Key takeaway: SR&ED for healthtech startups runs through the same two-part CRA test as any other software or hardware claim: technological uncertainty, resolved through systematic investigation. Regulatory approval and clinical validation are a separate track entirely — passing Health Canada review doesn't earn you a SR&ED credit, and needing it doesn't disqualify you from one. Software already takes 42.6% of all SR&ED credits allowed in a given year, and a lot of digital health engineering — diagnostic algorithms, biosignal processing, clinical data pipelines — lives inside that category. The regulatory wrapper around your product tells you nothing about whether the engineering underneath it qualifies.
Ask a healthtech founder whether their work qualifies for SR&ED and you'll usually get one of two wrong answers. Some assume a regulated, patient-facing product is too close to "medical" for a tax credit built around science and technology. Others assume the opposite — that anything touching clinical data or a diagnostic outcome must obviously count, because the stakes are so high and the domain is so specialized. Neither answer comes from reading the actual test.
This post walks through where SR&ED eligibility lands for a company building diagnostic software, remote monitoring, clinical data infrastructure, or a health-adjacent device: what clears the bar, what looks technical but isn't, and where healthtech teams specifically get the documentation wrong.
Does SR&ED cover healthtech engineering work
Yes — healthtech engineering can qualify for SR&ED, but "we work in health" isn't the test any more than "we're regulated" or "we use AI" is. The test is the CRA's two-part standard, applied the same way to a triage algorithm as to an inventory system: was there a technological uncertainty that standard practice couldn't resolve, and did you attack it through systematic investigation rather than following a known playbook.
Per the CRA's eligibility guidance, the "why" is advancement of scientific or technological knowledge against genuine uncertainty, and the "how" is systematic investigation by experiment or analysis. Computer programming, data collection, testing, and mathematical analysis all sit inside the eight recognized categories of support work, alongside engineering, design, operations research, and psychological research. A model for detecting arrhythmias in noisy wearable data gets evaluated on the same terms as a fraud model or a compiler. There's no separate health track, and no separate bar because patients are downstream of the outcome.
Regulatory approval and SR&ED are different questions entirely
Here's the distinction that trips up more healthtech teams than any other: needing Health Canada clearance, an ISO 13485 quality system, or clinical validation data has nothing to do with whether your engineering work qualifies for SR&ED. These are two separate tracks that happen to run alongside each other.
A device or algorithm that clears every regulatory hurdle on the first attempt, using a well-documented approach and a known validation protocol, may have generated excellent clinical evidence and zero SR&ED. Conversely, a team can run into genuine technological uncertainty — an algorithm that behaves unpredictably on a real-world patient population despite performing well in a controlled dataset — long before regulatory submission even enters the picture. Regulatory work itself, where you're following a prescribed, published validation protocol to demonstrate compliance with a known standard, looks a lot like quality control or routine testing once your team already understands the underlying technology — that's a CRA-recognized exclusion, and it applies regardless of how many binders of validation paperwork the work produces. What can still qualify, running alongside that regulatory track, is the underlying engineering: why a signal-processing approach fails on certain patient subgroups, why a model's performance degrades under a data condition no one's published a fix for, and what you tried before something held up.
This mapping of the general test onto a health-software scenario is our read on how the rule applies, not a CRA-published healthtech bulletin — there isn't one. The CRA publishes a technology-neutral standard, and expects an honest application of it whatever the product touches.
Where the uncertainty actually shows up
A quick filter: would a competent engineer at a comparable healthtech company have predicted the outcome before you ran the experiment? If yes, it's probably not SR&ED, no matter how much clinical weight sits behind the result.
Genuine uncertainty in this space clusters around a handful of recurring problems. A diagnostic or triage model whose accuracy degrades on a patient population, device, or data source no published benchmark covers, forcing structured experimentation across feature sets or architectures rather than tuning a known one. A biosignal — heart rate variability, gait, glucose trend — that behaves unpredictably outside a controlled setting, where standard filtering or calibration techniques don't hold up against the noise your specific sensor or population introduces. A data-interoperability problem where two clinical systems' records don't reconcile in a way any documented mapping resolves, and the team has to design and test its own approach. Or a real-time constraint — a monitoring alert that has to fire fast enough to matter without drowning clinicians in false positives — where no existing architecture hits both targets at once. None of that turns on the word "clinical." It turns on a documented trail: a hypothesis, a test, a result that didn't match, and what changed next. The CRA is explicit that a project doesn't need to succeed to qualify — a model that failed validation for a specific, documented reason is evidence, not a liability.
