Most would consider analytics a science. The Glassbeam team considers analytics an art of combining impermeable truth from machine logs with deep healthcare domain expertise. As we expand our penetration of the healthcare market after spending years in the data center world, where the gold standard for machine uptime was 99.999 percent, we have recognized a huge opportunity since the acceptable machine uptime for medical equipment ranged from 90 to 97 percent.
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.
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.
In this section we will investigate how Glassbeam’s DSL called SPL (Semiotic Parsing Language) helps in parsing multi-structured machine logs.
SPL allows a log file to be treated as a hierarchical document consisting of multiple segments (or sections). Each hierarchical segment is called namespace. This allows for zeroing in on the exact section to parse specific elements from, thus localizing the scope of extracts.
The Association for the Advancement of Medical Instrumentation (AAMI) Conference and Expo is a big stage for conversations about innovation, advancements and security for medical equipment. Our team recently attended the 2018 expo in Long Beach, CA, and we were thrilled to mingle with like-minded industry professionals who share our interests in the advancement of medical instrumentation.
Glassbeam’s business revolves around providing business intelligence on machine data. Intelligence comes from structured data. Machine data is not always structured. So, there is a gap between what is needed and what is produced. As Glassbeam’s head of engineering, I am going to write a two series blog about how Glassbeam bridges this gap.
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.
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.