The remote patient monitoring reset
The first generation of RPM was a reimbursement play. The second generation is a clinical outcomes play — and it looks very different.
What broke
RPM programs built around CPT 99453/99454 billing hit their ceiling. Payers tightened medical necessity requirements, patient engagement decayed after 90 days, and the unit economics stopped working for many operators.
What replaces it
Second-generation RPM is being built inside condition-specific care programs — heart failure, CKD, high-risk pregnancy — where the device is a means, not the product. The winning teams sell to at-risk providers, not fee-for-service billers.
Where founders should focus
Own the clinical protocol, not the sensor. The sensor commoditizes. The protocol, the escalation pathway, and the integration into the medical group's workflow are the durable moats.
The virtual-first economics finally work
For most of the last decade, virtual-first models struggled to hit unit economics that a payer would underwrite at scale. That is changing. The combination of ambient documentation, asynchronous triage, and specialty-specific care pathways has pushed provider capacity up and cost-to-serve down enough that a well-run virtual practice is now defensibly cheaper than the in-person alternative for a growing set of conditions.
The winners are the teams that resisted the temptation to be everything to everyone and instead pointed the entire operation at a single population until the CAC-to-LTV curve was decisively positive.
Distribution is still the moat
Product beats no product. Distribution beats product. The digital health graveyard is full of clinically superior tools that could not get in front of the patients they were built for. The founders winning in 2026 are the ones who chose a distribution channel — an employer, a health plan, a specialty group, a condition-specific community — and built the product for that channel from day one.
What we are watching from the studio
Inside the venture studio, we are prioritizing three build areas connected to this thesis: the workflow substrate under clinical AI (consent, provenance, routing, human-in-the-loop review); the measurement layer that translates model behavior into a claim a payer or regulator can act on; and the operational tooling for the specialty clinics and virtual-first practices that will be the first commercial buyers of the next wave.
We are less interested in another general-purpose copilot. We are more interested in the boring infrastructure that makes ten specialized copilots safe to run at once.
Why the incumbent playbook keeps failing
The instinct of a large healthcare organization is to procure a platform, run a governance committee, and let the technology diffuse through mandate. That playbook worked for imaging PACS in the 2000s. It has failed, visibly and expensively, for the current wave of AI-native tooling. The reason is structural: modern systems have to be tuned to the institution's own data, workflow, and liability posture, and that tuning is a product-engineering exercise, not a procurement exercise.
Founders who understand this shape their commercial motion around a technical champion inside the health system — usually a CMIO, a service-line chief, or a director of quality — and treat every deployment as a co-development contract with clear evaluation gates.
The corollary is that the sales cycle is longer than any founder wants to admit, and the winners raise capital that lets them survive twelve months of pilot without a single dollar of expansion revenue.
The economics underneath the demo
A demo is a snapshot of the best hour of a system's month. Enterprise economics are a function of the worst hour. The teams pricing themselves confidently in 2026 have done three things: they have measured deflection or throughput impact against a matched baseline, they have quantified the reviewer or clinician time returned per shift, and they have translated both into the currency the health system already uses in its own budget cycle.
Where founders lose margin is in the gap between what the model costs to run at peak and what it delivers on the median case. Inference cost curves are helping, but not fast enough to bail out a business built on a demo-day accuracy number. Underwriting a deployment requires a distribution, not a headline.
Expect procurement to get sharper about this in the next two cycles. The health systems that were embarrassed by 2023-era pilots have hired the people who will ask the harder questions in 2026.
