Artificial Intelligence (AI) and Machine Learning are hot topics in the global arena, including the medtech industry. Getinge is working on several cross-functional initiatives that will benefit both customers and patients.
“Using the vast amount of data we collect from our online devices will help our customers find and deal with problems before they even occur,” says Søren Stakemann, Director Products and Solutions Management, within the business area Surgical Workflows at Getinge.
Currently, Getinge has more than 3,000 pieces of equipment, such as sterilizers, ventilators and anesthesia machines connected online.
“Connecting our products online to collect data is something we have been doing for a long time,” Søren explains. “The challenge is to utilize this unstructured data in constructive ways that bring advantages for healthcare providers and their patients.”
Designed to predict failures
Getinge is about to launch PRIME (Predictive Intelligent Maintenance Engine), which is designed to predict failures.
“This tool enables us to tell a customer about a potential breakdown and the action that will prevent it,” Søren continues. “We can even alert if a single sterilizer cycle is about to fail, and soon we will be able to pinpoint the exact cause as well.”
Making accurate predictions preventing that customers change healthy components routinely, and supplying the right spare parts in time are expected to improve device uptime and increase sterile supply throughput for users of Getinge solutions.
“With PRIME, we are helping our customers to enhance their efficiency and reduce their costs” says Søren. “And as a patient, you will of course also appreciate that your scheduled surgery isn’t cancelled due to a breakdown.”
Using AI will enable need-driven maintenance also when it comes to Getinge products for acute care therapies. In addition, it can enable healthcare providers to compare costs and user patterns between hospitals and departments.
AI vs. Machine Learning
AI can be defined as a computer system performing a task that usually require human intelligence, while Machine Learning is the part of AI that enables computers to learn without being explicitly programmed. The learning happens by applying a set of well-defined statistical and mathematical techniques, which uses data to solve specific problems.
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