Top 5 Jobs in Healthcare That Are Most at Risk from AI in Canada - And How to Adapt

AI is already changing Canadian healthcare from drug discovery and machine‑learning imaging to automating clinical notes and claims processing, a shift that boosts accuracy but puts routine administrative roles most at risk; Miller Thomson's primer on AI in Canada's health industry explains how these tools can raise privacy and liability questions.

“free up time for patient care” - Miller Thomson primer on AI in Canada's health industry

EY's six‑point playbook stresses ethical design, trusted data foundations and strong governance to keep AI safe and equitable: EY six-point playbook for AI in Canadian healthcare.

For workers and managers ready to adapt, practical up‑skilling is essential - Nucamp's Nucamp AI Essentials for Work bootcamp teaches prompt writing and workplace AI use so teams can trade time on paperwork for more patient‑facing care, not just hope for it.

The risk rankings in this study rest on a transparent, repeatable recipe: map U.S. O*NET task and work‑context measures to Canada's NOC occupations, compute the original AIOE index (52 human abilities × 10 AI applications), then adjust for how complementary AI is to each role using the C‑AIOE formulation - so occupations are placed into three buckets (high exposure/low complementarity, high exposure/high complementarity, and low exposure) based on median splits.

Key inputs were the 2016 and 2021 censuses and O*NET, with robustness checks across years; important caveats were also flagged - the index is a static snapshot that relies on researcher choices (weights like w and the complementarity parameter θ), and it uses U.S. task data cross‑walked to Canadian codes, so results are illustrative rather than deterministic.

Practically, the method shows exposure is widespread but not destiny: employment outcomes depend on firm adoption, regulation and up‑skilling, which is why Canada's approach to safe deployment (see the Statistics Canada experimental AIOE estimates) and coordinated governance (see the Pan‑Canadian AI for Health guiding principles) matter; imagine nearly one in three Canadian workers - about 4.2 million people - already in the high‑exposure/low‑complementarity group, a vivid reminder that measurement must guide policy, not panic.

Health information management (NOC 12111) workers are the behind‑the‑scenes linchpins who collect, code, record, review and manage the clinical data that hospitals, clinics and health boards rely on every day; their work - classifying diseases, operating information systems and preparing medical and administrative statistics - feeds funding, research and planning (Manitoba Health and CIHI are named recipients), so accuracy and familiarity with digital records matter as much as medical vocabulary.

Typical employers include hospitals, clinics and health‑record consulting firms, and many roles require a two‑year college diploma plus eligibility to sit the national certification exam; provincial profiles show solid median pay (BC median $65,509) and small but steady employment in specialized teams (WorkBC reports skills like critical thinking, coding and active listening as top requirements).

For managers and workers, the practical takeaway is clear: these technicians already operate and update health information systems and can pivot toward higher‑value tasks that protect data quality and support clinical decisions - skills that keep these jobs central to Canadian care delivery rather than redundant.

Learn more from the federal Job Bank profile and the WorkBC occupation page for local detail and training pathways.

Medical administrative assistants and medical secretaries - who keep clinics running by scheduling, managing patient intake, processing billing and claims, and updating EHRs - sit squarely in AI's crosshairs because so many of their daily tasks are repeatable and data‑driven; one risk tracker rates the occupation as in the

“imminent” automation range

with a calculated automation risk around 88% (and an average score near 83%) for roles doing high volumes of routine work, a wake‑up call for Canadian offices that still rely on manual intake and phone juggling (Automation risk for medical secretaries and administrative assistants - Will Robots Take My Job).

Yet the same research and industry guides show a clear path forward: AI can safely automate scheduling, reminders, claims checks and EHR updates while leaving complex patient conversations, exception handling and privacy oversight to trained staff - so CMAAs who learn to operate, validate and audit these tools become the linchpin of a smoother, faster front desk rather than its last guardian of paper (ACMSO guide: AI and automation in medical administration for CMAAs).

The practical

“so what?”

is plain: adopting targeted automation can turn crowded reception desks into calm, single‑screen command centres where humans handle the unusual and machines handle the mundane.

