🎧 Part I Podcast free on Spotify.

Thoughts on Healthcare Markets & Technology

Part I: HHS Goes All-In on ChatGPT for State Audits: What the May 2026 Generative AI Fraud Expansion & Feb’s CRUSH RFI Mean for Payment Integrity, Provider Audit Defense & the Health Tech Buy-Side

HHS shipped an LLM workflow for federal audits without an RFP. No vendor circus. No multi-year authorization. That is the actual story from May 21…

Listen now

an hour ago · Thoughts on Healthcare

🎧 Part II Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Part II: HHS Goes All-In on ChatGPT for State Audits: What the May 2026 Generative AI Fraud Expansion & Feb’s CRUSH RFI Mean for Payment Integrity, Provider Audit Defense & the Health Tech Buy-Side

HHS shipped an LLM workflow for federal audits without an RFP. No vendor circus. No multi-year authorization. That is the actual story from May 21…

Listen now

an hour ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

1. What HHS actually did on May 21, and what got lost in the press cycle

2. The CRUSH RFI was the real signal back in February

3. Who built this, and who is going to build the next version

4. The RFP question, the procurement vehicles, and why there is no single mega contract

5. What the government can do now versus what industry still has to build

6. Payment integrity vendors, the incumbents, and the new provider audit defense category

7. The investor angle, with three buckets of capital deployment

8. Selling to the feds as a health tech founder, a casual playbook

9. Closing skepticism, the legal exposure, and the next 18 months

Abstract

• On May 21, 2026, HHS Asst Sec for Financial Resources Gustav Chiarello announced a rolling generative AI ingestion of state and grantee single audits from any entity spending >$1M/yr in federal funds. Tools include ChatGPT plus other LLMs. Targets: chronic noncompliance, repeat deficiencies, material weaknesses, delinquent audit obligations. Penalty: loss of funding.

• Builds directly on the Feb 27, 2026 CMS CRUSH RFI (Comprehensive Regulations to Uncover Suspicious Healthcare), comments closed 3/30/26. Topics: enrollment screening, identity proofing, ownership controls, MA preclusion, lab fraud (genetic + molecular dx), DMEPOS reform, claim filing deadline compression, AI-assisted coding review, beneficiary solicitation, surety bonds, Medicaid integrity, Marketplace integrity (FFE + SBE).

• 2025 enforcement baseline CMS cited: $5.7B Medicare payments suspended, 122,658 claims denied, 5,586 billing privileges revoked, 372 fraud referrals worth $3.7B. Concurrent actions: nat’l 6-mo DMEPOS moratorium, $259.5M Medicaid funds withheld from MN.

• Healthcare payment integrity market: $15.12B (2025) to $28.02B by 2030 at ~13.1% CAGR per Mordor. 6.26% claim error rate persisting. 60%+ claims now cloud-processed. Roughly $20B in legacy mainframe admin overhead still being retired.

• Gov-side incumbents: Cotiviti GOV (RAC Regions 3, 4, 5), Optum FWA, Performant, Qlarant, Gainwell (post HMS), SAS Institute, NTT Data. Commercial-side incumbents: Cotiviti, Optum, Codoxo, EXL/SCIO, ClarisHealth, Healthcare Fraud Shield, Shift Tech, Lyric, Zelis, HealthEdge, Sagility, Multiplan.

• Oz quote: “padlocking the jar and letting them starve.” RFK Jr. framing: “pay and chase” to “detect and deploy.”

• Three layers of buy-side opportunity emerging: (1) gov-side document-heavy unstructured data tooling, (2) provider-side audit defense and counter-AI, (3) infra (eval, observability, explainability, fed-compliant RAG) underneath both sides.

What HHS actually did on May 21, and what got lost in the press cycle

The press cycle made it sound like HHS pointed Skynet at every Medicaid claim. The actual scope is narrower, more interesting, and more strategically important than the headlines.

Single audits are filed under OMB Uniform Guidance, 2 CFR 200 Subpart F, by any entity that spends $1M or more of federal money in a year. States, counties, public universities, hospital systems with grant funding, nonprofits running addiction services or research. Audits land at the Federal Audit Clearinghouse. Most then sit there. Chiarello, who is the Assistant Secretary for Financial Resources (not exactly a household name), put it bluntly in the AP interview: everyone files an audit, it lands with a thud, nobody does anything. HHS is now running those PDFs through LLMs to surface chronic deficiencies, then sending formal letters that say, in effect, fix this or lose your funding. Chiarello also told reporters other federal departments could pretty easily copy the approach.

That framing matters for at least three reasons. First, the analysis is happening on already-public reports, which dodges most of the PHI and data-sharing issues that have bogged down federal AI projects for a decade. Second, HHS did not need new statutory authority to do this. The agency was sitting on the documents already. Third, it sets the political and procurement template for the next, much harder thing, which is bringing AI into pre-pay claims integrity on actual Medicaid and MA data flows.

The press reaction underplayed all three of those points and overplayed the dystopian framing. Anyone tracking the space should ignore the surface narrative and pay attention to the fact that HHS just shipped its first internal LLM workflow without an RFP, without a vendor selection circus, and without lawyers having a stroke. That is unprecedented for this agency at this scale.

The CRUSH RFI was the real signal back in February

Three months before the May announcement, CMS dropped the CRUSH RFI in the Federal Register on Feb 27, 2026, with a 30-day comment window that closed March 30. CRUSH stands for Comprehensive Regulations to Uncover Suspicious Healthcare, which is exactly the kind of acronym a brand consultant would have flagged but apparently nobody at CMS asked.

The RFI is enormous. It covers more than a dozen subject areas, each of which is its own enterprise software market. The biggest themes: enhanced provider enrollment screening with identity proofing and stronger ownership controls including potential citizenship and residency tests on beneficial owners. Preclusion list reform to stop revoked providers from billing Medicare Advantage. A possible requirement that MA providers also enroll in traditional Medicare so CMS has a continuous view. New tools against laboratory test fraud, particularly genetic and molecular diagnostics where the FWA economics have gone berserk over the last five years. DMEPOS supplier overhaul including the now-active six-month national moratorium on new enrollment for prosthetics, orthotics, registered pharmacist, and respiratory therapist supplier categories. Tighter claim filing deadlines, possibly compressing the current one-year window. AI-assisted coding accuracy and medical record review. Expansion of the DMEPOS telephone solicitation ban to other channels. Higher surety bonds and broader provider types subject to them. Stronger Medicaid and CHIP integrity tools, with more state authority and incentive payments. Marketplace integrity for both the federally facilitated exchange and state-based exchanges, with one of the literal questions in the RFI asking how CMS could use advanced technologies including AI to prevent, detect, and address FWA in those exchanges.

If a stakeholder read that menu and did not file comments, they will be eating whatever CMS decides on their behalf in the proposed rulemaking that follows. The Hall Render, Foley Hoag, DLA Piper, and Applied Policy memos all flagged how aggressive the program could become. The AHA filed its comments on March 30 and pushed back on automated downcoding, hallucination risk in AI coding tools, and the absence of independent physician review of coverage denials. AHA also pre-positioned legal arguments that will show up in court two years from now when the first FCA actions hit.

Mehmet Oz, in his administrator capacity, gave the line of the year for this announcement: CMS is done trying to catch fraudsters with their hands in the cookie jar, instead they are padlocking the jar and letting them starve. Whatever one thinks of the metaphor, the policy is real. Kennedy, as HHS Secretary, framed the shift as moving from pay and chase to detect and deploy. That phrasing is not a casual rebrand. It is a specific procurement and architecture signal. Pay and chase is recovery audit contractors, retrospective review, and recoupment workflows. Detect and deploy is pre-pay analytics, real-time scoring, and automated suspension at the EFT layer. Different vendors, different tech stack, different unit economics.

Who built this, and who is going to build the next version

For the May 21 announcement specifically, HHS appears to be using off-the-shelf ChatGPT and other unnamed tools. There was no public RFP for the single audit ingestion work. Chiarello told reporters the program is being run inside his office with existing tooling. This is not a flex on the technology, it is a flex on procurement. HHS effectively bypassed the entire federal acquisition stack by using commercial AI products on already-public data.

The previous generation of HHS AI work, going back to 2023, used tree-based machine learning models for outlier billing in Medicare. The then-HHS CIO Karl Mathias was on the record at FedScoop confirming that work. Those tree models are not glamorous, but they consistently beat newer architectures on tabular claims data, which is why the payment integrity incumbents still ride them. Beneath the gen AI announcement is a much older statistical fraud detection infrastructure that has been in production for years and will continue to be the workhorse.

For the next version, several procurement paths matter. Cotiviti GOV Services already holds the CMS Recovery Audit Contractor contracts for Regions 3, 4, and 5, awarded last year through competitive procurement. That gives Cotiviti structural advantage on any task order modification or scope expansion to pre-pay or AI-assisted review. Performant and Qlarant still hold UPIC, MEDIC, and SMRC work for various regions. Gainwell controls a big chunk of state Medicaid MMIS infrastructure since acquiring HMS, which historically did Medicaid third-party liability and credit balance recovery. SAS Institute is embedded in the Center for Program Integrity analytics shop. Palantir Foundry already sits inside CMS through the broader DOGE-era data integration push. Microsoft Azure Government, particularly the Secret-cleared Azure OpenAI Service from the August 2024 partnership with Palantir, is the most likely hosting environment for anything classified or PHI-handling.

So when somebody asks who built this, the honest answer is: ChatGPT for the audit summarization, SAS and tree-based incumbents for the Medicare claims work, Palantir Foundry for data plumbing, and a long tail of payment integrity contractors for execution. None of them got a fresh contract for the May announcement. All of them are positioning for the next one.

