healthcare will reach at least $10 billion by 2024. That number is based on projections of a whopping 40% compound annual growth rate, to be driven, experts say, by an anticipated surge in demand for cognitive computing, advanced machine learning, and predictive analytics. This is great news for a company like Glassbeam, a Santa Clara, Calif.-based developer of an AI/ML platform that employs advanced analytical frameworks to evaluate reams of “unstructured data,” such as physicians notes and radiology reports, to spot trends, good or bad, using predictive analytics. Read more about Puneet Pandit's thoughts on the evolution of Glassbeam and how machine data analytics can help a hospital improve the uptimes of its medical devices.

Reducing downtime using machine data analytics

Machine downtime can cause all kinds of problems for a facility. From scheduling messes, which lead to patient dissatisfaction, to the loss of income, to workflow disruption and more. It’s costly in time and money. That’s why we reached out to Glassbeam, a machine data analytics company that has introduced technology they’ve used in other industries into the healthcare market. Puneet Pandit, Glassbeam’s founder and CEO talked about some of the work being done with machine data analytics and the potential benefits for healthcare.  To begin, he explained that machine analytics in healthcare can be used for a wide range of equipment, from MR to ultrasound, to cath labs and beyond. “All these are heavily engineered machines running sophisticated software, generating logs all the time,” said Pandit. Read more on what Sean Ruck from DOTmed has to say here.

Reinventing the Healthcare Continuum with Machine Data Analytics

As healthcare delivery organizations navigate the dual challenges of providing high-quality medical care while facing tougher cost control measures from insurance carriers and government entities, one strategy they are turning to is optimization of both expensive equipment, as well as the entire ecosystem in which these assets operate. This is no small task, as capital expenditures for these machines totaled more than $350 billion in 2016. Innovators in many industries have adopted asset optimization strategies for several years. Now, leaders in healthcare equipment manufacturing are turning to this approach by implementing a new generation of data management and predictive analytics solutions to increase the quality, consistency and efficiency of medical equipment in support of patient care. To accelerate adoption of asset optimization solutions, manufacturers are turning to data management and predictive analytics solution providers. These players bring years of experience and best practices gleaned from deployments in multiple industries in addition to the use of artificial intelligence (AI) applications powered by machine learning to drive business impact. Read more on what Puneet Pandit has to say here.

Healthcare Purchasing News: July 2018 Product Picks

Glassbeam machine data analytics brings structure to complex data generated from any connected machine in the industrial IoT industry. When applied to complex healthcare products such as MRI machines, Glassbeam can increase uptime to more than 99 percent and save millions on maintenance costs. The patented technology can build analytics from Log data in a single step. The next generation cloud-based platform is designed to analyze multi-structured data, delivering solutions on customer support and product intelligence. Its predictive analytics can detect performance anomalies and alert engineers to take proactive steps to address and prevent failures.

Glassbeam unveils AI anomaly detection for imaging modality maintenance

Maintenance and repair for CT scanners may soon be more immediate, less frequent and more affordable following the upcoming expansion of Glassbeam Inc.’s anomaly detection technology.  The machine data analytics company elaborated on the development at the AAMI 2018 Conference and Expo in Long Beach, California, referring to it as a part of its approach for utilizing AI capabilities to detect and alert providers to changes in components of computed tomography scanners from tube temperature to waterflow. They plan to eventually include other critical imaging modalities such as MR.  Read more on what John R. Fischer from DOTmed has to say here.

Bringing New Revenue Opportunities to Healthcare Via Advanced AI/ML

The pressures facing healthcare delivery organizations are well documented: increased regulatory oversight, financial challenges, heightened competition, a shrinking talent pool, and many more. Also well known is the army of consultants, systems integrators and technology companies promising their solutions will address one or more of these market trends. All too often, implementation fails to deliver the promised results, leaving the healthcare organization with a big bill and little to show for it. What if there were new solutions, built on the powerful capabilities of artificial intelligence and machine learning (AI/ML) that provided proven, quantifiable cost savings of 30% or more, and consistent improvements in uptime for some of the most expensive equipment healthcare facilities operate? Read more on what Puneet Pandit and Frank Beltre have to say here.

Glassbeam inks new partnerships for its imaging equipment analytics solution

Uptime, service management, and utilization tracking for MR, CT and other diagnostic equipment are expected to improve at Scripps Health in San Diego following the deployment of Glassbeam Inc.'s proactive and predictive analytics solutions.  The implementation of the software products will be carried out by Radiographic Equipment Services (RES) which has agreed to become a strategic reseller for Glassbeam solutions in Southern California. The agreement is the second to be established this month by the machine data analytics firm, which has also signed on ISO Brown's Medical Imaging as a strategic partner and reseller. Read more on what John R. Fischer from DOTmed has to say here.

UCSF Health to apply predictive analytics to medical equipment maintenance

San Francisco-based UCSF Health tapped Glassbeam to manage predictive maintenance of its medical equipment, the data analytics company confirmed April 11. Glassbeam will apply its CLEAN, or "Clinical Engineering Analytics," blueprint to manage segments of UCSF Health's imaging, ultrasound, catheterization lab and physiological monitoring equipment. The CLEAN blueprint uses machine learning to manage equipment service operations. UCSF Health's goal for the clinical engineering analytics program is to improve the quality and reliability of its medical equipment. Read more on what Jessica Kim Cohen from Becker's Hospital Review has to say here.