The ultrasound TAM mirage: why billions haven't unlocked outpatient POCUS scale

Point-of-care ultrasound transformed hospitals. Why has it not scaled in outpatient care? POCUS has proven valuable in emergency medicine and critical care. In emergency departments and the ICU, it is now routine, used to diagnose pneumothorax, check cardiac function, evaluate undifferentiated shock, guide vascular access, and accelerate decision-making in ways that are both evidence-based and operationally indispensable. The clinical utility is real. The technology works.

And yet, despite hospital success and an estimated $3 to $6 billion invested across handheld hardware and AI-enabled ultrasound platforms over the past decade, POCUS has not achieved comparable scale in outpatient care.

That divergence is not a technology failure. It is a commercial architecture failure.

The denominator illusion

Total addressable market (TAM) models typically begin with a clean equation. Number of clinicians multiplied by patient visits multiplied by attach rate. The implication is straightforward. If ultrasound is portable, affordable, and AI-assisted, then nearly every outpatient clinician becomes a potential imaging node.

But the real denominator is not all outpatient clinicians who could benefit from simply having a handheld ultrasound probe in hand.

Rather, the value is only unlocked for clinicians practicing in revenue-cycle-mature environments, with imaging quality assurance governance, CPT-concordant documentation workflows, payer policy alignment, and without destabilizing internal referral economics, where reimbursement is better for standard imaging.

Thus the outpatient group that can benefit is materially smaller.

The TAM assumes frictionless reimbursement. The outpatient ecosystem is anything but frictionless.

CPT and medical policy were not built for handheld workflows

Most ultrasound CPT codes were constructed around comprehensive diagnostic studies performed in formal imaging environments. They assume defined view sets, structured interpretation standards, and department-level archiving and compliance processes. Payer medical policies were written within that same architectural understanding, and many do not even contemplate the presence of POCUS. Some could argue there are no CPT codes reflective of POCUS, only for full ultrasound. Beyond that, frequency limits, medical necessity criteria, and site-of-service assumptions reflect an imaging department model, a far cry from a handheld, AI-guided, AI-post-processed, clinician-performed workflow embedded in a 15-minute visit.

In some health plans, certain ultrasound studies also require prior authorization. If a handheld POCUS exam is performed without navigating that requirement, reimbursement denial risk is predictable. If documentation does not meet defined view expectations, downcoding is predictable. If medical necessity is not explicit in the record, payer recoupment risk is predictable.

These are not edge cases. They are structural features of the outpatient payment system.

Yet many MedTech manufacturers behave as though existing CPT codes are inherently sufficient. Few have engaged payers to evolve medical policy language to reflect handheld workflows. Few design product requirements explicitly around audit and reimbursement defensibility. Few model denial rates as a core commercialization variable.

Reimbursement was not addressed later. It might be assumed that it was never contemplated and engineered into the product strategy.

Ultrasound outside hospital walls is not a device sale. It is a payment-governed diagnostic service, but commercial approaches have yet to evolve.

The sonographer shortage narrative is economically incomplete

It is true that sonographer capacity is constrained in the United States and internationally. In emergency and critical care settings, POCUS has mitigated that constraint in time-sensitive environments where throughput and acuity justify workflow friction.

But expanding user bases beyond a sonographer via AI guidance in an outpatient clinic does not eliminate standard view requirements, image quality needs, documentation requirements, quality oversight, interpretation liability, denial management, or prior authorization workflows. It shifts the burden.

Labor substitution does not automatically create scalable margin. When clinician time expands to meet documentation standards and effective reimbursement is reduced by bundling, denials, or policy edits, already brittle unit economics become more fragile. If a family physician spends 12 to 15 minutes performing and documenting a limited study that reimburses at $50 to $80 after payer edits, the effective hourly rate may be lower than baseline E&M work, and that is before accounting for denial risk.

The math does not add up in fee-for-service outpatient environments.

The incentive conflict under fee-for-service

Under fee-for-service payment, comprehensive imaging studies frequently reimburse at higher rates than limited POCUS exams. Referral-based imaging supports specialist economics. In many systems, POCUS is substitutional rather than additive and may be more of a quick look before full imaging, but justifiable business cases are often absent.

Reducing downstream imaging is clinically rational. It may represent system-level savings. But unless the performing entity bears risk or shares in those savings, it may represent local revenue erosion.

The language of value-based care appears frequently in pitch decks. But without modeling who captures economic value, it remains rhetoric. Care enablement potential does not automatically translate into financial sustainability.

Why POCUS scaled in hospitals

The contrast with hospital adoption is instructive. Emergency departments and ICUs already operate within institutional infrastructures that support archiving, QA, compliance, and billing. Facility economics absorb variability in professional reimbursement and operate under DRG structures. Time-sensitive decisions justify operational friction, and internal cannibalization is minimal.

Payment architecture supported scale.

Outpatient environments lack that structural alignment.

Billions invested, and still no escape velocity

Disclosed venture capital across handheld and AI ultrasound companies exceeds $1.5 billion. When OEM R&D, commercialization infrastructure, and platform development are considered, ecosystem investment plausibly reaches $3 to $6 billion over the past decade.

If that level of capital has not unlocked broad outpatient scale despite strong evidence that POCUS changes clinical management in a substantial proportion of encounters, then the limiting factor is not probe engineering.

It is revenue-cycle design. It is payer engagement. It is utilization management alignment. It is business model innovation.

The real TAM

Ultrasound technology is ready. The evidence base is substantial. The care enablement potential in outpatient settings is meaningful.

But the true addressable market is constrained to settings that can:

• Capture reimbursement defensibly
• Navigate prior authorization requirements
• Survive audit scrutiny
• Maintain governance infrastructure
• Align incentives around avoided downstream utilization

That defines the real denominator.

A full solution in the outpatient setting includes not only the probe, but training, credentialing, PACS, reporting, readers, QA, management pathways, and much more. Until product development is engineered around what a full solution needs to enable payment architecture, value-chain economics, and policy engagement from inception, rather than bolted on after commercialization, outpatient projections will remain theoretical, suboptimal, and visionary about what could be.

Handheld POCUS has the potential to reshape ambulatory and post-acute medicine, but probes and AI alone will not deliver escape velocity.

Scale in healthcare is governed by economics. And economics require design.

This is the first of a two-part series. Continue with Part 2: The outpatient POCUS and AI litmus test, why AAA screening can scale and others won't, where I examine specific use cases where AI investment may be misaligned and highlight the one application where payment architecture, clinical guidelines, and market gaps align.

All views, analyses, and services presented here reflect my independent professional perspective, informed by more than two decades of real-world experience, and do not represent the views or positions of any current or former employer or affiliated organization.