The RFP question, the procurement vehicles, and why there is no single mega contract

A common mistake when reading announcements like this is assuming there is a giant capstone RFP coming. There almost certainly is not. CMS and HHS run procurement through a sprawl of vehicles, and the AI work will get spread across them rather than concentrated in a single award.

The biggest vehicles to watch: CMS’ SPARC (Strategic Partners Acquisition Readiness Contract), the agency’s main IT and analytics ID/IQ. NIH and CDC use NITAAC CIO-SP4 for IT and a portion of analytics. GSA MAS (the consolidated multiple award schedule) handles most commercial software. The VA runs T4NG-2 for IT and is increasingly buying generative AI tools through OT authority. DHA has its own enterprise vehicles. For smaller, faster work, the OT authority (Other Transactions) lets agencies skip most FAR rules entirely, which is how DoD has been doing its AI buys. Phase III SBIR conversions can also be used to sole-source if the work came out of a prior SBIR award.

The CRUSH-derived rulemaking, when it lands, will probably spawn modifications to existing payment integrity contracts (Cotiviti, Performant, Qlarant, Gainwell) plus new task orders for things like beneficiary identity verification, AI-assisted coding review, and surety bond compliance. Each will be modest in dollar terms (tens of millions, not hundreds), but in aggregate the program will move billions over five years.

For a founder thinking about going after the work, the actual answer is not “respond to the RFP.” It is: get on a vehicle, sub to a prime, build past performance, then prime your own task order on the second pass. The vehicle math typically takes 12 to 24 months. The FedRAMP authorization timeline is 12 to 24 months on top of that. Anyone who is not already on a vehicle and does not have FedRAMP Moderate is two to three years away from being able to invoice CMS at scale, regardless of how good their software is.

What the government can do now versus what industry still has to build

What the government has working in production today is a surprisingly limited capability set if you actually look at it. There is document summarization on text-heavy audits, courtesy of the May 21 announcement. There is tree-based ML on Medicare claims for outlier detection. There is statistical sampling for PERM and CERT improper payment measurement. There is Palantir Foundry doing cross-system entity resolution and data integration. There is human-in-the-loop investigation workflow at the UPICs and MEDICs.

What is missing, and where industry has to build, is a longer and more interesting list. Real-time pre-pay risk scoring at the EFT layer remains aspirational at the federal level despite Oz’s padlock metaphor. Multimodal fraud detection that combines claims with medical records, telehealth video, audio of patient encounters, and prescription history barely exists in commercial product form. Provider identity continuity, meaning the ability to follow an individual provider through LLC layering, address farms, NPI changes, and ownership swaps, is poorly handled by every existing vendor including the incumbents. Explainable AI infrastructure that produces an evidentiary record sufficient for False Claims Act litigation is a serious gap. Eval and observability tooling for production AI in regulated workflows is still being built. The NY Medicaid data error that the administration had to publicly acknowledge to the AP is exactly the kind of issue better eval infrastructure would have caught.

Counter-AI tooling on the provider side does not really exist as a category yet. If CMS deploys AI-assisted coding review at scale, providers will need software that simulates the government model’s risk scoring, identifies likely flagged claims before submission, and produces appeal-ready documentation. Today that work is handled by humans inside RCM teams and consultants. It will be productized within 24 months.

Privacy-preserving computation for cross-state Medicaid data sharing is another wide-open gap. Confidential computing, federated learning, and synthetic data all get tossed around in CMS conference panels, but none of them is in production at scale on Medicaid data flows. The technical and political problems are both nontrivial.

Payment integrity vendors, the incumbents, and the new provider audit defense category

The healthcare payment integrity market hit roughly $15.1B in 2025 and is forecast to push to $28B by 2030 at a 13.1% CAGR per Mordor. Claim error rates have stuck stubbornly around 6.26%, which is the part of the iceberg every CRUSH-style announcement is trying to crack. Cloud-based deployment now processes more than 60% of claims, retiring something on the order of $20B in legacy mainframe admin expense. The competitive landscape is concentrated at the top and fragmented at the bottom.

On the gov side, Cotiviti GOV is the heavyweight, with the new RAC Region 3, 4, and 5 awards reinforcing its position. Performant and Qlarant cover UPIC and MEDIC zones. Gainwell, since absorbing HMS, controls major state Medicaid TPL and credit balance work plus MMIS implementations in many states. NTT Data, EXL/SCIO, and SAS Institute hold longstanding analytics contracts. Booz Allen, Leidos, and GDIT play the integration and systems-engineering role across all of this.

On the commercial side, Cotiviti is also the leader, with Optum FWA right behind. Codoxo has carved out a real niche with Explainable AI branding in Medicaid and commercial plans. ClarisHealth, Healthcare Fraud Shield, Shift Technology, Lyric, Zelis, HealthEdge, Multiplan, and Sagility round out the next tier. Most of these vendors are some combination of private equity owned (Cotiviti at Veritas, EXL public), inside UnitedHealth, or otherwise spoken for. The greenfield is not in commercial payer payment integrity. That boat sailed when private equity rolled up the category between 2018 and 2023.

The actual greenfield is provider-side audit defense. As of mid-2026, no clear category leader exists. Several players are circling: Iodine Software on CDI and utilization, CodaMetrix on autonomous coding, Janus Health on revenue cycle workflow, Reveleer on risk adjustment, RhythmX AI on payer-provider claims dispute. None of them is yet built around the specific use case of “CMS or a commercial payer has flagged your claim, what do you do next.”

That category will get built. The Pieces Technologies Texas AG settlement on hallucination rate disclosure already created the regulatory groundwork. The Lokken v. UHC discovery battle in Minnesota established that plaintiffs can subpoena AI model internals when contesting denials. Provider-side audit defense will be sold to hospitals, large physician groups, MA risk-bearing entities, and eventually to ACOs and direct contracting entities. The pricing model will probably mirror what Cotiviti and Codoxo do but inverted, so contingent fees on the dollar value of denials successfully appealed.

The investor angle, with three buckets of capital deployment

For anyone allocating capital to this thesis, three roughly orthogonal bets line up.

Bucket one is incumbents on both sides of the gov-commercial line. Cotiviti, Optum FWA, Gainwell, SAS, Codoxo. None of these is cheap, none is going to be a venture return, and most are PE-owned anyway so the trade is either secondary or waiting for an eventual IPO window. The right reason to hold exposure here is that the underlying market grew 13% CAGR and the public-policy tailwind just got materially stronger.

Bucket two is provider-side audit defense, which is the actually-venture-investible piece. Founders here need to be deeply technical, fluent in CPT and ICD coding, comfortable with both 837/835 transaction sets and FHIR US Core profiles, and willing to live inside the workflow pain of a hospital CFO’s audit response team. The category is unbranded, the buyers know they need something, and the incumbents on the payer side are about to weaponize AI in ways that create predictable enterprise demand. Expect three to five Series A rounds in this space over the next six months, possibly led by Bessemer, General Catalyst’s healthcare team, a16z bio, and the usual late-stage names sniffing earlier. The dark-horse buyers will be the EHR vendors, who could either build this in-house or acquire to bolt onto Epic Payer Platform and Oracle Health.

Bucket three is infrastructure underneath both sides. Eval and observability tooling for healthcare AI workflows. Hallucination detection on clinical and claims documents. Privacy-preserving computation for cross-state Medicaid data sharing. Synthetic data for model training where actual PHI cannot move. Identity resolution and provider continuity infrastructure. These are unsexy plumbing bets that almost nobody is funding in healthcare specifically, even though the horizontal AI infra market has been pouring money into the same primitives. Whoever puts a healthcare wrapper on Patronus, Arize, Trulens, or Galileo and gets HIPAA plus SOC2 plus FedRAMP done first is going to own a real piece of the next decade. None of the current generalist AI infra companies has prioritized health, which is the gap.

For asset allocators thinking about thesis-level exposure, the rough decomposition would be 40% incumbents (for the macro tailwind), 35% provider-side audit defense (for the venture upside), and 25% infra (for the optionality). Adjust per personal risk tolerance and conviction on regulatory tempo.

Selling to the feds as a health tech founder, a casual playbook

A common founder failure mode is to think federal procurement is “just enterprise sales with more paperwork.” That is wrong in roughly the way thinking running a marathon is just walking with more steps is wrong.

The actual playbook, in compressed form. Pick the vehicle before pitching anyone. The federal customer cannot buy from a company that is not on a contract vehicle they can use. For health AI work, the main ones are CMS SPARC, NITAAC CIO-SP4, GSA MAS, T4NG-2 at the VA, and SEWP V at NASA (which everybody uses because it is fast). Get on at least one. If that takes a year, fine, that is the cost of entry.

Sub to a prime first. Booz Allen, Leidos, GDIT, ICF, and Maximus all carry the past performance and the security clearances needed to win the actual award. Sub through one of them, deliver on a few task orders, build the past performance, then prime your own work on the next recompete. This is the canonical path and there is no shortcut.

FedRAMP authorization is non-optional for anything touching CMS or HHS data. The classic path is FedRAMP Moderate via an agency sponsor, which costs roughly $750K to $1.5M and takes 12 to 18 months. The new FedRAMP 20x reform is supposed to compress that, but as of late 2025 the new process is still being shaken out. NIST 800-53 controls, FISMA Moderate or High classification, and ATO (Authority to Operate) are all gates. The cheap workaround is StateRAMP for state Medicaid work, which is easier and lets a vendor build muscle before going federal.