Medical transcriptionists and clinical documentation specialists translate clinicians' spoken notes into the clean, structured records that hospitals and EHRs depend on, but the job is changing fast: AI speech recognition can turn a 30‑minute dictation into text in about five minutes, leaving humans to catch terminology errors, edit for clarity and protect patient safety - tasks that still require medical knowledge, grammar and an eye for nuance (see the piece that explains how transcriptionists edit ASR drafts).

Demand has slipped as automation and outsourcing grow (a modest decline was projected in earlier outlooks), yet the practical path in Canada is clear: keep the documentation expertise, add skills that validate and audit AI outputs, and pursue recognized credentials so work shifts from pure typing to clinical documentation integrity and quality assurance.

Remote and part‑time options remain common, making this a flexible bridge role into higher‑value health‑informatics careers; for details on job duties, pay and training pathways see the comprehensive Coursera guide to the role.

Scheduling and patient‑flow coordinators - who juggle rostering, float pools, overtime rules and shift swaps while trying to match skill mix to real‑time demand - are a natural fit for AI that predicts census, automates rotas and flags coverage gaps; purpose‑built tools can convert the monthly “whac‑a‑mole” of last‑minute calls into a single coordinated notification and a filled shift from a phone tap.

Practical platforms now combine AI forecasting (census and acuity), skills‑based rostering, self‑scheduling and payroll reconciliation so managers spend less time wrestling spreadsheets and more time handling exceptions and staff morale - see the roundup of 11 staff‑scheduling tools for 2025 and the In‑House AI‑driven nurse scheduling platform that layers prediction on top of existing systems.

The adaptation playbook for coordinators is concrete: learn to validate model forecasts, own exception triage, audit fairness and fatigue rules, and run the human reviews that preserve patient safety and staff trust - so automation handles repetitive matches while people handle nuance and relationships.

“If we tried to remove MakeShift from the nursing staff, they would likely revolt.” - Barb Shellian (MakeShift customer)

Medical billing, claims adjudication and routine coding are the classic “back office” jobs that AI and automation now slice into: many billing rules and line‑by‑line edits are ripe for straight‑through processing, yet the system still relies on people for the tricky 20% of cases that need judgment - roughly one in five claims requires manual review, according to real‑time adjudication research - so modernization matters more than layoffs.

Canadian vendors like Maximus Canada claims processing solutions offer integrated claims platforms and omni‑channel operations to move legacy systems toward faster adjudication, and modular suites such as Medigent claims and payment modules already support ICD‑9/ICD‑10, reciprocal billing across seven jurisdictions and real‑time payment options that cut reconciliation headaches.

Case studies of AI plus RPA show dramatic gains: a Canadian insurer trial of automated adjudication lifted straight‑through processing many‑fold and reported a three‑hour daily turnaround to the RPA loop, about $50 saved per claim in the pilot and meaningful fraud recovery - proof that targeted automation trims costs while preserving human oversight.

Policymakers and managers should therefore prioritize standards, front‑end edits and human‑in‑the‑loop audits so coding and billing staff can move from rework to review, turning a crowded denials queue into predictable, rapid payments (real‑time adjudication policy brief).

“Is this claim valid? How much is our financial responsibility?” - Experian Health

Practical next steps for workers and managers in Canada start with a clear, local plan: map which tasks in your team are routine (prime targets for automation) and which rely on judgement, empathy or on‑site skills, then reassign people to the latter while building oversight for the rest - remember that in areas like billing roughly one in five claims still needs human review, so concentrate human expertise where it matters most.

Invest in short, job‑focused reskilling (aimultiple's reviews stress that over 40% of workers will need new skills by 2030) and protect entry‑level pipelines so on‑the‑job learning continues; see Adecco Canada's practical note on AI‑proof healthcare roles for career direction and role choices.

Managers should require human‑in‑the‑loop checks, fairness audits and clear exception workflows rather than blanket cuts, and workers should learn to operate and validate tools (prompting, auditing, basic model checks) - concrete skills taught in Nucamp AI Essentials for Work bootcamp can speed that transition and make AI a productivity partner instead of a threat.

“cutting entry-level roles is an ‘exponentially bad move' that harms internal talent pipelines.” - Dilan Eren (Ivey Business School)