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.
Rick and I are honored to present among some of the most respected imaging healthcare professionals who are looking to advance biomedical equipment maintenance technology and programs to achieve better patient outcomes.
I feel the radiology equipment industry faces a year of disruption and change. With that as the background, our presentation will aim to help HTMA members embrace the disruption and show how we can position ourselves to win in the connected machines economy.
Related Info: Answers to Commonly Asked Questions
If you are unable to catch the talk in person, then read on for the top 5 key takeaways from the session.
#1: Using Machine Data Technology to Create Meaningful User Experiences
We need to start thinking about the radiology infrastructure differently. One area that will take prominence this year is the shift from planned downtime to planned preventive maintenance. So, how do we measure if this is the right approach? And what’s the impact on patients? We will see a sharp reduction in an equipment’s Mean Time Between Failures (MTBF), resulting in fewer machine downtimes and reduced patient revisits.
#2: Preparing for the Future: Building Relationships across Imaging Machine Modalities
Let’s examine the machine data closely. To give you an idea of the data velocity, just one MRI machine generates over 50K events and 2.5K warnings per day. Now, consider the reams of data pushed out from other modalities. It’s a gold mine, ripe to build Machine Learning models. So, what really is the business case for this exercise? We are now in a position to forecast capacity usage for the infrastructure as a whole instead of for a single machine. What is the organizational impact? Better capital planning budgets.
#3: Smart Maintenance is Changing the World of Connected Medical Equipment
We are now in the driver’s seat to augment the human experience with data-driven actions. This development is probably the closest to my heart – Sales. If we have the ability to send an advance notice to procure parts, that will make any sales professional’s day instead of waiting for an order to come through manual interventions. That’s Smart Maintenance in action. Prescriptive recommendations based on a machine or part’s lifecycle will help the procurement department to place advance orders or plan for replacements before a failure occurs.
#4: Positioning Radiology Maintenance Professionals to Achieve Extraordinary Outcomes
Machine log data from a radiology environment is both diverse and complex. We need tools that understand that complex data and automatically form a set of instructions or recommendations, say, from an existing knowledge base. This forms the new normal to perform data-driven troubleshooting and root cause analysis. So, how do we measure if this is effective? We should expect a reduced Mean Time to Resolution (MTTR) per incident.
#5: Cascading Consequences of Medical Equipment Problem Diagnostics
AI is reshaping diagnostic equipment maintenance in many ways, exposing technicians and repair personnel to a wealth of opportunities to proactively diagnose issues. Deriving insights using classification algorithms and NLP techniques, support professionals can now act upon a pre-flight checklist assembled even before the engineer goes on site, even before the problem occurs. What is the impact? Reduced First Time Fix Ratio (FTFR).
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