Bring data, not slides. Federal customers respond to performance benchmarks, hallucination rates, false positive rates, sensitivity and specificity on representative datasets, and side-by-side comparisons against incumbents. A federal contracting officer who has been doing this for 15 years can smell a marketing deck from across the room. The Pieces Technologies Texas AG settlement, where the company allegedly overstated its hallucination rate, will be cited in every federal AI vendor due diligence package for the next three years. Founders should pre-empt that line of questioning by publishing their eval methodology.

Speak the language. Use phrases like task order, vehicle, ID/IQ, ATO, past performance, prime, sub, set-aside, GWAC, BPA, FAR Part 12, OTA, FedRAMP boundary, ConMon, and ATO inheritance. If those words do not roll off the tongue, hire someone for whom they do.

Pick the right entry point. CMS is the obvious target but also the slowest and most political. DHA (Defense Health Agency) and the VA are often faster and let a vendor build product muscle on real clinical data. NIH and CDC have smaller AI budgets but easier procurements. HRSA and SAMHSA care a lot about behavioral health and addiction services, which intersects directly with the single audit work HHS just announced. The Indian Health Service has the loosest procurement and the most operational pain.

Founders also need to understand the political tempo. The current administration’s stated priorities are fraud, identity verification, real-time scoring, and getting things shipped. The previous administration’s priorities were equity, bias auditing, and stakeholder process. Pitches that worked 18 months ago do not work now. Pitches that work now will need a different framing in 30 months. Do not assume any single policy stance will hold across cycles.

Closing skepticism, the legal exposure, and the next 18 months

Plenty of valid criticism exists. Critics including Public Citizen have pointed out that the administration’s anti-fraud efforts have disproportionately targeted Democratic states, sometimes with shoddy data work. The administration acknowledged to the AP that a major data error went into the NY Medicaid fraud investigation. Generative AI models hallucinate, and an audit finding generated by an LLM that cites a deficiency that does not exist is not just embarrassing, it is potentially actionable under APA arbitrary-and-capricious standards.

The litigation pipeline is loading up. The Pieces Technologies Texas AG settlement set the template for AI vendors getting sued over performance representations. Lokken v. UHC at the District of Minnesota is currently working through aggressive discovery on UnitedHealthcare’s AI claims tools and will produce precedent on what plaintiffs can demand from payer AI systems. The DOJ-HHS False Claims Act Working Group that re-launched in 2025 has signaled it will focus on AI-enabled billing and risk adjustment irregularities. Expect FCA actions specifically targeting providers and vendors who used AI tools that materially inflated reimbursements. Expect counter-suits from providers arguing that payer or government AI tools produced biased or erroneous denials. Expect at least one major FOIA fight over the training data and prompts used in the HHS single audit ingestion.

The next 18 months should produce, on the policy side, a CRUSH proposed rule probably in Q4 2026 or Q1 2027, a first round of CRUSH-related task orders against existing payment integrity vehicles, an OIG report critical of the May 21 announcement, and at least one Inspector General audit of the audit tool itself, which is the kind of recursive moment HHS specializes in. On the market side, expect Codoxo to push more aggressively into government work, Cotiviti to extend its RAC role, two to four provider-side audit defense Series A rounds, and probably one acquisition of a small AI startup by a payment integrity incumbent at a frothy multiple.

For builders, the real opportunity is not chasing the headline. The headline is just permission. The opportunity is in the inverse of what the government is doing: building tooling that helps providers, plans, and grantees produce, defend, and verify their own audit position before the government’s AI even sees it. That category has no clear leader. It is technically tractable. It has clear willingness to pay. It is sitting there waiting for the right founder to pick it up. The next 18 months will determine which two or three teams end up owning it.​​​​​​​​​​​​​​​​

Single audits from every entity spending over $1M in federal funds sit in the Federal Audit Clearinghouse. Most were never read. HHS is now reading them with ChatGPT and threatening funding cuts for chronic d…

Read more

Single audits from every entity spending over $1M in federal funds sit in the Federal Audit Clearinghouse. Most were never read. HHS is now reading them with ChatGPT and threatening funding cuts for chronic deficiencies.

Three months earlier, CMS dropped the CRUSH RFI: provider enrollment reform, lab fraud, DMEPOS moratorium, AI-assisted coding review, and a direct question about using AI in the insurance marketplaces. This is a coordinated policy signal, not a one-off.

The Oz line: CMS is padlocking the cookie jar and letting fraudsters starve. Pay and chase is out. Detect and deploy is in. Different vendors, different stack, different unit economics. The $15B payment integrity market just got a major policy tailwind.

Subscribe to www.onhealthcare.tech for free and paid articles, podcasts, and more.

🎧 Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Eli Lilly’s triple agonist retatrutide hits 28.3% mean weight loss in TRIUMPH-1 phase 3, blowing past tirzepatide benchmarks & rewriting obesity math for payers, PBMs & the Lilly Novo Duopoly

Eli Lilly’s retatrutide just posted 28.3% mean total body weight loss in TRIUMPH-1 phase 3. That’s ~70 lbs off a 248 lb baseline in 80 weeks. For context, tirzepatide peaked at ~22.5%. This is not a small gap…

Listen now

36 minutes ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

What TRIUMPH-1 actually delivered

Why glucagon agonism rewires the math

How retatrutide stacks up against tirzepatide and semaglutide

The 104 week extension and the bariatric surgery threshold question

Cardiometabolic signals beyond the scale

The 4 mg dose as a separate strategic asset

Payer and PBM implications

The LLY NVO duopoly and the oral pipeline question

Manufacturing, pricing, and the MFN policy overlay

What to watch from here

Topline data points from the May 21, 2026 readout:

• TRIUMPH-1 = phase 3 of retatrutide (GIP, GLP-1, glucagon triple agonist) in adults with obesity or overweight + ≥1 weight-related comorbidity, no T2D

• Baseline: 112.7 kg (248.5 lbs), BMI 40.0

• 80 wk primary endpoint, percent body weight change:

• 4 mg: -19.0% (-47.2 lbs)

• 9 mg: -25.9% (-64.4 lbs)

• 12 mg: -28.3% (-70.3 lbs)

• Placebo: -2.2% (-5.5 lbs)

• Categorical responders at 12 mg: 62.5% hit ≥25% TWL, 45.3% hit ≥30%, 27.2% hit ≥35%

• 65.3% of 12 mg arm crossed back under BMI 30 at wk 80; 37.5% of class 3 obesity entrants exited clinical obesity entirely

• Waist circumference reduction: -24.1 cm at 12 mg vs -3.6 cm placebo

• 104 wk extension (n=532, BMI ≥35 baseline 121.7 kg, BMI 42.8):

• 12 mg to MTD: -30.3% (-85.0 lbs)

• 9 mg to MTD: -29.5% (-80.7 lbs)

• 4 mg to MTD: -27.9% (-73.3 lbs)

• Placebo to MTD: -19.2% (-49.9 lbs)

• Cardiometabolic improvements vs placebo: waist circumference, non-HDL cholesterol, triglycerides, systolic BP, hsCRP

• 4 mg dose: AE-driven discontinuation rate lower than placebo

Strategic takeaways:

• First triple agonist past phase 3, ahead of every competing program

• TWL distribution closes gap with bariatric surgery (Roux-en-Y ~30%, sleeve ~25%)

• Pressure on bariatric procedure volumes, PBM utilization management regimes, ICER cost-effectiveness models

• LLY widens lead over NVO further; CagriSema and amycretin look pedestrian in comparison

• Open questions: pricing under MFN regime, Medicare Part D coverage path via outcomes trial, manufacturing capacity, orforglipron positioning in the same portfolio

What TRIUMPH-1 actually delivered

The trial put retatrutide up against placebo at three doses (4 mg, 9 mg, 12 mg) in adults carrying obesity or overweight plus at least one weight-related comorbidity, with type 2 diabetes pulled out of enrollment to keep the obesity question clean. Baseline cohort sat at 112.7 kg (248.5 lbs) with a mean BMI of 40.0, which is solidly class 3 territory and a much heavier starting point than the SURMOUNT-1 cohort that anchored tirzepatide’s label. At 80 weeks the 12 mg arm dropped 28.3 percent of body weight on average, which works out to 70.3 lbs off a 248.5 lb starting point. The 9 mg arm landed at 25.9 percent (64.4 lbs). The 4 mg arm, reached with a single escalation step, came in at 19.0 percent (47.2 lbs). Placebo did what placebo does in obesity trials, which is essentially nothing, posting 2.2 percent (5.5 lbs).

The categorical responder numbers are arguably more interesting than the mean. At the 12 mg dose, 62.5 percent of participants hit at least 25 percent weight reduction, 45.3 percent crossed 30 percent, and 27.2 percent crossed 35 percent. For reference, the 35 percent threshold has historically been the exclusive turf of bariatric surgery, and it was hit by basically no one on placebo (0.3 percent, which is statistical noise). The 65.3 percent figure for participants who crossed back under the BMI 30 threshold at week 80 is the line that the press release is going to get quoted on, because crossing out of clinical obesity is the kind of headline number that translates cleanly into payer reimbursement debates and clinical guideline updates. The 37.5 percent figure for class 3 obesity entrants who exited clinical obesity entirely is the more remarkable subset result, because those are the patients with the most metabolic burden and the most baseline weight to shed, and the historical pharmacological track record on that population has been close to nil.

Why glucagon agonism rewires the math

Read more

Why so much higher? Retatrutide is a triple agonist - GIP, GLP-1, and glucagon. That glucagon receptor activation raises resting energy expenditur…

Read more

Welcome to Healthcare Markets & Technology.

