canada needs a better health data system — but it still needs a human hand on the wheel
ai can support diagnosis, but it is not ready to make fully automated decisions on its own, and it should not be asked to
published jun 18, 2026
4 minute read
4 minute read
human care can still be human even when technology is part of how it is delivered. adobe stock
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canada’s new national ai strategy sends a clear signal: the country aims to lead in ai and use it to improve health care. central to the plan is a push to strengthen how health data is shared and used. backing a national health data platform shows an understanding that fragmented systems are standing in the way of better decisions and better outcomes.
canada’s health system has been trying to function in a digital age with information scattered across institutions, uneven interoperability and real gaps between settings of care. a hospital data platform meant to support research and innovation across provinces, vital is an attempt to close some of those gaps, and the work to date illustrates both progress and the scale of the challenge ahead.
public skepticism about ai in health lingers and is warranted. canadians are right to be wary of health ai that operates without oversight, without transparency about its limits or without accountability when it gets things wrong. that is especially true when the conversation jumps too quickly from “ai can assist” to “ai can decide.”
at the same time, dismissing ai altogether would miss the real opportunity. the problem in canadian health care is not that we have too much technology. in many places, it is that we still do not have the right technology in the right places, connected in the right ways. the 2022 expert advisory group on the pan-canadian health data strategy found that fragmented data systems and outdated policies limited canada’s ability to use health data effectively during the pandemic.
health researchers have been working for years to make cross-jurisdiction data more usable, with a range of efforts trying to solve the same problem flagged in the advisory group’s findings. the vital model is one example that has gained traction, in part because it has been able to execute at scale.
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its promise is not that a machine will replace a doctor. it is that de-identified hospital data can be used more safely and effectively to support research, improve care and help build tools that work in canadian settings. the approach keeps provinces in control of their own data while allowing authorized analysis across jurisdictions.
in a system long defined by data silos, that is a practical step forward.
the national strategy attempts to outline what is possible when ai is deployed ethically in health care. when an ai tool produces a flawed answer about symptoms or medications, the consequences aren’t abstract. ai can support diagnosis if it is properly implemented, tested and used in the context of clinical care. it can help surface patterns, flag risks earlier and give clinicians better information to work with.
but it is not ready to make fully automated decisions on its own, and it should not be asked to. the technology is improving, but solutions need to continue to demonstrate higher accuracy and reliability and even then, it would never replace the people who are accountable and responsible for care.
we know that while technology can enhance care, the moments that matter most are still human. it is a clinician making sense of uncertainty, reading what is not in the chart and taking responsibility for what happens next. it is reassurance in a crisis, clarity about a diagnosis or treatment, someone saying, “we’re going to take care of you,” and meaning it.
but this does not always mean a doctor sitting in front of you in an exam room. sometimes it means a clinician reviewing data before a problem gets worse. sometimes it means remote monitoring that helps a senior stay safely at home. sometimes it means a decision-support tool that helps a provider catch something they might otherwise miss.
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human care can still be human even when technology is part of how it is delivered.
what matters is whether the system is built around people instead of forcing people to adapt to the system. that means keeping in-person care where it is needed most, improving digital access and confidence and designing ai tools that are clear about what they can and cannot do. it also means giving clinicians tools that support care rather than adding noise, risk or administrative burden.
canada does need to move faster on health data. the current patchwork is holding back better care, better planning and better research. but speed cannot come at the cost of trust.
if this new ai strategy is going to work in health care, it should be judged by a simple standard: does it help people get safer, more connected, more responsive care while keeping a real person accountable at the point of decision? if the answer is yes, canada should move swiftly ahead. if the answer is no, it should slow down.
that is the balance worth aiming for: better data, better tools and stronger support for clinicians, with a human hand still on the wheel.
this story was provided by medavie for commercial purposes.
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