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EP 009
31 Min
Eva Deckers left Philips to implement AI inside a hospital, and learned that the best software still fails on everything that happens "underwater." Her conclusion: in healthcare, the care pathway is the solution, not the AI.
In most industries an AI mistake is annoying. In healthcare it can cost a life, so you have to get it right the first time. Eva Deckers spent nearly a decade leading design strategy at Philips before moving inside Catharina Hospital Eindhoven to run its AI Center of Excellence. On this episode of the Fini Podcast, she explained why so many AI projects stall on implementation, how to build trust when the stakes are this high, and why the real bottleneck is data, not algorithms.
Meet Eva Deckers
Eva led design strategy at Philips for nine years, running clinical studies on how to redesign care pathways, before moving into Catharina Hospital to lead its transformation to hybrid care and head its AI Center of Excellence. Her thesis is simple: innovation only matters when it changes how care is actually delivered, and her design background shapes how she approaches every AI decision.
The gap between building AI and using it
Eva's biggest realization moving inside the hospital was how much she had missed from the outside. You can have fantastic software and still fail, because of everything that happens "underwater": the purchasing process, the information analysts, the fragile one-to-one integrations that do not scale, and data that is never quite where it should be. Companies tend to target the healthcare professional as the user, but that person is just one of many stakeholders, the advocate for buying the solution, not necessarily the one who makes it work. Living fully in the customer's context, she argues, is something vendors still underestimate.
Building trust when mistakes cost lives
Eva's advice on safety is refreshingly unglamorous: do not invent a brand-new process for AI. Europe's AI Act already defines what counts as high risk, and healthcare already has strong safety and security processes, so reuse them and add only what AI specifically needs. Because today's AI cannot learn on the spot, it is essentially software with extra requirements. The durable investment is in the fundamentals, what she calls "plumbing wins": safe, available, well-described data, plus the lifecycle management almost no one in healthcare talks about yet, like detecting when a model drifts as patients change over the years. She also runs a deliberately broad portfolio, from startups to large vendors, because there is no settled answer yet for how to make AI safe and trusted, so you build the fundamentals and learn in your own context.
Why AI is not glue
Eva is wary of using AI as expensive glue to connect systems that do not talk to each other. Her instinct is to solve the problem in the real "water world" of data first, not with a layer of tools on top that simply move more data around and cost more. The real challenge, especially for hybrid care and remote monitoring, is near-real-time data availability across many systems, often asynchronous, where a clinician monitors a whole population and decides who needs attention. Solve the data challenge, she says, and AI follows. Skip it, and AI becomes expensive and unsustainable.
The care pathway is the solution
The clearest example came from skin-cancer reconstruction surgery. A surgeon wanted an AI that shows patients an avatar predicting how they would look after different reconstruction techniques. But when Eva's team mapped the workflow, the real improvement points had nothing to do with AI: how and where the patient conversation happens, whether before-and-after photos and patient-reported expectations are captured, and whether surgeons follow consistent workflow agreements. Only after those design and data steps does AI become valuable. As she puts it, AI is never the solution by itself, the redesigned care pathway is, and the biggest barrier to adoption is not technology, it is care pathway design.
What leaders should take from this
Live in the real context. The best software fails on the "underwater" details. Map every stakeholder, not just the end user who champions the purchase.
Reuse your existing safeguards. Don't build a separate process for AI. Start from your current safety and security processes and add only what AI requires.
Invest in the plumbing. Safe, available, well-described data is the durable investment, even though the return is slow and hard to sell.
Plan for drift. Models degrade as your population changes. Build lifecycle management and monitoring in from the start.
Don't use AI as glue. Fix disconnected systems and data first, rather than paying to move more data around on top.
Redesign the pathway, then add AI. The workflow is the solution. Process and communication changes often matter more than the model.
Listen to the full episode
Eva goes deeper on the AI Center of Excellence, precision medicine, and how to handle patients arriving with AI self-diagnoses, in the full episode of the Fini Podcast. You can connect with her on LinkedIn.
Fini builds AI support for regulated, high-stakes environments. Book a demo to see how we approach compliance and trust.
Why does Eva Deckers say the care pathway is the solution, not the AI?
Because adding technology to an unchanged workflow rarely helps. When her team mapped a reconstruction-surgery project, the biggest improvements were in patient communication, data gathering, and consistent workflow agreements, not the AI. AI becomes valuable only after those design steps, so the redesigned care pathway is the real solution.
How do you build trust in healthcare AI when mistakes cost lives?
Reuse existing safeguards rather than inventing new ones. Europe's AI Act defines high-risk uses, and healthcare already has strong safety processes, so start there and add only what AI needs. Invest in solid data fundamentals and lifecycle management, including monitoring for model drift as patients change over time.
What is the real bottleneck for AI in hospitals?
Data, not algorithms. Data is often not where it should be, integrations are fragile and do not scale, and hybrid care needs near-real-time data from many systems. Eva argues that if you solve the data challenge, AI follows, and if you skip it, AI becomes expensive and unsustainable.
Should AI be used to connect fragmented hospital systems?
Eva is cautious about using AI as "expensive glue." Her first instinct is to solve the problem at the data level rather than adding a layer of tools that move more data around and raise costs. AI is worth it where it adds genuine value, not as a patch for disconnected systems.