Rigorous analysis of AI, policy, capital, technology, and clinical operations across U.S. healthcare — written for the people who build, invest in, and lead it.

Free subscribers get 2 public articles per week. Upgrade to paid → for the full 7 articles/week, paid podcast episodes, deal breakdowns, and the complete 538-deep-dive archive.

Subscribe or upgrade here →

One thing to bookmark: the searchable Knowledge Base at kb.onhealthcare.tech isn’t in Substack’s menu. Save it now — on mobile, tap share → “Add to Home Screen.”

Reply to any email with questions. I read every one.

— Trey

Video Preview

🎧 Part I Podcast free on Spotify.

Thoughts on Healthcare Markets & Technology

Part I: Forus Just Raised $160M at a $1B Valuation to Become the Operational Routing Layer for Specialty Rx & the Real Story Is Why Investors Now Believe Healthcare Orchestration Beats SaaS

Forus raised $160M at a $1B valuation on ~$50M in annualized revenue. That is a 20x multiple at Series B. Here is why Thrive and General Catalyst wrote that check…

Listen now

a day ago · Thoughts on Healthcare

🎧 Part II Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Part II: Forus Just Raised $160M at a $1B Valuation to Become the Operational Routing Layer for Specialty Rx & the Real Story Is Why Investors Now Believe Healthcare Orchestration Beats SaaS

Forus raised $160M at a $1B valuation on ~$50M in annualized revenue. That is a 20x multiple at Series B. Here is why Thrive and General Catalyst wrote that check…

Listen now

a day ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Abstract

• Healthcare is structurally a transaction routing economy, not a care delivery industry, and the most valuable real estate sits at the seams between stakeholders

• Forus raised over $160M ($123M of it as Series B per AlleyWatch) led by Thrive Capital and General Catalyst at a $1B valuation on roughly $50M of expected annualized revenue this year

• The thesis being underwritten is operational and infrastructural, not a workflow software bet

• Specialty drug economics changed the math by making every coordination failure expensive enough to justify rebuilding the plumbing

• AI collapses the labor cost of bridging fragmentation, which is the historical reason orchestration was never commercially viable at scale

• Traditional digital health SaaS keeps plateauing because seat licenses cannot capture transaction value and because healthcare workflows fragment continuously rather than standardize

• Forus monetizes biopharma manufacturers downstream while giving the product away free to providers and patients, which is the same business model as Visa and Google

• The incumbent stack of PBMs, hubs, specialty pharmacies, and EHR vendors is going to fight this category hard

• The orchestration layer compounds on multi sided network effects rather than feature parity

• The next decade of healthcare infrastructure outcomes will probably look more like Stripe than like Veeve

Table of Contents

1. Why a Series B Round Is Actually a Market Signal

2. The Coordination Economy Hiding Inside Healthcare

3. How Specialty Drugs Quietly Rewired the Admin Layer

4. Why Most Digital Health SaaS Plateaued

5. What Forus Actually Built and Why It Works

6. The Math on Free Software That Still Generates Real Revenue

7. Why AI Is the Cheat Code for Fragmentation

8. The Looming Control War Between PBMs Hubs and Orchestrators

9. What Investors Are Really Underwriting

10. Final Thoughts on the Routing Layer Thesis

Why a Series B Round Is Actually a Market Signal

On May twelfth, a New York company most healthcare operators had not heard of two weeks earlier announced it had pulled together over $160 million in equity financing at a $1 billion valuation. The company is Forus, previously branded as Tandem, founded in 2023 by a thirty one year old named Sahir Jaggi who used to be a partner at Rough Draft Ventures and earned a biomedical research degree at Columbia. The round was led by Thrive Capital and General Catalyst, with Accel, Bain Capital Ventures, Redpoint, BoxGroup, and Pear VC alongside. Roughly $123 million of that came in as the new Series B per AlleyWatch’s reporting, and the company is expecting to clear $50 million in annualized revenue this year.

On its face this looks like another late cycle digital health raise, of which there have been plenty in the last eighteen months, most of them now quietly being forgotten by the same LPs who got pitched on them. What makes this one different is not the headline number, not the cap table, and not the now mandatory “AI powered” descriptor in the press release. It is the underlying thesis, which is operational rather than technological. The investors did not write a check because they believe in another point solution that automates prior authorization or another patient access platform built on a workflow tool. They wrote a check because they believe the bottleneck in American healthcare is no longer storage, integration, or analytics. The bottleneck is transaction coordination, and whoever owns the orchestration layer in the next decade is going to own something closer to Visa than to Veeva.

That last part deserves to be stated plainly. The market has spent fifteen years assuming healthcare looks like enterprise software, where the winners are the companies with the cleanest interfaces and the deepest workflow penetration in a single user persona. What is actually happening, and what this round reflects, is that the most valuable real estate in healthcare is the routing layer between stakeholders, not the application layer sitting on top of any one of them. That is a very different game with a very different power law, and the people writing the checks at this stage are starting to act like they finally figured that out.

The Coordination Economy Hiding Inside Healthcare

Most outsiders think healthcare is a care delivery industry. It is not. Healthcare is, mechanically, a transaction routing economy that occasionally performs medicine on the side. The reason this distinction never quite lands is that the routing is invisible to the patient and largely invisible to the physician, but it is doing essentially all of the real work between the moment a clinical decision gets made and the moment a therapy reaches the body.

Every referral is a transaction. Every prior authorization is a transaction. Every benefits verification is a transaction. Every formulary check, every specialty pharmacy assignment, every copay assistance enrollment, every reimbursement determination, every credentialing verification, every step therapy override, every appeal, every shipment, every refill authorization, every adherence outreach, every patient assistance program eligibility recalculation. These are routine operational events that happen by the tens of millions every week, and the entire economy of intermediaries in healthcare was built to handle the friction at the seams between them.

PBMs exist because the system is fragmented. Clearinghouses exist because the system is fragmented. Revenue cycle vendors exist because the system is fragmented. Specialty pharmacies exist because the system is fragmented. Provider data companies exist because the system is fragmented. Hub services companies exist because the system is fragmented. Prior authorization vendors exist because the system is fragmented. If you collapsed the fragmentation, you would erase trillions of dollars of intermediary revenue in the process, which is also why no incumbent has any economic incentive to collapse it.

The companies that have historically gotten genuinely large in healthcare technology did not do it by building the prettiest dashboards. They did it by inserting themselves into transaction flow and monetizing volume. CoverMyMeds got bought by McKesson for roughly $1.4 billion in 2017 because it had become the de facto rails for electronic prior authorization, not because it had a beautiful front end. Surescripts is a network, not a product. Change Healthcare was, fundamentally, a clearinghouse before UnitedHealth swallowed it for around $13 billion. The pattern is the same in every category that has ever produced an outlier outcome in this industry. Software seats are a tax. Transaction control is a toll. The difference compounds.

How Specialty Drugs Quietly Rewired the Admin Layer

The reason this is finally hitting an inflection point in 2026 instead of in 2010 has almost everything to do with what happened to the drug pipeline over the past decade. When most therapies cost a few hundred dollars a month, administrative friction was a nuisance but it was survivable. The system tolerated phone calls and faxes and three day turnarounds because the dollars at stake on any individual prescription were not large enough to justify rebuilding the plumbing. That tolerance ended the moment specialty became the dominant share of pharmacy spend.

A modern specialty therapy can cost anywhere from sixty thousand dollars a year to three million dollars one time across the course of treatment. Cell and gene therapies, biologics for inflammatory and rare disease, oncology infusions, GLP ones in their high cost indications, and the wave of pipeline assets behind them all sit in this band. When the average prescription on a patient population is that expensive, every operational failure carries an enormous shadow cost. A prior authorization denied for a missing piece of clinical documentation can delay therapy by weeks and cost the manufacturer a fully written script. A benefits verification routed to the wrong specialty pharmacy can blow up patient onboarding entirely. A copay enrollment that fails because the hub did not communicate cleanly with the foundation can collapse adherence in the first ninety days, which is exactly the window where specialty persistence is most fragile.

What changed is that the transaction layer itself became economically valuable in a way it simply was not when most prescriptions were generic statins and ace inhibitors. Manufacturers will now pay almost any reasonable price for a service that reliably shortens time to therapy by a week, because the net present value of an additional patient on a sixty thousand dollar a year biologic dwarfs the per patient cost of the service. Providers will adopt almost any tool that reduces the staff hours burned per prescription, because they are losing money on every minute their nurses and pharmacists spend on hold with payers. Payers quietly tolerate anything that drives better clinical documentation upstream because their own utilization management economics depend on it. The entire stakeholder set is finally aligned in a way it was not for the previous twenty years, and that alignment is what makes a true orchestration layer commercially possible for the first time.

Why Most Digital Health SaaS Plateaued

The graveyard of digital health is full of beautifully designed products that hit a wall somewhere between thirty and seventy million in annual recurring revenue and then never broke through. The companies kept telling their boards the next big customer was around the corner, the boards kept telling their LPs the same story, and the customers kept slow rolling the procurement until eventually the cap table got pushed underwater by a flat round and the founders went to go do something else.

The reason this happened so consistently is structural. Traditional enterprise SaaS scales because workflows standardize over time, which lets one product address more and more buyers with the same code base. Healthcare workflows do the opposite. They fragment continuously because reimbursement methodologies change, payer rules change, formulary positions change, clinical pathways change, network configurations change, benefit designs change, regulatory requirements change, and specialty drug economics evolve faster than software roadmaps can possibly track. Every standard you ship is obsolete within twelve months because the ground has moved underneath it.

