The conference is in full swing when Kim Oreskovic admits she doesn’t wear a smartwatch. It’s the second day of the Health Tech Global Summit in Basel, March 2026, and the floor outside the room is humming of founders selling the opposite case: rings, patches, breath analysers, blood-test interpreters, all promising that more data will produce better health.
Oreskovic is the kind of person they have travelled to find. She is launching Innoviance, a preventative health venture fund built on four pillars — diagnostics, personalised AI, remote care, inclusive tech — and is currently writing cheques between £500,000 and £1.5 million into late-seed and early-stage health tech companies. Her credential is meaningful in this room: she co-built Antler Netherlands from inception and backed seventeen startups out of it, and before that she ran cross-border M&A at KPMG in Amsterdam and Vancouver. She knows the deal-flow side so there is, in other words, no obvious case for her not being deep in the data.
But she isn’t.
“I don’t wear a ring or smartwatch because I don’t want anxiety from more data,” she says. She’s not making a stance about it. She has tried the inputs. She did a blood test through LabCorp because her cousin in Silicon Valley pushed her to. She remembers the result. “I saw something like ‘potassium is high’ and didn’t know what to do. Then you just get stressed.”
This admission should not be mistaken for technological scepticism. Oreskovic is committed to building preventative health as a category. Her thesis, written into the four pillars on the Innoviance website, is that the gap between current healthcare spending and a system built to stop disease before it starts is the largest opportunity in European health tech. Her objection is structural. The data exists. The technology exists. Neither produces outcomes.
Why preventative health investors are walking past wearables
The bottleneck, she says, is not what most of the founders on the conference floor think it is.
“It’s incentives. If hospitals don’t have budget to implement apps, it won’t happen. The data exists, the tech exists, but incentives are misaligned, so progress is slower than it should be.”
This is the inconvenient part of the health tech investment thesis. Wearables and diagnostics dominate the conference circuit because they are visible, brand-friendly, and easy to demonstrate. The investor-side reality, Oreskovic says, is that the unit economics underneath those brands are punishing. “I like B2C, but there’s no money in it right now. I know many health tech VCs who are struggling. I see the returns in Benelux health tech funds — they’re pretty abysmal.”
The diagnosis sits inside a problem that European health tech founders confront every time they try to scale. “In Europe, you have to go country by country, regulation by regulation. In the US, it’s much easier. But even in the US, health tech is struggling.” The country-by-country friction makes B2C diagnostics in particular a slower business than the consumer-tech analogues founders are pitching themselves against.
What this leaves is a category problem investors are still figuring out how to price. The companies that look most exciting at a conference like this are often the ones least likely to return capital. Equally, the companies most likely to return capital are often the ones nobody is photographing.
The implementation bottleneck in health data
The description Oreskovic uses for the actual opportunity is one she repeats more than once. “Implementation. That’s where the smart money is going.”
It is the load-bearing claim of her thesis, and the claim that explains why a former Antler GP would walk away from a generalist fund to build something more focused. Implementation, in her use of the word, means the systems that move data from sensor to clinical decision and back to the patient as a behaviour change. It means the software layer that lets a hospital integrate a new app without re-budgeting the entire department.
Her test for whether a company is doing real work in this category is unsentimental. “Anyone who has a clear answer on how to turn data into outcomes through behaviour change is tackling the real problem.” Most of what she sees does not pass it. The deck that arrives saying “we have built an algorithm that predicts X” is competing in a crowded space where the predictive accuracy is increasingly commoditised. The deck that arrives saying “we have built the integration layer that lets the hospital act on the prediction” is in a less crowded space, and the harder business case to build, but the one with the better economics if it lands.
This is also where her four-pillar fund structure starts to make sense as a strategy. Personalised AI matters to her not because the algorithms are interesting but because, as she puts it, “historically we didn’t have a viable business model for preventative health because the incentives weren’t there. Now, with AI, we can prove these business cases to insurers and hospitals.” The AI is the lever that lets the implementation case land with the budget holder. Without it, preventative health is a hopeful argument in search of a buyer.
Where the smart money is moving in health tech infrastructure
“If your question is ‘where is the smart money going?’ it’s often not the sexy stuff — it’s infrastructure,” she says. The infrastructure she is referring to is not just the technical plumbing — APIs, interoperability standards, FHIR layers, but the commercial infrastructure that lets a preventative health company get paid by an insurer or a health system whose incentives currently reward treating disease rather than preventing it.
This is where the value-based-care argument enters her thinking, and where she finds something to admire about a system she does not work in. “I’m interested in value-based care — where you’d get a discount on your healthcare premiums if you’re healthy and taking preventative action. That’s a behavioural mechanism to move money in the right direction.”
“Yesterday someone mentioned that in ancient China, doctors only got paid if their patients were healthy. I found that fascinating.” The anecdote is durable because the inversion it implies is exactly the inversion preventative health needs in 2026: a payment model that rewards keeping people out of the system. Until that inversion exists, the implementation layer keeps running into a budget question, and the budget question keeps killing companies that look good in pitch decks.
Her own fund’s role in this is narrower than the thesis. Innoviance writes cheques into companies with a small team, early customers, and (ideally) a reason to expand into the US, where her network in lab partners like LabCorp can accelerate the route to revenue.
Behaviour change is the real outcomes problem
The reason she does not wear a wearable is not that she doubts the data. It is that she has experienced the failure mode the entire consumer diagnostics category is built on. The user receives a result. The user does not know what to do with it. The user becomes more anxious, not less, about a body they were already worried about. “Without integration and clear actionability,” she says, “the data alone doesn’t help.”
For an investor in preventative health, this is not a small problem to acknowledge in public. The natural pitch for her fund is the bullish one: data is exploding, costs are coming down, the inflection point is here. Her pitch is the inverse. The data is the easy part. The implementation is the entire game. And until somebody builds a system that lets her potassium reading produce something other than stress, the consumer side of the category will keep producing brands she likes the look of and refuses to buy.
Photography Mohamed Nohassi
