HLTH Europe 2026: Innovation in Critical Care

What HLTH Europe 2026 Revealed About Innovation in Critical Care

HLTH Europe 2026 in Amsterdam was an exciting hub of innovation with AI high on the agenda in nearly every session. But the leaders on stage weren’t debating whether AI belongs in healthcare. They were working through how to deploy it safely, where it actually helps, and what needs to be in place underneath it. The point that came up again and again: good AI starts with good data.

That matters for critical care, where a large volume of clinical data is generated every minute. Here’s what stood out, and what it means for the teams we work with.

What You’ll Learn

• The main themes from the keynote sessions at HLTH Europe 2026.
• Why structured, high-quality clinical data is the foundation for AI that works.
• How critical care fits into the move toward AI that supports clinicians.
• What hospital leaders can take from the event when planning their data infrastructure.

The Question Has Changed From “If” to “How”

The debate is no longer about whether to adopt AI. It’s about how to do it well.

In the session “AI: Hype, Hope, or Hard ROI,” leaders from the Mayo Clinic and Charité University Medicine Berlin looked at where AI is delivering real value and where it’s still looking for a problem to solve. Heyo K. Kroemer of Charité argued that healthcare can’t afford to ignore AI, while weighing adoption against clinical risk.

A second session, “You Had to Be There: Is AI the inflection point healthcare has been waiting for?,” brought together leaders from Philips, OpenAI, and the former head of NICE. As Stephanie Tran of OpenAI put it, it’s no longer a question of when to integrate AI, but how.

Once you accept that AI is part of healthcare, the questions become practical. The data, the workflows, and the trust matter more than the model.

Why Good AI Needs Good Data

AI is only as good as the data it runs on. That was one of the most consistent messages of the event, and it’s especially relevant to critical care.

Several speakers made the point that much of healthcare data isn’t structured or accessible enough to support AI. Records are built for people to read, not for machines to reason over. Systems don’t always communicate with one another. Even a capable AI tool can struggle when the data underneath it is scattered across places that don’t connect.

ICUs and operating rooms produce dense, continuous data: vital signs, ventilation, medications, fluids, and clinical events, captured minute by minute. When that data is structured and connected, it gives AI something solid to work from. When it stays siloed, it’s one more gap someone has to close by hand.

A clinical information system that captures structured data directly from devices does more than cut down on charting. It builds the kind of organized, connected record that good AI depends on. The data conversation and the AI conversation are really the same one.

AI Should Support Clinicians, Not Replace Them

There was clear agreement across the event that AI should help clinicians do their work, not take it over.

The view from the Mayo Clinic was to keep the clinician in the loop, with AI supporting decisions rather than making them. The picture people kept describing was AI working quietly inside the systems clinicians already use, handling some of the complexity and the administrative load so there’s more time for patients. Patrick Mans of Philips put the emphasis on solving real problems instead of innovating for its own sake, and on making AI usable enough that more people across an organization can actually benefit from it.

Trust Is a Prerequisite

Patrick Mans of Philips made the point that AI implementation is a change-management challenge as much as a technical one. He talked about democratizing AI: making it usable enough that more people across an organization can benefit, not just specialists. But none of that lands without trust. Clinicians need to understand and trust the tools they’re using, or the tools won’t become part of everyday practice. That’s as much about how a system is introduced as what it can do.

Trust also gets built over time, through evidence and through a track record. A platform that has been deployed and refined across hospitals in many countries carries a different kind of credibility than one that hasn’t. In a setting as demanding as critical care, that history matters.

Where AI Actually Helps

AI helps most when it works inside the tools clinicians already use, and when it spots patterns in data that people could otherwise miss.

The useful version of AI isn’t a separate tool clinicians have to open and manage. It’s built into the systems they already work in. Speakers described AI handling things like surfacing risk scores, helping prioritize events and tasks, and easing the documentation load, all without asking clinicians to step out of their workflow.