What usually doesn't qualify
Most of what a healthtech engineering team ships is still ordinary software or product work, whatever the domain looks like from the outside.
Integrating an EHR system through a vendor's published API, or building against a standard like HL7 or FHIR the way its specification describes, is adopting existing technology, not advancing it — the uncertainty about whether the interface behaves as documented was already resolved by the standard itself. Running a validation study against a protocol your team already knows will demonstrate compliance is quality control or routine testing, even when the study is rigorous and legally required. Building a patient-facing app on top of a well-documented off-the-shelf wearable SDK, with the engineering effort going into UI, onboarding, and notifications, is applying known technology.
None of that makes the work unimportant — a mishandled EHR integration can cause real harm faster than almost any other kind of bug. It just isn't SR&ED. Difficult, high-stakes, and clinically load-bearing are not the same test as technologically uncertain, and healthtech teams conflate the two more than most, because the downside of getting it wrong is so visible.
Two healthtech startups, two different claims
Picture two companies that would both describe themselves as "digital health" without hesitation.
Company A builds a medication-reminder app. It connects to a pharmacy API through a documented integration, sends push notifications on a schedule, and layers a clean interface on top. The product might genuinely help patients stay on schedule, and the engineering behind it is real. But nothing about the underlying technology's behaviour was uncertain going in — the pharmacy API's documentation describes exactly what it returns. If Company A files a SR&ED claim on this build, it's likely thin, because there isn't much "why" to point to.
Company B builds an early-warning model for sepsis risk from ICU vitals, where every published detection approach either fires too late to be clinically useful or floods clinicians with false alarms at the hospital's actual patient volume and monitoring cadence. The team runs structured experiments across several modeling approaches and feature windows, discarding most of them, with a documented log of what was tried, what the hypothesis was, and why each attempt fell short of the latency and accuracy bar. That record is what makes Company B's claim strong — not the fact that its product also happens to sit in an ICU.
Neither company is more of a "healthtech company" than the other on a pitch deck. Only one of them ran into and resolved a real technological unknown, and that's the whole difference for SR&ED purposes.
Where contractors and clinical partners fit
Pay an outside development shop, a data science contractor, or a specialized lab to build or test experimental work on your behalf, and only 80% of that arm's-length contract spend enters your qualified expenditure pool — the full invoice doesn't count. That rule has applied since 2012 and doesn't bend because the deliverable involves patient data instead of a checkout flow.
Whether the 80% rule even applies turns on the substance of the arrangement, not what the statement of work says: did the contract require specific experimental work with real technical risk, or was it a fixed-scope build to a spec your team had already defined? Who carried the cost risk if the model or system didn't perform? Healthtech teams working with outside data scientists, contract research organizations doing technical (not clinical-only) work, or specialized labs should get that scoping — and IP ownership — settled in the contract itself, since that's the fact pattern a reviewer looks at, not a story assembled after the invoices are paid.
The overhead question
Salaries for the engineers and data scientists running the experiments are the core of most healthtech SR&ED claims, and the CRA's proxy method adds an overhead allowance on top without requiring line-item tracking of every expense. The prescribed proxy amount has been 55% of your SR&ED salary base since 2014, meant to approximate overhead rather than making a team itemize compliance infrastructure, cloud costs for storing clinical data, and every software licence separately.
Healthtech infrastructure spend — secure data storage, specialized annotation tooling, compute for model training — can be substantial, and how it gets categorized against a specific eligible project is a real question with real dollars on either side. It's not something the 55% proxy automatically absorbs correctly by default. Worth resolving deliberately with whoever prepares your claim, not assumed away.
What healthtech teams get wrong about documentation
The most common failure mode isn't ineligible work; it's eligible work with no evidence trail. Healthtech teams already generate an unusual amount of process discipline — validation reports, model performance logs, incident reviews — but almost none of it was captured with a SR&ED claim in mind.
A validation report that says "model v3 cleared the accuracy bar, moved to submission" doesn't show what the hypothesis was going in, why the earlier version underperformed, or what specifically changed. A CRA reviewer isn't a clinician or a biostatistician by default; they're reading the narrative to understand whether real technological uncertainty existed and whether your team investigated it systematically. Teams that already document rigorously for regulatory or clinical reasons are usually sitting on most of the raw material for a strong claim. It just needs to be captured as the work happens and translated into SR&ED's language — separated from the regulatory narrative, not substituted for it — rather than reconstructed eighteen months later from a submission binder nobody wrote with a tax claim in mind.