The companies that survived this dynamic almost always did it by abandoning the seat license model. They moved to transaction based pricing, hybrid models, capitated arrangements, percentage of spend, or revenue share with downstream stakeholders. The companies that stayed pure SaaS struggled because the buyer kept rebudgeting them as discretionary spend even when the product was beloved by end users. Health systems are not software companies. They do not allocate budget like software companies. They allocate budget the way operations heavy enterprises do, which means anything that does not show up as either revenue or cost takeout eventually loses funding when finance gets squeezed.

The other thing the SaaS playbook never solved is the network problem. A patient access platform is only useful if the manufacturer and the provider and the pharmacy and the hub are all reachable through the same connection. If the platform only sits with the provider, the manufacturer still has to maintain its own hub. If it only sits with the manufacturer, the provider still has to do everything manually. The economics work only if you control the connections on multiple sides at once, which is to say if you are running a network rather than a product. Networks are much harder to build, take much longer to compound, and require a category of patience that public market software investors have basically run out of. That patience now sits almost entirely with crossover and growth equity firms who are willing to underwrite ten year compounding stories at infrastructure economics, which is exactly the firm profile leading this Forus round.

What Forus Actually Built and Why It Works

Forus is, structurally, an orchestration network embedded directly inside the physician workflow at the point of prescribing. The moment a clinician writes a prescription, the system picks it up and handles essentially everything that has to happen between that click and the patient actually starting therapy. It checks medication history, identifies pharmacy restrictions, manages prior authorization documentation, routes the prescription to the right fulfillment pathway, and gives both the prescriber and the patient real time visibility into where the process is stuck. The reported result, per the company’s own materials and external coverage, is that approvals that historically took months are now closing in two to three days, and median time to therapy initiation has compressed from over a week to roughly one day.

The product is given to providers and patients for free. The revenue model is downstream, captured through relationships with biopharma manufacturers, who pay for the orchestration because they get the thing they want most in the world, which is patients on therapy faster and more reliably. Five of the top ten global biopharma companies are reportedly already commercially engaged, and the company expects to clear $50 million in annualized revenue this year on what is functionally a zero priced product to the demand side of the market. That structure is the actual unlock. By giving the product away to the side of the equation that has the budget squeeze, Forus solves the historical adoption problem that crushed every prior authorization SaaS company. By monetizing the side of the equation that experiences the operational ROI most directly, it has a willingness to pay curve that scales with the value of the prescription rather than with the number of users.

The adoption signal is the part that should make competitors uncomfortable. Provider penetration grew ten times year over year for the past two years and the company now claims coverage of roughly eighty percent of US residential zip codes, with thousands of medical practices and health systems across all fifty states actively using the platform. That growth has reportedly been driven almost entirely by word of mouth, which is the single hardest go to market metric to fake in healthcare. A health system office manager telling her counterpart at another system that something actually works is, empirically, the only marketing channel in this industry that consistently converts. The fact that this growth happened under the previous Tandem brand without a particularly aggressive PR posture is itself a tell about how much operational ROI the product is delivering on the ground.

The Math on Free Software That Still Generates Real Revenue

The economic structure of the business is worth sitting with for a second, because it explains a lot about why these particular investors moved at this particular valuation. A product priced at zero to the user but monetized through transaction adjacent revenue from a different stakeholder is the same basic business model that built Google, Visa, and most of modern advertising infrastructure. The thing that makes it work in any of these categories is that the side of the market paying the bills cares dramatically more about volume and quality of transactions than about the unit price of the underlying interaction. A pharmaceutical brand spending billions a year on field force, hub services, and patient support programs is not going to flinch at paying a meaningful fee per script started if the alternative is losing patients to abandonment.

To put numbers on it, consider what a typical specialty hub services contract looks like today. Manufacturers commonly spend somewhere in the range of one thousand to three thousand dollars per patient enrolled on bespoke hub operations, often more for therapies with complex enrollment requirements. They will spend that money on call center staffing, benefits investigation, prior authorization support, copay administration, and adherence follow up, and they will spend it even if the patient never actually starts therapy, because the unit economics of the brand still work out when persistence is high enough. If an orchestration network can deliver equivalent or better outcomes at meaningfully lower cost per enrolled patient while increasing speed to therapy and conversion, the manufacturer is buying a better outcome at the same or lower price. The orchestration company is capturing a fraction of what was already being spent, but doing it across many manufacturers at once with a software cost structure rather than a labor cost structure.

The reason this is so attractive at $50 million in annualized revenue with a billion dollar valuation is that the unit economics get dramatically better as scale builds. Every new manufacturer relationship increases the value of the network to existing providers because more therapies can be routed through the same pipes. Every new provider adoption increases the value of the network to existing manufacturers because more potential prescribers are reachable through the same connection. Every transaction generates routing intelligence that improves every subsequent transaction. The compounding is, in the technical sense, multi sided, and multi sided networks tend to either lose or win enormously. There is rarely a middle outcome.

Why AI Is the Cheat Code for Fragmentation

The reason this approach was not commercially viable a decade ago is that the operational layer in healthcare has always required a brutal amount of human labor to bridge the gaps between systems that cannot speak to each other. Benefits verification teams sit on hold with insurance companies. Prior authorization coordinators ferry clinical documentation between EHRs and payer portals. Hub case managers manage spreadsheets and faxes and PDF intake. Specialty pharmacy liaisons chase missing information across multiple systems. Patient access teams reconcile mismatched data across formularies and copay programs. The whole apparatus runs on a labor force whose existence is essentially a tax that the rest of the system pays to compensate for the fact that the underlying data and decision pathways are not connected.

What has changed is that large language models are unusually well suited to exactly this kind of work. They are good at reading unstructured clinical documentation. They are good at parsing poorly designed payer portals and faxed forms. They are good at extracting structured fields from ugly inputs. They are good at routing decisions where the rules are nominally written down but practically applied with a lot of edge cases. They are good at communicating in semi formal language with humans on the other side of a phone or an email. Almost every job that exists today in the healthcare administrative layer is, in some real sense, a job that a properly orchestrated AI system can do faster, more cheaply, and at higher consistency than a human can.

The thing to be careful about is that automating healthcare administration is not actually a model problem. The model is the easy part. The hard part is the orchestration around the model, the integrations into payer portals and EHRs and hub systems, the regulatory and compliance overlay, the human in the loop logic for edge cases, the change management with provider offices, and the actual operational rigor required to run a system that touches care delivery. Any reasonably capable foundation model can pass a prior authorization criteria check in isolation. Only a company with several years of integration depth and operational learnings can actually orchestrate the end to end workflow at scale. That moat is built operationally, not algorithmically, which is why investors with deep healthcare experience are placing bets on companies that have been quietly building in this space for years rather than on the freshly minted “we are an AI agent for prior auth” startups that have been pitching seed rounds for the past eighteen months.

The Looming Control War Between PBMs Hubs and Orchestrators

The category Forus and a handful of similar companies are building toward sits directly on top of territory that existing intermediaries currently consider theirs. PBMs control formulary and routing logic. Specialty pharmacies control fulfillment. Hub services vendors control patient enrollment and adherence. EHR vendors control the point of prescribing. Manufacturers control brand strategy and patient support program economics. Each of those stakeholders has historically built proprietary infrastructure to handle the slice of the workflow they touch, and each of them has an interest in not letting a neutral third party become the connective tissue across all of them.

This is going to get ugly before it gets stable. PBMs are not going to enjoy having an external orchestration layer that can see across formulary decisions and route around restrictive utilization management when clinical evidence justifies it. Specialty pharmacies are not going to enjoy losing some of their selection power to algorithmic routing based on patient geography, network status, and turnaround time. EHR vendors are going to want to build or buy the orchestration layer themselves rather than cede the in workflow real estate to a third party. Hub services vendors are going to feel directly threatened, because they are essentially the manual version of what Forus is trying to automate, and they know it.

The dynamic that often gets missed in conversations about this category is that the incumbents have most of the data but very little of the workflow leverage at the point of prescribing. The EHR has the workflow but not the cross stakeholder routing logic. The PBM has the formulary but not the prescriber relationship. The hub has the patient relationship but lives in a parallel universe from clinical care. The specialty pharmacy has the fulfillment but is downstream of every meaningful decision. An orchestration company that is embedded at the moment of prescribing, with relationships across manufacturers and pharmacies and payers, is structurally positioned to mediate among the rest of them, which is exactly the position that lets a Visa style intermediary capture a small percentage of an enormous transaction flow.

The category that loses most directly in this future is the manual hub services business. There is a real possibility that the field collapses meaningfully over the next five years as the orchestration layer absorbs the workflows that previously required outsourced staffing. The same is true of significant pieces of the patient access and prior authorization point solution market, where many of the companies that raised growth rounds in the 2021 boom are now stuck with products that do part of what an orchestration network does for free as a feature. Some of those companies will get acquired into the orchestration stack. Some will get rolled up by hubs trying to defend their turf. Most will quietly run out of runway over the next two to three years as the category consolidates around a small number of network winners.

What Investors Are Really Underwriting

The cap table tells you most of what you need to know about what is actually being underwritten here. Thrive and General Catalyst do not usually lead late stage rounds in companies whose ambition is to be a workflow vendor. Their checks scale with their conviction in network effects and infrastructure economics. Accel and Bain Capital Ventures have spent the past two decades watching network businesses outperform application businesses on every meaningful long term metric. Redpoint, BoxGroup, and Pear were earlier participants and have been on the inside watching the operational metrics develop quarter by quarter. None of these firms are paying a billion dollar valuation on $50 million in annualized revenue because they think Forus is a really good prior authorization product. They are paying it because they think Forus is becoming the routing layer for specialty prescriptions, and if that thesis plays out the multiple on the current revenue is going to look comically cheap in retrospect.

