5 Big Reasons to Switch to Artificial Intelligence-enabled Predictive Maintenance

Puneet Pandit

Next week, Glassbeam is gearing up to participate at AMMI Exchange 2019 conference in Columbus, Ohio. The event promises fantastic insights into the concerns and questions of Radiology and Clinical Engineering professionals and some great line up of talks from all walks of Healthcare Technology Management (HTM).

AI and Machine Learning, undoubtedly is making a buzz at this year’s edition.

This post is a preview to our participation at the event.

I am co-presenting a session with @Binseng Wang (Chair, ACCE International Committee) on Sunday, June 9 at 8 AM at the show. The crucial question Binseng and I will bring to the session is how AI/ML-enabled predictive maintenance of medical equipment saves costs and downtime and how Radiology / Clinical Engineering groups can evolve their equipment-patient experience journey around this new tech.

Today’s medical equipment systems produce huge amounts of complex machine data that is increasing rapidly in variety, requiring advanced data transformation solutions. Some of the data is structured, some is semi-structured and some is unstructured. Advanced AI/ML can now provide insights far beyond basic diagnostics to help healthcare providers anticipate and address equipment failures and maintenance.

With this as the context, here are the top five takeaways participants in our session can expect:

  • Embrace machine data-driven actions to maximize machine utilization — Overcome the deadlock of machine uptime remaining at the 96%-98% industry standard for years now. Healthcare facilities can increase uptime to more than 99.5%, resulting in more efficient maintenance windows and recovering millions of dollars per year in new revenues from higher machine uptime.
  • Adopt early warning systems based on Anomaly Detection (AD) — Providers can utilize machine learning-based AD techniques to predict anomalies from historical data sets and address issues earlier, saving millions in maintenance costs.
  • Realize Four KPIs with clear business impact — Facilities can measure each of these KPIs: Mean Time to Resolution (MTTR), Part Replacement Costs (PRC), First Time Fix Ratio (FTFR), Mean Time Between Failure (MTBF) that affect troubleshooting issues, procurement processes, field enablement processes, and preventive maintenance schedules using AI/ML technologies.
  • Extend a life of a part in machines, save previous capital dollars — By avoiding making part replacement decisions based on the age of the machine, number of scans performed, image quality rendered amongst other subjective factors instead rely on a solution to get a warning signal about a week in advance, for instance, of a potential tube failure that can save thousands of dollars per year per facility.
  • Initiate a fresh approach to reducing patient wait times at radiology labs / imaging centers — By combining machine log data with other data sources such as DICOM, HL7, CMMS, and RIS, in a single platform, hospitals for the first time have an eye on the patients’ movement end-to-end, therefore, the ability to avoid unnecessary delay in providing the services to the patient.

If you are looking to know more about our HTM solution and how they can work in your healthcare facility, shoot me an email. I’d love to know what we can do to get you started on this program.

See our solutions live

I invite you to visit our booth #442 at Huntington Convention Center, Cleveland, Ohio #AAMIExchange2019. If you’d like to schedule an exclusive demo at our booth, I’d encourage you to block a time slot using this link: Schedule a Meeting with Glassbeam team at AAMI Exchange 2019. Slots are fast filling up! Do block your time right away.

See you at AAMI Exchange 2019…

We are looking forward to witnessing some amazing innovations from other exhibitors, and stellar talks from renowned speakers through the show days. See you next week!

I’m interested in hearing your thoughts on the role Artificial Intelligence will play in Radiology and Clinical Engineering groups, and if there are examples you have to share.