Downtime patterns can give HTM teams a clearer picture of equipment performance and help inform maintenance and capital planning decisions.

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By Radhika Kumar, continuous improvement director, Trimedx

Viewing device downtime as a series of isolated, one-off incidents can mean missing a critical opportunity to improve system reliability, manage long-term costs, and safeguard patient care. Looking at downtime patterns over time can provide insight into the underlying condition of the asset fleet.

Using downtime data for strategic planning can help HTM teams make informed, long-term capital decisions instead of relying on reactive fixes. Artificial intelligence now makes this work faster and easier to run across an entire device inventory.

What Downtime Data Can Tell Us

Any time a medical device fails, the immediate priority is getting it up and running as soon as possible. One US study found clinicians and staff spend at least 10% of their working time around operational failures, including situations where needed equipment isn’t available. Once the device is back in service, health systems often close the event and never revisit it, losing a useful view of how that equipment performs over time.

Repeated service calls, recurring performance issues, or increasing maintenance demands can indicate reliability concerns and long-term operational risk.

Some health systems are using AI-powered tools to review device performance across the entire inventory and spot patterns or warning signals.

Downtime data can reveal which assets are becoming increasingly costly to maintain, which devices may be approaching end of life, and where reliability issues could eventually disrupt patient care.

Using Downtime Data for Predictive Insights

To interpret the warning signs correctly, HTM teams need to understand the factors driving downtime instead of simply tracking it. Not every downtime event points to a device that needs replacement, and not every performance issue signals a looming failure.

Real-time device monitoring and predictive analytics identify performance trends across thousands of devices. AI can help health systems identify which assets are performing as expected, which may require targeted intervention, and which could be candidates for future replacement by continuously analyzing device performance and service data. According to an Aquant report, service teams see a 39% improvement in resolution time when using AI, compared to traditional methods.

In a rural emergency department, a radiology and fluoroscopy system appeared to be operating normally despite experiencing occasional tube spits. While the issue seemed minor, predictive monitoring identified the recurring pattern and flagged it for investigation. Trimedx teams ultimately found the root cause was a generator issue, not a failing tube, preventing an unnecessary replacement and avoiding three to five days of unplanned downtime.

Discovering a root cause early can prevent an unnecessary replacement. Doing this consistently across an entire inventory can build a record of where continued investment is justified and where it is not.

Downtime Insights Can Guide Capital Decisions

Tight budgets and growing demand make every repair decision more important: not just whether a device can be fixed, but whether fixing it is the best use of resources. Downtime trends and performance data can help organizations distinguish between assets that can continue operating reliably and those that are getting more expensive and less dependable. This information can help with capital planning. Rather than replacing equipment on a fixed schedule, health systems can direct capital toward the assets where it matters most—the ones whose failure would most interrupt care.

In the early stages of implementing a centralized clinical asset management strategy at a large Midwestern health system, Trimedx’s predictive technology platform, TMX, allowed technicians to address dozens of issues with CT and MRI machines before a failure occurred. This saved more than 400 hours of equipment downtime and avoided revenue loss from canceled procedures.

Teams also used utilization history, maintenance records, and lifecycle stage data to develop a five-year replacement roadmap for critical equipment. This approach grounds decisions in true need rather than assumptions or outdated records.

Shift from Reducing Downtime to Eliminating It

Ultimately, health systems should strive to eliminate unplanned downtime, not just reduce it. One recent hospital study found medical equipment was unavailable an average of 8.78% of the time—with some critical equipment categories experiencing downtime of more than 40%.

As predictive technology matures, organizations can move beyond reactive maintenance and begin anticipating failures before they occur. Downtime data is critical to that transformation.

Organizations can use downtime events to make more informed maintenance decisions and prioritize capital investments more effectively. Combining predictive intelligence with long-term planning can help HTM teams work toward a future where equipment failures are exceedingly rare and patient care is less vulnerable to disruption.

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