IIOT

Enhancing Utilization Analytics portfolio with Reject Analysis Dashboards

Vivek Sundaram
Nov 18, 2019

We at Glassbeam always strive to expand our portfolio of supported products by listening to our customers and understanding their areas of acute pain. As we rolled out our utilization analytics solution in Clinsights™, we spoke to various radiology groups to understand what gaps still need to be addressed. One recurring issue that came up is the ability to understand the reject ratio for technologists, particularly in the Digital Radiography department.

Why Visiting Our Booth at AHRA 2019 is an Unmissable Opportunity to Recover Thousands of Dollars in Lost Revenues

Puneet Pandit
Jul 19, 2019

Are you attending the AHRA 2019 Event? If you are, you must plan on visiting our booth #205.  

This event is designed to keep up with the latest developments in the world of imaging, with a keen focus on using AI-driven analytics in the clinical engineering and radiology world.

Growing by Leaps and Bounds: Our Q2 2019 Recap

Puneet Pandit
Jul 17, 2019

We wrapped up Q2 and it has been full of changes.

Over the past 3 months, Glassbeam has:

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

Register Microservices to Consul Out-of-the-box Using Scala Macro Annotations

Shivam Kapoor
Mar 21, 2019

Off late at my work which currently involves moving our platform code from monolithic to microservices architecture, I have come to realize that there is a lot of boilerplate code that not only needs to be implemented in every other service but also involves maintenance pertaining to tribal knowledge within the team. This means that there isn't any standard way of implementing a service, raising the possibility of a lot of code duplication, thereby resulting in a maintenance nightmare.

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.

Semiotic Parsing Language (SPL) - Breakthrough DSL for IIoT Analytics - Part Two

Ashok Agarwal
Jun 13, 2018

In this section, we will investigate how Glassbeam’s DSL called SPL (Semiotic Parsing Language) helps in parsing multi-structured machine logs.

SPL Terminology:

Namespace:

SPL allows a log file to be treated as a hierarchical document consisting of multiple segments (or sections). Each hierarchical segment is called a namespace. This allows for zeroing in on the exact section to parse specific elements from, thus localizing the scope of extracts.

Semiotic Parsing Language (SPL) - Breakthrough DSL for IIoT Analytics - Part One of Two

Ashok Agarwal
May 30, 2018

Introduction

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.

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