The routing layer in healthcare AI is bigger than the diagnosis layer. Six million unattached Canadians prove it.

The trade-press coverage of AI in healthcare through 2023-2025 has run heavily on the diagnostic-AI category. The radiology approvals, the dermatology scanners, the cardiology rhythm analyzers, the pathology image interpreters, the ophthalmology screening tools. The press has reported these as the central story of AI in healthcare, with the deployment volume implied to be substantial and the clinical impact implied to be transformative.

The deployment data tells a different story. The diagnostic-AI category has shipped meaningfully but bounded by the regulatory, reimbursement, and clinical-workflow barriers discussed elsewhere. The routing-and-care-coordination-AI category, quietly through the same period, has shipped to substantially more health systems and reached substantially more patients, with operator-level deployment volume that by mid-2025 exceeds every diagnostic-imaging tool combined.

This is a counter-thesis essay. The trade-press claim is that diagnostic AI is the big story of AI in healthcare. The durable read is that routing AI is the bigger story, with the diagnostic category being the more press-friendly but smaller half of the actual deployment picture. Six million Canadians without a family doctor are the visible signal that the routing problem is the larger problem the AI category is being deployed against, even if the U.S. trade-press coverage has been generally not running the deployment-volume comparison.

This piece walks the press-vs-deployment gap, the categories of routing AI that actually shipped, the Canadian-system signal, what the structural reason for the gap is, and what the durable read on the deployment trajectory should be.

What the press-vs-deployment gap looks like

The diagnostic-AI category by mid-2025 has roughly 700-800 FDA-approved tools (many with overlapping functionality), with deployment volume concentrated at academic medical centers, large integrated delivery networks, and selected community hospitals that have made the engineering investment to integrate the tools into the clinical workflow. The total number of patient-encounters touched by diagnostic-AI in the U.S. in 2024 is meaningful but bounded by the regulatory pathways, the reimbursement structure, and the radiologist-workflow integration discussed elsewhere.

The routing-and-care-coordination-AI category has shipped through a different deployment path. These tools (claim-routing, prior-authorization processing, referral-and-care-coordination, scheduling-and-no-show prediction, post-discharge engagement, chronic-care-coordination, care-gap closure, social-determinants-of-health screening and routing) operate primarily through the payer-and-provider operational stack rather than the clinical-encounter stack. They do not require FDA approval (most of them are classified as administrative or care-management tools rather than medical devices). They are reimbursable through different mechanisms (administrative-cost recovery, value-based-care contracts, employer-and-payer-fee structures). They integrate with the EHR but do not depend on the clinical-workflow integration the diagnostic tools require.

The deployment volume, measured by patient-encounters touched per year, runs substantially higher for the routing category than the diagnostic category. The operator-class working in this layer has been generally aware of the deployment-volume gap. The press has been generally not.

The categories of routing AI that actually shipped

Several routing-and-coordination categories have been shipping at scale through 2023-2025.

The prior-authorization-processing category covers AI tools that accelerate the submission, review, and adjudication of prior-auth requests across the payer-and-provider relationship. Vendors here include Cohere Health, Olive (before its difficulties), the prior-authorization features inside the major payer-platform vendors, and several specialty tools. The deployment volume runs in the millions of prior-auth requests per year through these tools, with measurable cycle-time reductions and approval-rate improvements that the operator class has documented in their P&L.

The claim-routing-and-adjudication category covers AI tools that route, code, and adjudicate medical claims through the payer side. The deployment volume here is substantial because every claim that flows through a U.S. payer gets touched by some layer of automation, and the AI-augmented version of that automation has been progressively rolling out across the major payers and the larger TPAs. The number of claims-touched per year runs in the hundreds of millions.

The referral-and-care-coordination category covers tools that help health systems route patients to appropriate specialists, manage the referral workflow, and track the care-coordination outcomes. Deployment vendors include Salesforce Health Cloud, Epic-and-Cerner referral modules, plus specialty vendors. The deployment volume is measured in millions of referrals per year, with measurable improvements in referral-loop closure rates.

The post-discharge-engagement category covers AI-augmented patient outreach, follow-up scheduling, medication-reconciliation support, and readmission-prevention work. Deployment volume runs in the millions of post-discharge engagements per year across the deployed health systems.

The chronic-care-coordination and care-gap-closure categories cover the longitudinal care-management work for high-risk and chronic-condition patient populations, with AI-augmented routing of patients into appropriate care programs and coordination across the multi-disciplinary care team. Deployment volume here scales with the size of the at-risk population the program covers, which runs in the millions across U.S. payers and ACOs.

