CUSTOMER INTELLIGENCE

As Glassbeam expands its sales into the healthcare provider market, we have found an unmet need for Radiology groups to automate their asset utilization reporting.  A key business driver is their quest to enhance patient care by reducing patient revisits and reschedules, understanding physician referral leakage and maximizing machine utilization.

TDWI Business Intelligence Volume 23 Number 2 carries my article on the antidote to improving a Healthcare provider’s performance. I have based the article on Harbor Research’s Machine Data Analytics Drives Innovation in Healthcare Market research report.

On the occasion of Healthcare Technology Management Association (HTMA)’s Annual General meeting, Glassbeam is excited to share our thoughts on the challenges Imaging technologists have in extracting actionable insights from Machine Log data.

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.

In my concluding post, let’s look at the use cases that help our Sales/Account teams to manage our customer relationships efficiently, discover upsell and cross sell opportunities and analyse application use across our install base to help improve the adoption of our product. 

In the second post of this series, I have listed the high level use cases of Glassbeam for Glassbeam across our internal teams: Technical Support, Sales, Engineering, and Product Management. In the next 4 posts, I will dive deep into the use cases for each of the above teams and talk about the value Glassbeam for Glassbeam as a data-driven decision making solution, brings to each team.

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.

The need for proper tool to organize and understand the underlying patterns in machine data exists. From what we’ve been hearing, that’s gripping the Chief Data Officer’s mind. Why?