The bigger opportunity is in what AI can see that people can’t. A few speakers talked about AI finding patterns in clinical data that would otherwise go unnoticed, supporting earlier detection, sharper risk prediction, and better use of the information a hospital already holds. One example raised was AI helping with dementia diagnosis while taking work off clinicians’ plates. Both rely on the same foundation: data that’s complete, structured, and connected. The more demanding the question you want to ask of your data, the more its quality decides whether you get a useful answer.

Moving Upstream Toward Prevention

There was also a wider thread running through the event about the pressure on healthcare systems to rethink how they deliver care, and not just how they make the current model run better.

Several speakers described a move from treatment-heavy care toward earlier intervention and smarter use of resources. Heyo K. Kroemer of Charité named prevention as one of the main jobs for hospitals in the years ahead. That comes back to data, too. Acting earlier means spotting things sooner, and spotting things sooner depends on data that’s complete and available when clinicians need it.

Critical care sits right in the middle of this. It’s where some of the most urgent decisions get made, often without the full picture. A stronger data foundation there helps when it’s needed most.

Why This Matters for iMDsoft

Much of what we heard at HLTH Europe maps directly onto the work we do at iMDsoft. AI needs high-quality, structured clinical data. It needs to fit into how clinicians actually work. It needs people kept at the center. And it needs open, connected platforms to run on.

Critical care produces exactly the kind of rich, high-frequency data these applications draw on. That is why MetaVision is built around structured data capture, device connectivity, and the ability to connect with the systems a hospital already runs. The point is not AI for its own sake. It is about creating a connected data foundation that supports clinicians today and prepares healthcare organizations for what comes next.

Our new Connectivity Platform takes this a step further, evolving MetaVision from a Clinical Information System into an open platform for innovation. Through open APIs and FHIR-based interoperability, it enables healthcare organizations to connect AI-driven applications, analytics solutions, and digital health technologies within their existing digital ecosystem. Rather than operating as a closed system, MetaVision gives organizations the freedom to choose the solutions that best meet their clinical, operational, and strategic needs, and connect them efficiently. This open approach provides greater flexibility, creates long-term value for users, and supports the adoption of new technologies and innovation over time, without dependence on a single vendor.

Key Takeaways

• The AI conversation at HLTH Europe 2026 moved from whether to adopt AI to how to deploy it safely and usefully.
• Good AI depends on good data, and much of healthcare’s existing data isn’t structured or connected enough to support it.
• Critical care produces dense, high-frequency data that, once structured and connected, can serve as a strong foundation for future AI.
• The agreement across keynotes was that AI should support clinicians inside their workflows, not replace them.
• The most useful AI either works quietly inside existing systems or surfaces patterns people would otherwise miss, and both rely on good data.
• Open, connected platforms and reliable device connectivity are what make usable data and useful AI possible.

Bring Connected Data to Critical Care

The conversations at HLTH Europe clearly indicate that useful AI starts with usable data. See how MetaVision supports structured data capture and connectivity across critical care. Schedule a demo to talk through your data foundation.

FAQs

• Why does AI in healthcare depend on good data?

  AI draws its conclusions from the data it's given, so scattered or unstructured data limits what it can do. Speakers at HLTH Europe stressed that much of healthcare's existing data isn't structured or accessible enough, which makes data quality and connectivity the starting point for useful AI.

• How does critical care relate to AI?

  Critical care units produce dense, continuous clinical data captured from devices at the bedside. Once that data is structured and connected, it forms a strong foundation for AI applications, which makes the ICU and OR important settings in the wider AI conversation.
  What should hospital leaders prioritize when planning for AI?
  Hospital leaders should start with the data layer: how their clinical systems capture, structure, and share information. Open architecture, device connectivity, and connected systems matter before any specific AI tool, because they decide whether that tool can work at all.

• What should hospital leaders prioritize when planning for AI?

  Hospital leaders should start with the data layer: how their clinical systems capture, structure, and share information. Open architecture, device connectivity, and connected systems matter before any specific AI tool, because they decide whether that tool can work at all.