The comparison set is also worth dwelling on. Most of the digital health companies that went public in the last cycle traded at one to four times revenue inside two years of listing because the market correctly identified that they were workflow point solutions rather than infrastructure plays. Twenty times forward revenue at the Series B is a price that investors only pay when they believe the company is going to compound at infrastructure economics over a decade or longer. The right reference set is not other patient access companies. It is the early stage valuations of companies like Stripe and Plaid, which similarly looked overpriced on near term revenue when they raised growth rounds and which similarly turned out to be priced correctly on the basis of routing layer optionality.

The risk in this thesis is not that the operational opportunity is wrong. The opportunity is obviously real and the math on automating administrative friction in specialty therapy is uncontroversial. The risk is execution against an incumbent stack that is going to fight back, sometimes with regulation, sometimes with exclusivity arrangements, sometimes with parallel internal builds, and sometimes simply with the inertia of healthcare contracting. Whoever wins the orchestration category is going to need to be diplomatically excellent with PBMs and pharmacies and EHRs while simultaneously eating their lunch operationally, which is not a particularly easy combination of qualities to find in a single management team. The early signals from the Forus go to market suggest the team understands this, but it is still very early.

Final Thoughts on the Routing Layer Thesis

The broader thing to take away from this round, regardless of what one thinks of Forus specifically, is that the center of gravity in healthcare infrastructure investing has clearly moved. For the past decade, the most ambitious money was chasing data plays, analytics plays, interoperability plays, and clinical AI. Those categories are not going away, but the next decade of giant healthcare technology outcomes is going to be defined by who controls the operational layer that moves prescriptions and authorizations and patient flows between fragmented stakeholders. That control is more strategically valuable than any individual application or dataset, because it sits on top of every meaningful transaction in the system.

The competitive set is going to expand quickly. Lumini, Qventus, Confido Health, and a handful of others are already pursuing variations of this thesis from different angles, and the AI native cohort raising seed rounds today is going to produce a few additional credible entrants over the next twelve to eighteen months. Some of the incumbents will respond with acquisitions, some with internal builds, some with strategic partnerships, and some with the kind of contractual brick walls that PBMs have historically deployed to slow down disintermediation. The market will sort itself out faster than most participants are expecting, because the underlying labor cost arbitrage on AI driven automation is so large that even mediocre execution can win significant share before the incumbents fully wake up.

For operators inside hub services, prior authorization vendors, patient access platforms, and revenue cycle companies, the strategic question is no longer whether the orchestration layer is going to consolidate workflows that currently live across multiple vendors. It is when, and what your moat looks like once the orchestration layer is in place. For investors, the question is whether the orchestration thesis is now too consensus to underwrite at attractive entry valuations, or whether there are still adjacent categories where the same dynamics are about to play out at earlier stages. Medical benefit drugs, behavioral health, durable medical equipment, complex referrals, and any other workflow where multiple fragmented stakeholders coordinate around high cost decisions are all candidates, and the alpha will go to investors who can identify the next category before the multiples reprice.

For everyone else, the simplest summary is that healthcare’s most important infrastructure companies of the next decade probably will not look like the most important infrastructure companies of the last one. They will sit further down the operational stack, they will monetize transactions rather than seats, they will be hated by some incumbents and loved by others, and they will compound on network effects rather than feature parity. Forus is the first of these to clear the visibility threshold of a billion dollar valuation with a credible biopharma customer roster and a real product in market. It will not be the last, but the fact that it exists at all is a useful signal that the market has finally figured out where the money actually is.​​​​​​​​​​​​​​​​

Healthcare is a transaction routing economy, not a care delivery industry. Every prior auth, every benefits check, every specialty pharmacy assignment is a transaction. The intermediary stack …

Read more

Healthcare is a transaction routing economy, not a care delivery industry. Every prior auth, every benefits check, every specialty pharmacy assignment is a transaction. The intermediary stack exists entirely because of that fragmentation.

Specialty drugs changed the math. A therapy at $60K-$3M per patient makes every coordination failure expensive. Manufacturers now pay real money for faster time to therapy. The transaction layer became economically valuable for the first time.

Forus gives the product free to providers and patients. Revenue comes from biopharma manufacturers downstream. Provider adoption grew 10x year over year for two years, reportedly through word of mouth. That is the structural unlock.

Subscribe to www.onhealthcare.tech for free and paid articles, podcasts, and more.

Welcome to Healthcare Markets & Technology.

Rigorous analysis of AI, policy, capital, technology, and clinical operations across U.S. healthcare — written for the people who build, invest in, and lead it.

Free subscribers get 2 public articles per week. Upgrade to paid → for the full 7 articles/week, paid podcast episodes, deal breakdowns, and the complete 538-deep-dive archive.

Subscribe or upgrade here →

One thing to bookmark: the searchable Knowledge Base at kb.onhealthcare.tech isn’t in Substack’s menu. Save it now — on mobile, tap share → “Add to Home Screen.”

Reply to any email with questions. I read every one.

— Trey

Video Preview

🎧 Podcast episode for paid subscribers only.

Thoughts on Healthcare Markets & Technology

CMS-2449-P recasts Medicaid State Directed Payments & FFS targeted practitioner supplements @ the Medicare benchmark, kills the ACR ceiling, bans uniform increases & projects $774.8B in 10yr savings

CMS just proposed a rule that could cut Medicaid spending by $774.8B over ten years. It targets State Directed Payments, a financing mechanism most people outside Medicaid policy have never heard of. Here is why it matters…

Listen now

2 days ago · Thoughts on Healthcare

Table of Contents

1. The setup, in plain English

2. How SDPs got to $144B before anyone noticed

3. What the proposed rule actually does

4. Grandfathering, the eligibility maze, and the ten percentage point glide path

5. The death of the uniform increase SDP

6. Grey area payments, third party fees, and the provider class problem

7. The FFS side at 447.381, which is almost the same rule with different plumbing

8. The dollar impact, plus the relative price assumptions that drive it

9. Who actually loses, because the gross numbers are not the net numbers

10. Open comment questions worth flagging

• Rule: CMS-2449-P, scheduled for Federal Register publication 5/22/2026, 60 day comment period

• Statutory basis: Section 71116 of the Working Families Tax Cut legislation (Pub. L. 119-21, enacted 7/4/2025), plus the 6/6/2025 Presidential Memorandum on waste, fraud, and abuse in Medicaid

• Two regulatory pieces: managed care SDPs at 42 CFR 438.6, and a new FFS targeted practitioner payment limit at 42 CFR 447.381

• Old SDP ceiling under the 2024 final rule: 100% of the Average Commercial Rate (ACR) for the four statutory services (inpatient hospital, outpatient hospital, nursing facility, qualified practitioner services at AMCs)

• New ceiling under WFTC: 100% of total published Medicare for Expansion States, 110% Medicare for Non-Expansion States, 100% State plan rate when no Medicare equivalent exists

• Applied per service or per discharge, not as an aggregate UPL

• CMS goes further than the statute and extends the same Medicare-based limit to all SDPs in all states (including Territories) and all services, applicable with first rating period on or after 1/1/2029

• Grandfathered SDPs: limited to completed preprints submitted before 7/4/2025 for rating periods within 180 business days of enactment; phase down at 10 percentage points of the grandfathered total dollar amount per year starting 1/1/2028

• Uniform increase SDPs eliminated for new and renewal SDPs starting 1/1/2028, except inside the grandfathering window

• Confirms grey area payments impermissible; bans SDP funds from being routed through provider associations or consultants

• Parallel FFS rule: 100%/110% Medicare for targeted practitioner payments, applied at the provider-specific level, with exceptions for cost-reconciled payments and services without a Medicare equivalent; transition deadline 1/1/2029

• Projected ten-year impact (medium scenario, 2026 to 2035): -$774.8B total computable, -$510.1B Federal, -$264.4B State, plus another -$2.44B from the FFS provisions

• Low and high scenarios on the SDP piece run from -$408.4B to -$989.7B over the same window

• Current SDP spending: $97.8B in FY 2024, projected to hit $295.9B by FY 2034 absent the rule

• 80.8% of SDPs above Medicare today are funded in whole or part by intergovernmental transfers (IGTs) or provider taxes

The setup, in plain English

For roughly a decade the SDP has been the dominant fiscal lever in Medicaid managed care. Two states submitted four SDP preprints in 2016, the year the framework was born. By 2024 that number was 41 states and 366 preprints. Cumulative submissions since inception now exceed 1,950. The 2024 final rule under the Biden administration tried to draw a line by codifying a ceiling at 100% of the ACR for four big service buckets, plus various transparency and quality-eval requirements. Roughly 18 months later, the line is being redrawn.

Two things changed. First, the Trump White House issued a presidential memo on 6/6/2025 titled Eliminating Waste, Fraud, and Abuse in Medicaid, which instructed HHS to ensure Medicaid payment rates do not exceed Medicare, to the extent permitted by law. Second, Congress wrote that instruction into statute. Section 71116 of the Working Families Tax Cut, signed 7/4/2025, directs CMS to revise 42 CFR 438.6(c)(2)(iii) to cap the total payment rate under SDPs for inpatient hospital, outpatient hospital, nursing facility, and qualified practitioner services at AMCs. The cap is 100% of the total published Medicare rate for Expansion States, 110% for Non-Expansion States, and the State plan rate when there is no Medicare comparator. Section 71116(b) carves out a grandfathering window with a phase down beginning with the first rating period on or after 1/1/2028.

