Getinge’s digital transformation shows scaling and adapting in equal measure

When Getinge started 120 years ago, the main focus was agricultural machinery, but that shifted in the 1930s to medical technology, which is what the globally recognized company is known for today.

More recently, products have become increasingly digital, with software that manages patient flows, tools for surgery planning, and sterile management processes that optimize inventory and ensure that surgical instruments are delivered at the right time to the right place.

“We’re now scaling all these services and have increased the volume toward our end customers,” says Getinge CIO Pelle Nilsson.

Last year, for instance, the company launched a connected operating table and a solution called Servo Twinview, a digital ventilator twin where you can follow patient data by computer, smartphone, or tablet without having to disturb the patient unnecessarily. It also provides the opportunity for remote support, training, and easier handovers for hospital staff.

Sensible integration

To be able to offer this type of service, internal support is also essential.

“The business drives product development, and then we collaborate so they’re integrated in a sensible way and in the right environments,” says Nilsson. “For example, we make sure we don’t work in isolation when it comes to cybersecurity, but rather we have a common thought about it and use common tools.”

The IT organization is also works to improve the quality and management of data to better utilize their many sources in order to become a more data-driven organization. There’s been good progress but, admittedly, there’s still a lot of work to do, says Nilsson, since it’s difficult to take ownership in a large organization and streamline data management.

There is measurable progress, however, as data from the company’s connected products are collected in its own platform, where customers have access to information via a portal.

“In the long run, we envisage new services such as being able to handle predictive maintenance based on what we know about the products,” he says.

Access to data

Over the past two years, Getinge has migrated its legacy BI solutions to a modern Power BI environment to make it easier for end users to access the data they need.

The company is also applying machine learning (ML) to gather information from various public sources that can be used internally for market and product analysis. In the future, there are opportunities to make information even more easily accessible by allowing users to search for information directly from a data warehouse instead of going through traditional BI tools.

Another example of how Getinge has used ML is an application made in response to the 2020 pandemic — and mostly used in Germany — where a map showed the number of infected people in a specific area, and where Getinge’s applicable products were sold so they could be moved from one hospital to another where they were needed most.

Generative AI opens the door

Now, gen AI makes AI more accessible and opens up many new opportunities for a company like Getinge. “Everything from simple translation services to more advanced solutions for creating product catalogues or risk analyses,” says Nilsson.

Even though Getinge is in an early stage, pressure from management to adopt emerging technologies is high. Among other things, Microsoft Copilot is now being tested in a number of pilot groups, and has been rolled out to parts of management. And like many others, Getinge is looking for use cases for AI applications that align with the business. Ideas are collected in a database where they’re scored according to the value they can provide, and at the moment, over 25 areas have been identified that show immediate benefit for the company.

“There’s also a lot of information about what other companies are doing, and for us it’s important to translate it into our own unique ideas,” he says. “But it’s important to get a balance in how much you invest and the value you get out.”

It’s a difficult balancing act. On the one hand, there’s demand from the business for more AI services, and at the same time, it’s essential to keep an eye on quality and cost.

“We don’t want to commit to specific suppliers because it moves so fast, so we try to be agnostic,” he adds. “The trade-offs are difficult to make and communicate, and as a user, you want a yes or a no.”

To move forward, Getinge tries to find internal tools that can be appropriately scaled — without absorbing too many resources — that offer more to employees. This may require more specific tools for more advanced users, and Nilsson thinks a user classification system may be needed for specific needs.

The cost of progress

While there is a lot of groundbreaking digitalization work going on at Getinge, challenges remain, one being technology debt due to acquisition activity.

“It’s a bit of an uphill battle when you acquire companies and get them onto our more standardized platform to achieve economies of scale,” says Nilsson. “It takes time. We don’t have so many resources we can put in a SWAT team.”

More importantly, the increasing amount of regulations the company must comply with demands even greater attention. Everything from industry-specific requirements to regulations for cybersecurity and AI put pressure not only on internal processes but on the whole value chain.

“It’s tough to face in addition to the time needed for all the demands that come internally,” says Nilsson.

Externally, requirements don’t only come from the EU, but for a global company like Getinge, it also needs to comply with requirements that don’t always harmonize with those of the EU.

“A typical example from the past two years has been about where data should be stored,” he says. “The EU was the first to do so, but now other parts of the world are also starting to impose requirements for storage, sometimes down to country level. And it creates barriers to how data can be moved between regions, and it becomes tougher with global services.”

The work with regulatory compliance also permeates the entire digitalization work that’s underway as it’s incorporated into all process improvements, and is part of the effort to gain control of data and ensure its quality.

“We work together with our business for product development,” says Nilsson. “If you look at the legislation that’s coming, we should be able to explain how our AI services work and have control over data sources.”