The combined patient-encounters-touched volume across these routing categories substantially exceeds the diagnostic-AI category by mid-2025. The deployment is real, the operational impact is real, the dollars are real, and the press coverage has been thinner because the routing work is operationally less press-friendly than the diagnostic-AI demos.

The Canadian-system signal

The Canadian healthcare system, structurally different from the U.S. system, surfaces the routing-vs-diagnostic gap more clearly because the diagnostic-AI deployment in Canada is even more bounded by the system's reimbursement-and-regulatory shape than in the U.S. The routing-AI deployment, by contrast, has been ramping faster in Canada than in the U.S. through 2023-2025, because the Canadian system's structural problems map more directly onto the routing layer.

The most visible Canadian signal is the unattached-patient population, which by 2024-2025 was estimated at approximately 6.5 million Canadians without a family doctor, with the number rising through the period. The provincial health systems running these populations have been deploying AI-augmented routing tools at substantial scale to manage the unattached-patient routing problem: matching patients to available primary-care capacity, routing them through walk-in clinics and virtual-care services, coordinating their access to specialty care without the family-doctor anchor. The routing-AI deployment in Canada is operationally significant because the unattached-patient population is so large that no manual coordination layer can scale to it.

The Canadian routing-AI deployment is not the same as the U.S. routing-AI deployment, but the structural pattern is the same: when the underlying healthcare system has a routing problem larger than its diagnostic problem, the routing-AI category ships faster and at higher volume than the diagnostic-AI category. The Canadian system's routing problem is more visible because the system's structural shape (single-payer, capacity-constrained, with a documented family-doctor shortage) produces the routing problem in a way the U.S. system's distributed insurance markets obscure.

The signal worth reading carefully is that the routing-AI deployment in Canada is running ahead of the U.S. equivalent on certain metrics, despite the U.S. having larger AI-vendor capacity, more venture-funded AI-startup activity, and more press coverage of the broader category. The structural fit between the Canadian healthcare problem and the routing-AI deployment is closer than the U.S. equivalent, which produces faster deployment.

Why the press coverage gap exists

The structural reason the press coverage has been concentrated on the diagnostic-AI category rather than the routing-AI category is mostly cultural rather than operational. Diagnostic AI is press-friendly: the demos are visual, the stories about cancer detection or stroke triage are narratively compelling, the FDA-approval cycle produces named milestones for the press to cover. Routing AI is operationally important and press-unfriendly: the demos are spreadsheets and workflow diagrams, the stories about prior-auth-cycle-time-reduction are not narratively compelling, the deployment milestones happen quietly inside enterprise IT integrations.

The gap also reflects the venture-capital-class allocation pattern. The diagnostic-AI category has attracted more visible venture capital because the unit economics, when they work, support enterprise-software-class multiples and the exit narrative is press-friendly. The routing-AI category has attracted less visible but substantial venture capital, with the exits running quietly through enterprise-payer or healthcare-IT acquisition rather than through public markets.

The combined effect is that the press, which reads the venture-capital-class signaling alongside the FDA-and-clinical-AI signaling, has been calibrated to over-cover the diagnostic-AI category and under-cover the routing-AI category. The structural reading the deployment data directly sees a different picture.

What the operator class should take from this

For founders building in healthcare AI, the part that holds is that the routing-and-care-coordination layer is the larger near-term opportunity than the diagnostic-AI layer, with substantially less competitive density relative to the operational opportunity. Building for the routing layer requires accepting that the work is press-unfriendly, the buyer is the operations side of the health system or payer rather than the clinical leadership, and the unit economics look more like enterprise-SaaS than venture-rocketship. Founders willing to accept those constraints have access to a category that is shipping at scale and producing real economic returns.

For investors, the read suggests that the routing-AI category is under-priced relative to its actual deployment trajectory, and that the diagnostic-AI category may be over-priced relative to the structural barriers that limit its deployment scale. Pricing the two categories with the same multiple framework produces wrong allocation decisions.

For health-system and payer-class buyers, the read is that the routing-AI deployment is where the dollar returns concentrate, and the procurement-and-integration investment should be sized accordingly. Health systems that have prioritized diagnostic-AI deployment over routing-AI deployment have sometimes captured headline-class wins without proportional operational returns. Health systems that have deployed routing-AI at scale have generally captured the operational returns the diagnostic-class deployment alone cannot produce.

The diagnostic-AI category will continue. It is real, it ships, it produces value. It is not, however, the bigger half of the AI-in-healthcare category. The routing layer is. Six million unattached Canadians are the visible signal of why. The operator-level running the math sees this clearly. The press will catch up. The next 24-36 months are likely to make the gap unmistakable, and the categories of routing-AI that have been shipping quietly will become the categories the trade press eventually covers.

The routing layer is the larger opportunity. The trade-press hasn't said so. The deployment data has.

—TJ