CMS-2449-P is the implementing rule. It does the statutory minimum, then goes further by extending the same Medicare-based limit to all SDPs, all services, all states including the Territories, and to parallel FFS targeted practitioner payments under a brand new 42 CFR 447.381. The proposed effective date for the broader expansion is the first rating period on or after 1/1/2029, which roughly aligns the managed care timeline with the FFS transition deadline.

How SDPs got to $144B before anyone noticed

Read more

SDPs grew from 4 preprints across 2 states in 2016 to 366 preprints across 41 states by 2024. Total spending: $97.8B in FY2024. CMS …

Read more

Welcome to Healthcare Markets & Technology.

Rigorous analysis of AI, policy, capital, technology, and clinical operations across U.S. healthcare — written for the people who build, invest in, and lead it.

Free subscribers get 2 public articles per week. Upgrade to paid → for the full 7 articles/week, paid podcast episodes, deal breakdowns, and the complete 538-deep-dive archive.

Subscribe or upgrade here →

One thing to bookmark: the searchable Knowledge Base at kb.onhealthcare.tech isn’t in Substack’s menu. Save it now — on mobile, tap share → “Add to Home Screen.”

Reply to any email with questions. I read every one.

— Trey

Video Preview

Podcast, Part I (Free)

Thoughts on Healthcare Markets & Technology

Part I: RN, NP, and PA Pay by State Adjusted for Cost of Living Using BLS May 2024 Data: Why CA Headline Crown Is a Mirage, Why OK and IN Quietly Win, and What It Means for Workforce Strategy in 2025

California pays RNs $148K nominally. After cost-of-living adjustment it drops to third. Oregon wins. Minnesota is second. Hawaii, nominally second, falls dead last…

Listen now

3 days ago · Thoughts on Healthcare

Podcast, Part II (Paid)

Thoughts on Healthcare Markets & Technology

Part II: RN, NP, and PA Pay by State Adjusted for Cost of Living Using BLS May 2024 Data: Why CA Headline Crown Is a Mirage, Why OK and IN Quietly Win, and What It Means for Workforce Strategy in 2025

California pays RNs $148K nominally. After cost-of-living adjustment it drops to third. Oregon wins. Minnesota is second. Hawaii, nominally second, falls dead last…

Listen now

3 days ago · Thoughts on Healthcare

Quick read on what is in here.

• Top adjusted RN hourly wage: OR at $51.71. Bottom: HI at $31.82.

• Top adjusted NP hourly wage: OK at $71.29. Bottom: HI at $34.73.

• Top adjusted PA hourly wage: IN at $76.61. Bottom: HI at $36.78.

• CA wins every nominal race and loses every cost-adjusted race against the Midwest and Plains.

• Hawaii nominal pay looks fine. Hawaii adjusted pay looks like a punishment.

• RN-to-NP nominal spread runs roughly $25K to $50K depending on state. NP-to-PA spread is small or negative in a surprising number of states.

• Implications cover bedside staffing economics, the geographic skew of virtual care platforms, capitated value-based care unit costs, SNF and home health margin compression, the build-versus-buy math for travel staffing, and where venture-backed clinical models can actually scale.

Table of Contents

1. Why headline nurse and APP pay numbers are mostly noise

2. RN pay across the fifty states

3. NP pay across the fifty states

4. PA pay across the fifty states

5. Patterns that cross all three roles

6. The methodology stuff most people skip past

7. What this means for workforce strategy and digital health unit economics

8. Bottom line for operators, investors, and policy folks

Why headline nurse and APP pay numbers are mostly noise

Open any nursing trade publication on any given week and the same headline shows up. California pays nurses the most. The number is $148,330 for the average RN, climbing every year, fueled by union strength, mandatory staffing ratios, and a labor market where Kaiser pays new grads more than half the country pays mid-career nurses. The number is real. It is also, on its own, useless for anybody trying to make a workforce or capital allocation decision. A $148,330 RN paycheck in San Jose buys a different life than an $80,000 paycheck in Tupelo, and the difference is not a rounding error. Cost of living indices vary by close to 110 percentage points between the cheapest and most expensive states in the BLS dataset, with West Virginia clocking in at a COL index of 84.1 and Hawaii sitting at a frankly comical 186.9 according to the 2024 World Population Review composite. Apply those multipliers and California stops winning. Hawaii stops winning by a country mile. And a bunch of states nobody puts on a recruiting brochure end up at the top of the actual purchasing-power leaderboard.

This matters way past the personal-finance question of where an individual RN should take a job. Health system CFOs trying to model the unit cost of an FTE across a multi-state footprint, payers building NP and PA capacity for Medicare Advantage in-home assessment programs, digital health platforms running an asynchronous primary care model across all 50 states, value-based care enablers staffing their wrap-around clinical teams, and venture investors trying to back the next telepsych or virtual urgent care company all need the same thing. They need the real labor cost picture, not the nominal one. The BLS May 2026 OEWS release, combined with the World Population Review 2025 COL index that Becker’s used for its three role-specific tables published in April 2026, is the cleanest publicly available cut of that data. Becker’s did the cost-of-living math on hourly wages. What follows is the analysis layer on top.

RN pay across the fifty states

The nominal RN pay map looks like a coastal monopoly. California sits at $71.31 per hour, an average annual figure of $148,330. Hawaii is next at $59.48 and $123,720. Oregon at $57.92 and $120,470. Massachusetts at $54.14 and $112,610. Alaska at $53.86 and $112,040. New York at $53.12 and $110,490. DC at $52.52 and $109,240. New Jersey at $51.44 and $106,990. Connecticut at $49.84 and $103,670. Nevada at $49.17 and $102,280. The pattern is exactly what anybody who has spent five minutes in healthcare workforce planning would predict. High-density, high-cost coastal markets dominate, with Alaska and Hawaii thrown in because they always have a geographic isolation premium baked into their wage structure. The bottom of the list is South Dakota at $34.72 per hour ($72,210), followed by Alabama, Iowa, Arkansas, Kansas, Mississippi, Missouri, Tennessee, and a cluster of Plains and Southeast states. The nominal spread between the top and bottom state is more than $76,000 a year. That is a real number for an individual nurse considering where to put roots down, but a misleading one for everybody else.

Now adjust for cost of living. Oregon takes the top spot at $51.71 per hour, which annualizes to about $107,500 at 2,080 hours. Minnesota is second at $50.28 (about $104,600 annualized). California, after getting dragged down by a 144.8 COL index, sits third at $49.25. Nevada is fourth at $48.54. New Mexico fifth at $48.63. Washington fits in at $48.72. Georgia, of all places, surfaces at $48.42 adjusted. Oklahoma at $48.13. Michigan at $48.17. Texas at $47.55. The story flips. The states people associate with high-paying nursing are mostly still in the top half, but they are no longer dominant, and a band of Midwest and Southern states quietly outperform them on real take-home purchasing power. The bottom of the adjusted RN list is brutal. Hawaii at $31.82 adjusted (giving up roughly 47 percent of nominal pay to cost of living). DC at $37.01. Massachusetts at $37.11. Maine at $37.50. South Dakota at $37.66. Vermont at $38.96. Maryland at $40.29. New Hampshire at $40.40. Utah at $40.44. Arizona at $41.07.

Notice the geographic clustering. New England wages do not stretch. The DC labor market is brutal for nurses on a real-dollar basis. Hawaii is in a category by itself. Meanwhile, the Upper Midwest punches well above its weight, and the Mountain West splits, with Oregon and Nevada doing very well adjusted and Utah and Arizona getting hammered by housing inflation that has not been fully matched by wage growth. Texas, Georgia, and Michigan all look much better adjusted than nominal, which is consistent with what every health system operator in those states has been saying for two years: the workforce is more available there, the dollar goes further, and recruiting is less of a knife fight than it is in Boston or San Francisco. The one place where the conventional wisdom holds clean is Oregon. The Pacific Northwest has been the quietly best-paying region for nurses on a real-dollar basis since at least 2021, and the 2024 data confirms the pattern. It is also a state where union density and Medicaid expansion have pushed wages up faster than housing in the parts of the state that are not Portland proper.

NP pay across the fifty states

Nurse practitioners are where the workforce strategy conversation gets interesting, because NPs are the lever every value-based primary care model, MA carrier, telehealth platform, and behavioral health startup uses to scale clinical capacity at sub-physician cost. Nominal NP pay in CA is the highest in the country at $173,190 a year, $83.26 per hour. New York is second at $148,410 and $71.35 per hour. Oregon at $148,030. Massachusetts at $145,140. Washington at $143,620. Alaska at $142,340. Connecticut at $141,140. New Jersey at $140,470. Rhode Island at $139,600. DC at $137,600. The same coastal pattern. CA carries a $25K nominal premium over the next-highest state, mostly driven by the Bay Area NP labor market, where Sutter and Stanford and UCSF compete for the same talent pool with the same salary bands that have crept up consistently since the pandemic. The bottom of the nominal NP list is mostly Southeast and Plains, with Tennessee at $108,180, Alabama at $109,650, South Carolina at $113,950, Arkansas at $116,030, Kentucky at $116,930.

