Healthcare Technology Can Help Predict Failures Before They Occur

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Equipment failures in healthcare can have serious consequences, including delays in diagnosis or treatment, scheduling disruptions, and patient safety risks. Health systems should empower clinical engineering teams with technology that helps identify potential failures. This will allow health systems to gain visibility into the performance of their equipment and take action to prevent failures before they occur. 

Avoiding equipment failures leads to shorter turnaround times for maintenance, fewer disruptions to patient care, improved patient safety, and reduced stress for biomedical equipment technicians (BMETs).

Solve problems before they occur with predictive work systems

Health systems can significantly decrease the number of unforeseen equipment breakdowns by providing BMETs with predictive work systems. This advanced technology combines remote device monitoring, service expertise, and data science to recognize common, preventable equipment problems before a failure happens.

For example, an MRI could be losing helium without anyone realizing it. If this were to continue, the magnet could reach a critical point, resulting in quench, significant unplanned downtime, lost revenue, and effects on patient care. A predictive work system would alert technicians that the helium is approaching a critical level, allowing them to take preventive actions and schedule downtime that’s convenient for clinicians and the clinical engineering team.

A predictive work system could also analyze conditions that indicate air bubbles are developing within a CT machine, even when it appears to be working normally. This is a problem that typically can be difficult to predict. However, a predictive work system can notify BMETs that there’s a problem before it interferes with patient care.

While a predictive work system is an extremely beneficial tool for BMETs, it doesn’t replace their expertise. When the system automatically alerts the team of a possible problem, a skilled technician is needed to assess the problem and follow through to complete the necessary repair. This technology allows clinical engineering teams to foresee possible failures and prevent extended equipment downtime, canceled patient appointments, and lost revenue.

Increase preventive maintenance with a real-time location system

An effective way to streamline preventive maintenance is by taking advantage of real-time location systems (RTLS). Regular preventive maintenance is one of the most reliable ways to lessen the risk of equipment failures and reduce costs, but clinical engineering teams can struggle to keep track of large, complex, and mobile equipment inventories. RTLS allows BMETs to know exactly where devices are located, find them, and perform the needed preventive maintenance.

RTLS can reduce time technicians spend searching for a device and increase the number of preventive maintenance tasks they’re able to complete each month. TRIMEDX has found RTLS can reduce the time spent searching for devices by up to 50%. Without RTLS, if a BMET hasn’t been able to find a device to service it, the device could fail when a clinician tries to use it on a patient. This may lead to schedule disruption as well as clinician and patient frustration.

By tracking equipment in real time, BMETs can more easily stay on top of preventive maintenance work—saving time, reducing major equipment issues, and protecting the health system’s investment in its clinical assets.

Take advantage of automation to improve equipment testing accuracy

Traditionally, when testing equipment, BMETs have relied on pen and paper to record test results. After running the tests, they’ve written down the results, visually compared the results to acceptable limits, then manually entered the results in a database. This process is time-consuming and filled with opportunities for human error—which can result in incorrect readings, unexpected equipment problems, or other equipment issues that could negatively affect patient care and safety.

If BMETs have the latest technology, they can instead run tests through a mobile app that automatically feeds results into the system. The app can eliminate handwritten documentation, quickly validate that results are within permissible limits, and allow technicians to complete proactive maintenance at the time of testing. When technicians are equipped with this type of technology, the health system will see fewer incorrect test results and improved failure diagnostics, data handling, and compliance reporting.

Leverage artificial intelligence to minimize interference with patient care

There are a multitude of ways artificial intelligence (AI) can empower clinical engineering teams to act proactively to avoid equipment downtime. Another benefit of automated equipment testing is the valuable data collected. Machine-learning engines can collect and analyze the vast amounts of data generated during testing to continuously improve testing accuracy. This ultimately helps minimize unforeseen failures and ensures that equipment is consistently in working order.

By adding machine-learning tools to their toolbox, health systems can access real-time data and analysis about their clinical assets. By the time a health system manually analyzes data about equipment use, part replacement, and failure diagnostics, that data are likely out of date. In addition, manually analyzing such large amounts of data is time-consuming and expensive. AI collects and analyzes data in real time, giving health systems more visibility into equipment performance and allowing them to make smarter decisions.

AI also allows technicians to optimize their preventive maintenance to keep equipment and devices up and running while minimizing interference with patient care. For example, if a certain part needs to be replaced on a machine, AI-powered systems could warn the BMET there is a high probability another part will need to be replaced within three weeks. This allows the BMET to order and replace both parts at the same time, instead of working on the machine twice in a matter of weeks.

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