RTLS, AI, and ChatGPT Manage Assets at Turkey Hospitals

Published: November 8, 2023

MLPCare has deployed the asset management product from Borda Technology across 32 hospitals to provide end-to-end intelligence into each asset by managing data from BLE beacons.

Turkey’s healthcare company MLPCare is among the first adopters of a new product from Borda Technology that provides end-to-end intelligence about asset use in healthcare facilities. That includes maintenance, calibration, and breakdown management of the tools used to care for patients. The Asset Maintenance Management product, commercially released this summer by Borda, provides RTLS-based asset tracking, AI, and ChatGPT to provide technicians with maintenance or breakdown requests on the go, with real-time location of assets. The solution not only provides insights into asset use but also makes recommendations to ensure assets are not over- or under-utilized and provided with proper service before use on a patient.

Gürkan Cağlıoğlu, MLPCare’s Chief Technology Officer

With the product, MLPCare can schedule, track, and manage each stage of asset maintenance, calibration, and other activities in a centralized platform, says Gürkan Cağlıoğlu, MLPCare’s Chief Technology Officer. The system leverages Bluetooth Low Energy-based RTLS data via Bluetooth tags attached to assets and the hospital’s existing beacons. They also can gain more granular location data with Borda Technology’s Bluetooth 5.1 angle of arrival (AoA) locators.

MLPCare is the largest hospital group in Turkey, with 30 hospitals in 15 cities. “When we started exploring Borda’s hospital asset maintenance management, our main challenge was efficiently handling assets across our 30-plus branches,” says Caglıoglu. “We struggled to keep track of how many assets we had, whether they were being used effectively, and identifying idle ones.” Therefore, the healthcare company was seeking a system to streamline and standardize asset management, apply the workflow within global standards, and draw a picture of what assets were onsite and which were being used.

The company also wanted to find a solution specifically designed for healthcare. Caglıoglu points out that “asset management in healthcare is more complicated than other sectors since it requires unique complexities of the asset product lifecycle and site experience to manage operations.”

The healthcare company piloted the technology first in one of its smaller hospitals, chosen because it was a site with lower patient and asset traffic compared to larger hospital branches.

“This allowed us to uncover any initial challenges and fine-tune the system before scaling up,” says Caglıoglu. They then progressed to the implementation phase at the larger branches. “This stage enabled us to observe and optimize asset management operations across multiple hospitals simultaneously.”

Asset Maintenance the Most Recent of a Family of Solutions

Burak Bardak, Borda’s CTO

Borda was founded in the U.S. in 2007 and more recently moved its headquarters back to Turkey, where its founders originated, says Burak Bardak, Borda’s CTO. The company has an office in Sweden as well. The company reports a presence in 50 million square feet of hospital space, managing more than one million assets.

The company provides products for asset management and utilization and staff utilization and other products to make hospitals operationally aware.

Most recently, it released its asset maintenance management solution to bring AI and ChatGPT-based intelligence to asset tracking “to help our customers reduce downtime an asset’s process Life,” Bardak says.

Asset Maintenance Management

The solution consists of BLE asset tracking tags, locators, and access points that can provide up to a sub-meter accuracy of location for assets as they move around a facility, explains Burak Apaydin, Borda’s product manager. If a hospital requires only zone-level accuracy, as opposed to an exact location, however, the investment in hardware would be reduced. That’s because zone-level data can be achieved via access points alone.  If a company needs highly accurate location data in some areas but not in others, “we can do a hybrid mode,” he says. The AI algorithm on the software platform provides intelligence related to the accuracy of people or asset locations.

In March of this year, MLPCare’s deployment was live in 30 branches in Turkey. The goal is to not only provide information about the real-time location of an asset but also a breakdown of asset status, the maintenance required, and even automated scheduling for the appropriate service, says Doruk Ünsal, Borda’s product manager.

Typically, Borda provides its locators in a network that consists of single devices deployed for every 100 square meters. In this way, the deployment is built to be lower cost than traditional RTLS systems for location information; the technology displays where assets are, in real-time, on the map. The software can assist personnel in finding the closest asset they are looking for, or they can draw geofences and process alarm notifications according to the usage.

Suppose users need granular location data about which side of a room a tag is in and how close it is to other tags. In that case, the Borda product can provide reports on that, as well as interactions of personnel and assets – and even measure their proximity with each other.

Users open the mobile app and can use a QR code or serial number on the asset to pull up a form that allows them to view and update data about the maintenance for that item. Once any work order is completed, the requester is notified of the asset’s updated status, ensuring that assets are handled properly and are in good working condition. The biomedical technician can also rate the instructions to retrain the software model for that item further.

Hospital management can use the app to assign tasks to technicians as well. Those assignments can include details such as what asset requires what services, as well as where to find it. For cost analytics, Borda’s product also contains cost documents and maintenance expiration dates for each asset.

AI and ChatGPT for Analytics

To manage asset maintenance, Borda provides a mobile app known as Lighthouse AI so that hospital personnel can follow a list of maintenance tasks displayed for them specific to that equipment. Those assets might be cardiovascular devices, dialysis machines, or other healthcare support devices. Technicians can access the step-by-step troubleshooting instructions and solutions to fix the breakdown with just one click through the application.

Borda’s product can measure the uptime and downtime of each item based on its movement and location, leveraging the accelerometer sensors built into the BLE tags. Based on that movement, for instance, Borda’s solution can provide executive reports about the utilization rate of these devices. If a hospital already has a software system measuring the use of an asset, such as an X-ray machine, that data can be fed into the Borda solution.

The features enable users to view overall asset utilization rates and when some items may be used less often and may not even be necessary onsite.

The data helps healthcare companies manage data related to contractors and their services, such as a third-party company that might be conducting calibration. The Borda product links the real-time data to information about each service contract.

“We can measure the uptime, downtime, and if there’s a breach in the contract,” says Caglıoglu. “There’s no way a biomedical technician can know all the details of that particular contract, but our platform keeps track of the contract.”

The system can determine—based on usage data, maintenance, and servicing—when it is time to decommission a medical device.

Thus far, MLPCare is using the product to monitor 191,000 items across its hospitals. Caglıoglu says it has benefited the hospitals in multiple ways, offering that “we received excellent feedback from the project’s inception, which has been instrumental in addressing various challenges.”

Since deploying the technology, adds Caglıoglu, “the integration of ChatGPT into asset breakdown management features shows great potential to optimize and accelerate breakdown flows. Therefore, the system is more supported and well-trusted among the users.”

The hospital company has found the technology increased the efficiency of preventative maintenance and calibration, ensuring timely execution, thereby extending asset lifespan and averting potential patient safety risks. Caglioglu says the Asset Maintenance Management has saved staff time and further elevated patient care.

“Instead of spending excessive time on manual asset searches, we can now locate mobile assets in seconds, significantly improving our operational efficiency,” he said.

In the future, the technology may provide MLPCare with a competitive advantage. “Given the dynamic nature of our era and the constant need for upgraded systems,” Caglioglu points out, “we envision incorporating Artificial Intelligence into our hospital Asset Management for network-connected devices to be utilized in more healthcare processes.”

Key Takeaways:

  • Borda’s asset maintenance management solution leverages AI and ChatGPT to provide insight into all phases of hospital assets.
  • MLPCare in Turkey has deployed the technology across 32 hospitals to improve efficiency and boost patient safety.