Who should think twice before claiming
Skip this, or claim carefully, if your healthtech work is mostly integration and configuration: a well-documented EHR connection, a standard wearable SDK, or a validation study run against a protocol you already know will pass. The same goes if the honest answer to "what was uncertain here" is "we weren't sure which vendor to use" or "we weren't sure clinicians would adopt this workflow." Those are real business and clinical risks. They aren't technological uncertainty.
Plenty of healthtech teams also get it backwards the other direction, assuming that because their product is life-or-death or heavily regulated, the underlying engineering must automatically qualify. If your team ran into a genuine algorithmic, signal-processing, or data-interoperability unknown and tested its way through it with a documented trail, that's exactly the kind of experimental development the program is built for — separate from, and unaffected by, whatever regulatory process the product also has to clear.
What SR&ED for healthtech startups is actually worth
The rates don't change because the uncertainty happened to resolve in a clinical context instead of a general one. A Canadian-controlled private corporation earns the enhanced 35% refundable rate on up to $6 million of qualifying expenditures a year, for tax years beginning after December 15, 2024, with the basic 15% rate applying beyond that. At the enhanced rate, that's as much as $2.1 million a year. Credits on current expenditures like salaries at the 35% rate are 100% refundable up to the limit for most CCPCs — cash even against zero tax owing — though excluded corporations receive a 40% refund on that same spend instead.
The clock is identical too: a corporation's SR&ED reporting deadline is 18 months after its fiscal year end, with no extension process available. A healthtech team mid-regulatory-submission, heads-down on getting a clearance package finished, is exactly the kind of company that lets that date slip — and reconstructing why a model iteration failed validation fourteen months ago is harder than a general software team reconstructing a training run's logs, because so much of the record lives split across engineering tickets and a separate regulatory file.
That's the part of the job that doesn't care whether the R&D produced a diagnostic model or a database index: continuous documentation, captured from the tools your team already uses, reviewed by a qualified independent SR&ED expert before anything is filed. Automation on our side handles the collecting; a named human still stands behind what goes in front of the CRA. It doesn't decide whether your algorithm or device work qualifies — that's still the two-part test, run separately from your regulatory track — but it means the evidence exists when the claim gets built instead of being pulled apart from a submission binder after the fact.
Frequently asked questions
Does building a healthtech or medical product automatically qualify for SR&ED? No. The CRA's eligibility test looks for genuine technological uncertainty resolved through systematic investigation, the same standard applied to any other software or hardware claim. Working in health doesn't change the test; it changes what the uncertainty looks like.
Does getting Health Canada approval mean my work qualifies for SR&ED? No, and the reverse isn't true either. Regulatory clearance and SR&ED eligibility are separate tracks. A validation study run against a protocol your team already knows will pass looks like quality control or routine testing, which is explicitly excluded — regardless of how clinically important the study is.
Are diagnostic algorithms or clinical risk models eligible SR&ED work? They can be, if there's genuine uncertainty about whether an approach will work for your specific patient population, data, or performance target, and your team investigates it systematically. Applying a standard, published approach that performs as expected is routine practice, not SR&ED.
Does integrating with an EHR system or a wearable SDK count for SR&ED? Usually not on its own. Building against a documented standard or vendor API the way its specification describes is adopting existing technology, not advancing it. The uncertainty about whether it behaves as documented was already resolved before you integrated it.
Does outsourcing model or systems work to a contractor change what I can claim? Yes, if the contractor is arm's length. Only 80% of that contract payment enters your qualified expenditure pool, and whether it counts as a contract payment at all depends on the substance of the arrangement, not the invoice description.
Do failed models or clinical validation attempts still count for SR&ED? Yes, for the engineering side of the work. The CRA's own eligibility guidance states a project doesn't need to succeed to qualify. A well-documented model or approach that underperformed and was scrapped is evidence of real uncertainty, not a reason to leave it out of the claim.
Healthtech doesn't get an easier or harder SR&ED rulebook, and it doesn't get a stricter one because patients are on the other end of it. It gets the same two-part test as everything else, run separately from whatever regulatory approval the product also needs.
See what your healthtech engineering work could be worth — estimate your refund or check your eligibility.