In the MR suite, providers have to walk a line between keeping scanners well maintained and up to date, and avoiding costly unplanned downtime. In recent years, remote monitoring capabilities have been a game changer.

These technologies generate alerts in real time to minor irregularities in components and environments that risk snowballing into bigger problems if left unaddressed. Responding promptly to changes in chiller water temperature or coldhead function, for example, can prevent weeks of unplanned downtime, hefty repair costs, and loss of revenue from canceled appointments.

Scanner manufacturers have been finding ways to bake AI into their equipment software in recent years, and remote monitoring represents a huge opportunity for that. It fits neatly into efforts to eliminate traditional break-and-fix service models in favor of predictive and preventive approaches to maintenance, which are expected to evolve as AI algorithms improve.

Despite that tremendous potential, AI-based remote monitoring alone cannot fully service an MR scanner. It’s just one piece of the puzzle. HCB News sat down with three MR manufacturers to talk about how they’re adapting their service offerings to incorporate the latest capabilities and customizing agreements based on the unique needs of a given provider.

Reducing maintenance and repair time
Healthcare providers today seek more risk-sharing service models that reduce overhead by allowing them to pick and choose which services are provided by the manufacturer or independent service team, versus which can be overseen in-house by biomedical personnel.

To accommodate these demands, many MR vendors offer training and support resourced to in-house service teams. United Imaging Healthcare, for example, has a cloud-based Empower Platform to connect biomedical engineers to its national service infrastructure, from which they can order parts, and view call center cases, work orders, service appointments, and more.

AI helps make risk-sharing service models like these more feasible for providers, according to Troy Lewein, U.S. vice president of MRI at United Imaging Healthcare. It also speeds up jobs by automating tedious and complex tasks and processes involved in repairs and maintenance.

“The system can self-diagnose and self-treat software issues before a field service engineer gets on site,” Lewein told HCB News. “For hardware repairs, the system can develop its own action plan and send for the appropriate part, so that the field service engineer or in-house biomedical engineer has everything he or she needs to significantly reduce system downtime once they get on site.”

With the amount of machine data that AI can collect, manufacturers are also using the technology to create digital twins of MR scanners to help predict failures before they happen on applicable real-world systems. Using complex algorithms, these solutions can visualize and display vast quantities of data and run simulations to show how a machine will likely behave under specific conditions.

As AI becomes more refined and learns to identify new patterns and changes, the deployment of digital twins for MR servicing will grow and evolve, providing OEMs with additional capabilities for monitoring and addressing diverse issues under a range of circumstances.

“This is where we use data and the information that's collected on a daily basis,” said Hani Chohan, VP for service growth for GE HealthCare U.S. and Canada. “A virtual model helps us predict things like the estimated remaining life of the component that's being monitored, and that helps us then look at the alerts around the environment coil, MR magnets, cryo, and more.”

GE HealthCare is leveraging this advanced technology with its OnWatch Predict solution, which combines remote monitoring and predictive analytics and creates models and alerts for changes in the MR imaging environment, image quality, magnets and cryos, patient handling, and core system, among other variables.

As a result of these and other potential capabilities fostered by AI, service plans for MR scanners are expected to incorporate a more usage- or condition-based preventive maintenance approach in their designs, further simplifying and accelerating maintenance and repairs for both OEM service manufacturers and the healthcare providers they serve.

“We will monitor more functions and come in when it is necessary instead of following a rigid preventive maintenance pattern. Always, the idea should be to create as few interruptions as possible, but as much as you need to keep the scanner up and running,” said Mark Lothert, head of product management for services for the U.S. at Siemens Healthineers.

Knowing servicing needs in advance
According to Chohan, utilizing AI and remote monitoring effectively requires providers to know and communicate everything they can about their MR imaging operations to the OEM. “They have to understand what they want from their service and support. What are some of those metrics or provisions that the service contract needs to have in place? For example, is it to minimize unplanned downtime, or the details around coverage response time? What's excluded, what's included with after-hour support, uptime guarantee, things like that.”

Many manufacturers like GE HealthCare will work with providers on the best time to carry out fixes remotely and in person before any actual failures occur, which helps avoid disrupting scheduled scans, unplanned downtime, and loss of revenue. For providers with large MR imaging fleets, knowing the criticality of each one is crucial to convey when prioritizing service requests and determining when to perform repairs.

Lothert says this also extends to preventive maintenance updates and upgrades, and that any remote monitoring platform should account for multiple types of monitoring, including machine performance, environmental conditions, parts status, cybersecurity, and more. Siemens Healthineers’ Guardian Program oversees performance but also can be combined with environmental monitoring. It also keeps cybersecurity in mind through continuous software updates with Advanced Now, which extends the life of the MR system to spare patients from paying huge investments for upgrades.

“Extending means not only to keep it running but also adding on new applications and so forth,” said Lothert.

According to Philips, another major medical imaging OEM, in-house biomedical teams should consult with clinical teams as well as information security and technology teams to understand and ensure they are aligned on what their service needs are before striking an agreement with the OEM. They also should ensure that any servicing contract, which should be updated regularly, meets the needs of the entire facility rather than just one department.

While AI and remote monitoring can help meet these collective needs, providers also need to be proactive and take steps to prevent issues from existing in the first place. “Maintain the MR environment as outlined in your MR vendor planning/siting document,” recommends Lewein.

Utilizing AI and remote monitoring solutions to maintain and repair MR scanners most effectively comes down to the individual needs of the provider, how those needs relate to their MR scanning operations, and how much information they share with the OEM. While AI and remote monitoring show great promise, the provider’s actions are still the determining factor behind whether or not these technologies successfully help meet their needs.

Additionally, on-site visits, whether by an OEM field service engineer or in-house biomedical engineer, will always be required in some capacity, and AI-based remote monitoring should not be misconstrued now or in the future as a replacement for these personnel but rather as assistants that are poised to make the maintenance and repair of MR imaging scanners simpler and more effective for both the OEM and healthcare provider.