Glassbeam Analytics Changes the Game for Clinical Engineering and Imaging Equipment Service Teams

Rick Gaylord
Friday, March 16, 2018

In my role as the Healthcare Solutions Specialist for Glassbeam I've had the opportunity to meet with a wide range of Glassbeam customers and partners. I've spoken to members of the C-Suite, Directors of Clinical Engineering, and Field Service Engineers/Imaging Service Engineers (FSEs/ISEs) on the frontlines of imaging system service.

First, let me share their common excitement about having the ability to harness the power of connected asset intelligence. Up until last year, they were in the trenches trying to keep pace on how their field service engineers can acquire value from IIoT technologies. The service folks I had talked to identified real-time asset utilization, remote diagnostics and monitoring, and predictive asset analytics as key to getting their departments up to speed in raising their support capabilities levels. We stepped in and offered Glassbeam Analytics.

It’s been a few months since deployment now and here’s what I am hearing. I’ve identified the benefits they've derived from using Glassbeam Analytics, focused on three wildly important goals:

  • FTR —First time repairs
  • MTBR — Mean time between problems
  • MTTR — Mean time to resolution

FTR— First time repairs:

Typically, the first thing an FSE or ISE must do before beginning the troubleshooting process is collect as much information as possible. Our solution empowers them by providing more detailed, more accurate information about the system, about the failure, and the moments leading up to the failure, before the Service Engineer goes onsite. 

Glassbeam Analytics provides contextual, granular information, giving the ability to identify intermittent failures and of course can give insight on which parts to carry or order before an FSE goes on site. Our solution allows easier access to a searchable knowledge base (tribal knowledge) about the specific type of failure and suggested resolutions as well as things that might have been tried in the past given the integration of a computerized maintenance management system’s (CMMS) past service history. The near real time parsing of machine logs provides an additional way to test implemented solutions before leaving the site, therefore increasing the odds the "root cause” has been found the first time. Overall system performance can also be monitored remotely over time to verify the resolutions and for detecting reoccurring failure trends from day to day.

MTTR — Mean Time To Resolution:

Integrating Glassbeam Analytics into their service best practices has shortened the entire troubleshooting cycle by providing more detailed information about sub-systems, parts failure, overall system health, and possibly the operations being performed in the moments leading up to the failure itself and all of this before travelling to the jobsite. In some instances, it’s been found that the root cause wasn’t within the imaging equipment but was due a failure within the support environment, for instance, chillers, HVAC, electrical supply, and so on, which was all monitored by Glassbeam Analytics.

In many cases, early warning alerts were sent, and issues addressed well before they appear as system shutdowns or catastrophic failures, thanks to our predictive trend analysis. The facilities and staff never even knew they had a failure. 

For root cause analysis, the FSE was enabled to easily search for other possibly discrete symptoms, patterns, or parameters by using the patented Explorer functions or setting up specific rules and alerts before they present as failures.

MTBR — Mean Time Between Repair:

Our solution helped imaging equipment service engineers to increase MTBR by highlighting symptoms which could be alleviated by changing environmental or external conditions i.e. HVAC or housekeeping procedures. When utilization data is combined with pattern analysis, and detailed failure information the Service Engineers have been able to mitigate or eliminate some repeat failures by adjusting protocols, workflow, and or operator procedures.

Does that sound interesting? Perhaps, this Datasheet and the Healthcare Industry’s First Industrial IoT Analytics Blueprint – CLEAN™ architecture, is a perfect follow up reading material.