More Money, More Staff, More AI - So Why Are Canadian Health Costs Still Climbing?
Every few months, a new announcement lands. A funding injection. A workforce strategy. A sweeping AI initiative that promises to transform how care is delivered. And yet the underlying trend line barely moves - healthcare costs continue to outpace GDP, wait times remain stubbornly long, and clinicians are burning out at rates that no hiring plan has managed to reverse.
I’ve spent over two decades working at the intersection of digital health and public sector transformation. I’ve seen the announcements come and go. And I’ve come to believe that we are collectively reaching for the wrong levers, or at least, not reaching for the right one loudly enough.
The Three Escape Hatches
When health system leaders face a cost and capacity crisis, three responses tend to dominate the conversation.
The first is funding. More money for hospitals, more beds, more equipment. Necessary, without question, but insufficient on its own. You cannot fund your way out of a structurally inefficient model. You can only afford to run it a little longer.
The second is workforce expansion. Train more nurses, recruit more physicians, bring in internationally educated health professionals. Again, essential, but the pipeline is too slow, too expensive, and frankly too finite to outrun the demand curve driven by aging populations and rising chronic disease burden.
The third, and currently the most seductive, is technology. AI-powered diagnostics. Automated administrative workflows. Predictive analytics. The promise is real, and I don’t dismiss it. But here’s the uncomfortable truth: AI applied to a broken care delivery model doesn’t fix the model. It accelerates it. If patients are falling through the gaps between fragmented systems today, a faster, smarter version of those same fragmented systems will just drop them faster.
The Harder Conversation
What we talk about far less, and what I’d argue matters far more, is the fundamental question of how care is delivered. Not how much care, or who delivers it, or what tools they use. But the underlying model itself.
Who needs to see a physician versus a nurse practitioner versus a community health worker? Where should care happen: in a hospital, a clinic, a patient’s home, or a digital channel? When in the disease journey should the system intervene, at the point of crisis, or years earlier when intervention is cheaper and outcomes are better? How do we pay for health outcomes rather than health activity?
And critically, we need to ask who else belongs in that conversation. Housing, income support, mental health services, and community social care shape health outcomes in ways that no EMR or AI diagnostic can address. A care model that stops at the hospital or clinic door will always be fighting the last mile of a problem that started long before anyone called 911. Integrating with the social services side of the house isn’t a nice-to-have, it’s a precondition for any serious effort to bend the cost curve.
These are not new questions. But they are chronically underweighted in the policy conversation relative to the attention given to funding envelopes and technology deployments. Model redesign is slower, messier, and harder to announce at a press conference. But it is where the real lever is.
Getting It Right: Nova Scotia Health Authority
The most compelling evidence I’ve seen doesn’t come from places that deployed the most sophisticated technology. It comes from places that were disciplined enough to redesign first and digitize second.
The Nova Scotia Health Authority (NSHA) transformation journey is a good example. Working with partners including Kainos, the team spent six months in discovery before a single line of code was written, interviewing clinicians, mapping workflows, and understanding where data was fragmented and where decision-making was flying blind. The technology choices that followed weren’t made in a vacuum. They were made in service of a redesigned model, anchored to a clear citizen-centred goal: make it easier for Nova Scotians to navigate to the right care, in the right place, at the right time.
Getting It Right: The NHS Journey
The NHS App tells a similar story at population scale. The ambition wasn't to digitize the existing front door to care. It was to reimagine what that front door could be, reducing burden on an overstretched clinical community while genuinely empowering patients to manage their own health. With more than 50 million users, that critical mass is now catalyzing something even more significant: previously siloed hospital systems are being pulled into the orbit of a common patient-facing platform. The technology was the enabler. The redesign was the strategy.
In both the NSHA and NHS cases, the sequencing mattered enormously. Discovery before deployment. Model before mechanism. That discipline is rarer than it should be.
The Canadian Moment
Ontario is at an inflection point right now. The Primary Care Action Plan, the provincewide EMR investment, the broader push toward integrated digital health infrastructure, all represent a genuine and time-limited window to get the sequencing right.
What gives me confidence that this moment can be different is that the people doing the work aren’t just watching from a distance. Through Davis Pier, Kainos has teams embedded directly with Ontario Health Teams across the province, sitting alongside clinicians, administrators, and community care workers as they navigate integration in real time. That combination of system-level perspective and frontline proximity is rare, and it matters enormously when the goal is redesign rather than just digitization.
The risk is that we invest heavily in digitizing the current model and declare victory. The opportunity is to use this moment of system-level attention and investment to ask harder questions about the model those digital tools will serve. Are we funding care in the places where it generates the most value? Are we incentivizing prevention or just faster treatment? Are we building digital infrastructure that connects care teams, or just automates their current silos?
The window won’t stay open indefinitely. Momentum is a precious thing in health system transformation, and it has a way of getting absorbed by implementation without ever reaching the structural questions underneath.
A Challenge to Health System Leaders
Before the next technology investment decision is made, I’d encourage one prior question: is the care model this technology will operate within the right one?
If the answer is yes, or even mostly yes, then invest with confidence. Digital enablement on a sound model is genuinely powerful. But if the honest answer is that the model itself needs rethinking, then the technology decision is premature. Or at minimum, it needs to be made alongside a model redesign conversation, not instead of one.
More money, more staff, and more AI will all be part of the solution. But none of them, alone or together, will bend the cost curve without a fundamental rethinking of how care is structured and delivered. That’s the conversation I think we need to be having more loudly, and more urgently.
If you agree, let's chat further
Feel free to reach out to me on LinkedIn via the button below.
Alternatively, we'll be at eHealth Conference (Halifax Convention Centre) in June so be sure to pop by the Kainos booth (89/90) if you're there.