Glassbeam, Inc., the premier machine data analytics company, announced recently that it has successfully built Artificial Intelligence (AI) applications powered by Machine Learning (ML) models for predicting part failures in expensive imaging modalities, allowing healthcare providers to deliver better and more efficient patient care. Business impact of such new applications delivered real time through cloud-based dashboards and rules-based alerts will revolutionize the landscape on how equipment maintenance is performed today by in-house support staff at healthcare providers, independent service organizations (ISOs), and by the OEMs themselves.
“The management of medical machines such as MRI and CT Scanners have taken on a new level of complexity in recent years, due in part to the increased sophistication of equipment and ever-increasing requirements for compliance, safety, reliability and accuracy,” said Corey Holtman, President at Gateway Diagnostic Imaging. “Predicting machine health and utilization patterns with help from latest techniques of Artificial Intelligence and Machine Learning is the next frontier to improve operations in Clinical Engineering function. I am pleased to see Glassbeam innovating on this exciting front for healthcare providers.”
The first phase of these applications will focus on CAT (Computed Tomography) Scanners that can cost anywhere between $1 million to $2.5 million or more, depending upon the desired image quality in procedures such as CT Angiography (CTA). One of the most expensive parts of a typical CAT Scanner is the X-ray tube provided by OEMs costing anywhere between $150,000 to $200,000. Unfortunately, replacing a CT scanner’s X-ray tube today is more an art than a science and is based on a number of ad hoc data inputs based on age of the machine, number of scans performed, image quality rendered amongst other subjective factors. Without proper diagnostics on machine data signals, many companies end up replacing tubes under the gun to ensure machine uptime at all costs. Glassbeam now has the solution allowing a facility to get a warning signal about a week in advance of a potential tube failure. This can alert the clinical engineering staff to become proactive in avoiding unplanned downtime, saving costs, and averting patient re-scheduling at the last minute.
“The number of signals coming from connected machines in the IoT market have surpassed the ability for humans to keep track of them years ago,” said Lise Getoor, Professor of Computer Science and Center Director of D3 (Data, Discovery and Decisions) initiative at University of California, Santa Cruz. “I am excited to see Glassbeam, as a supporting member of D3 Center, taking a leadership role in leveraging artificial intelligence to change the rules of the game for the healthcare market.”
“The parts replacement industry for global installed base of medical imaging equipment in 2020 is slated to be a $3.6 billion market,” said Puneet Pandit, Co-founder and CEO at Glassbeam. “With AI and ML applications based on analyzing millions of sensor readings captured in Glassbeam cloud each day, even with a modest 10% savings, we are ready to make a significant dent on the underlying inefficiencies of support operations, supply chain, parts and material logistics planning for large enterprises in the healthcare market.”