Adjust for cost of living and the leaderboard inverts. Oklahoma jumps to first at $71.29 per hour, roughly $148,000 annualized, which is mathematically very close to what an NP in CA makes nominally. Iowa is second at $71.26. Kansas third at $70.67. New Mexico fourth at $70.38. West Virginia fifth at $69.83. Texas at $67.90. Michigan at $67.65. Missouri at $67.51. Mississippi at $67.23. Indiana at $67.20. The Midwest and Plains absolutely dominate the adjusted NP map. Every state from New Mexico to West Virginia along the southern tier of the Midwest is paying NPs real-dollar wages that exceed what CA pays its NPs after rent and taxes get stripped out. The bottom of the adjusted NP list is, again, Hawaii at $34.73, where NPs nominally make $135,020 but the COL multiplier shreds it. DC at $46.63. Massachusetts at $47.83. Maryland at $53.01. Maine at $54.80. Vermont at $54.88. Alaska at $55.27, despite its $142K nominal pay. The pattern that nominal-money outliers turn into adjusted-money losers holds.

Two structural factors are worth flagging for anybody using NPs as a strategic clinical resource. First, scope of practice. The states that grant full practice authority to NPs (which now covers more than half the country after Utah, New York, and Kansas joined the FPA list in the last few years) tend to have somewhat compressed nominal NP wages relative to reduced or restricted states. The mechanism is supply-side: when NPs can hang their own shingle or run a clinic without physician collaboration, supply expands, and per-NP wages get marginal pressure. The interesting twist is that FPA states are also the states where NPs are most productive in real volume terms. An NP in Iowa or New Mexico, both FPA states, can see more patients and bill more cleanly than an NP in Florida or Texas operating under collaborative agreement constraints. So even at slightly lower nominal pay, the per-RVU economics for the employer can be better. Second, MA penetration matters. States with high MA penetration are pulling NPs hard into in-home assessment and chronic care management programs, which has pushed NP wages up in states like Texas and Arizona faster than the regional average. Florida looks oddly mid-pack on adjusted NP pay at $60.02 despite being the MA capital of the country, mostly because the Florida COL index has crept up faster than NP wages, especially in South Florida and Tampa Bay.

PA pay across the fifty states

Physician assistants are the third leg of the stool, and the data here flips a few common assumptions. The first surprise is that PAs in many states actually out-earn NPs nominally, despite NPs getting most of the headline attention. California pays PAs $77.88 per hour, $161,980 a year, less than CA NPs nominally but still dramatically high. Washington pays PAs $75.03 per hour ($156,060), Oregon $73.16 ($152,160), New York $72.73 ($151,280), New Jersey $71.71 ($149,160), Rhode Island $71.29 ($148,290). The next tier is full of states where PA pay is essentially identical to or higher than NP pay: Minnesota PAs at $69.68 versus Minnesota NPs at $61.60. Indiana PAs at $69.33 versus Indiana NPs at $60.83. Arizona PAs at $68.83 versus Arizona NPs at $63.90. Wisconsin, Iowa, Kansas, Nebraska, all show PA pay matching or exceeding NP pay. This is a real specialty mix story. PAs disproportionately work in surgical specialties, orthopedics, dermatology, emergency medicine, and cardiology, where compensation runs higher than primary care, which is where most NPs cluster. The mix shifts the average enough to flip the rankings.

Adjust for cost of living and the PA map does what every other map in this analysis does, which is to send the Midwest to the top. Indiana takes first at $76.61 per hour adjusted, roughly $159,000 annualized, the highest cost-adjusted clinical wage of any state for any role covered here. New Mexico is second at $74.24. Missouri third at $74.16. Minnesota fourth at $73.27. Iowa fifth at $72.60. Oklahoma at $71.62. Texas at $71.08. West Virginia at $69.25. Kansas at $69.62. Michigan at $68.76. The cost-of-living-adjusted PA in Indiana is making more in real-dollar terms than the cost-of-living-adjusted PA in California by roughly $23 an hour, which annualizes to a $48,000 swing. That is not noise. That is a structural arbitrage that hospitals in IN, MO, and NM have been quietly running for years, and that physician staffing firms have figured out how to monetize through travel and locum placements that effectively let big-system PAs work in lower-COL markets while keeping their compensation closer to coastal levels.

The bottom of the PA list is the usual suspects, with one twist. Hawaii at $36.78 adjusted, dead last as always. Massachusetts at $45.28. DC at $48.87. Arkansas at $50.89 (PAs are not paid well in AR even nominally, at $93,880, the lowest in the country). Mississippi at $53.75. California at $53.78 adjusted, which is a remarkable fall from a $77.88 nominal hourly rate. Alaska at $54.21, which is another big-nominal-pay state getting hammered by COL. Maryland at $56.11. Kentucky at $56.95. Maine at $56.33. The PA story is the cleanest illustration of the broader pattern. Coastal pay scales look great in absolute terms but the cost-of-living tax is enormous. The Midwest and Mountain West are where PA labor cost and PA standard of living align best, both for the workforce and for the employer.

Patterns that cross all three roles

Once the three role-specific maps are stacked on top of each other, a few patterns become impossible to miss. Hawaii is structurally the worst place to work as a clinician on a real-dollar basis. The COL index of 186.9 is doing damage that no realistic wage premium can overcome, and the gap between Hawaii nominal pay and Hawaii adjusted pay is wider than any other state. DC, Massachusetts, Maine, and Maryland are persistent adjusted-pay losers across all three roles. The DC labor market has the additional twist that federal employer caps and a heavy nonprofit health system mix put a soft ceiling on nominal wage growth, while housing in DC, Northern Virginia, and Maryland has run hot for a decade. Massachusetts has the Boston housing problem plus a payer mix dominated by Mass General Brigham and Beth Israel Lahey, which gives the employer side pricing power on wage bands.

The Midwest and Plains states dominate the adjusted-pay top end across all three roles, with Oklahoma, Iowa, Kansas, New Mexico, Indiana, Missouri, and Minnesota appearing in the top ten for at least two of the three roles. The mechanism is the same in every state: nominal pay is roughly 70 to 90 percent of coastal pay, but housing, food, and services run 65 to 90 percent of the national index, which leaves more dollars in the clinician pocket and lower unit costs on the employer side. This is the basic labor arbitrage that has driven a decade of health system consolidation toward the Midwest, the expansion of telehealth platforms with Midwest-based clinician networks, and the value-based care enablers that have figured out that capitated economics work much better when the clinical labor cost is structurally 20 percent lower than it is in San Diego or Seattle.

California is the perpetual nominal champion and the perpetual adjusted disappointment. Its nominal RN pay is 80 percent higher than South Dakota’s. Its adjusted RN pay is roughly 30 percent higher, which is still meaningful but a fraction of the gap. California NPs nominally earn 60 percent more than Tennessee NPs. Adjusted, the gap shrinks to about 0 percent. California PAs make 70 percent more than Arkansas PAs nominally. Adjusted, the California PA earns only 5 percent more than the Arkansas PA on a purchasing-power basis. That is the kind of finding that should reshape recruiting strategy for any system trying to win clinicians who are weighing relocation. CA has the sun, the food, the politics, and the brand. It does not, on a cost-adjusted basis, have a meaningful pay advantage anymore. That is a sales pitch problem for CA recruiters and an opportunity for any low-COL state trying to poach talent.

A subtler pattern is the relationship between nominal pay and adjusted pay variance. The states with the most volatile rankings across the three roles tend to be the ones with idiosyncratic labor mix issues. Arizona looks great for PAs on adjusted basis ($61.73, decent) but mid-pack for NPs and RNs, which probably reflects the heavy dermatology, ortho, and travel PA concentration in Phoenix and Scottsdale. New Mexico is in the top five for both NP and PA adjusted, which mirrors the federal IHS and state-driven primary care expansion that has poured money into APP workforce there for the last decade. West Virginia is in the top ten for NP adjusted and bottom third for nominal PA pay, which is more about the state’s struggle to attract PAs into rural settings than it is about adjusted purchasing power.

The methodology stuff most people skip past

A few things to know before anybody quotes these numbers in a board deck. The BLS May 2024 OEWS release is based on a six-panel survey covering responses from November 2022 through May 2024. That means the nominal wage figures lag the actual labor market by anywhere from six months to two years, depending on the panel weight. In a fast-moving wage environment, like the one nursing has been in since 2022, the headline numbers understate current pay. RN pay in particular has continued to drift up in 2025, driven by travel-to-perm conversion premiums, retention bonuses, and union contracts that have ratified faster than payer rate growth. The number a recruiter quotes today in any state is probably 3 to 7 percent higher than the BLS figure.

Second, the COL index Becker’s used is the World Population Review composite, which is a useful one-number summary but compresses real underlying variation. Within California, the COL index runs from about 110 in the Central Valley to over 180 in San Francisco proper. Within Texas, Austin and Dallas run roughly 115, while smaller markets like Lubbock and Amarillo run below 90. A state-level number is useful for cross-state comparison but is a blunt instrument for any operator trying to model unit cost in a specific MSA. The BEA Regional Price Parities, published annually for both states and MSAs, are the cleaner source for sub-state work. For the analysis here, the state-level COL index is good enough to make the big-picture point, but anybody making a real capital allocation decision should pull MSA-level data.

Third, BLS suppressed Colorado data entirely for the May 2024 release because the state’s unemployment insurance system modernization caused data quality issues that the Quarterly Census of Employment and Wages could not resolve in time for the April 2 publication. Colorado will probably show up in the next release, but for any 2025 analysis using May 2024 data, Colorado is a hole in the dataset. NP data is also suppressed for Nevada due to confidentiality thresholds, which means the Nevada NP map is a guess until a future release. PA data has nominally been collected for Mississippi this cycle ($98,270 nominal, $53.75 adjusted), which is an improvement on prior years where MS PA data was suppressed.

Fourth, mean versus median. The BLS data used in these tables is the mean. Mean RN pay is pulled up by senior leadership track nurses, CRNAs (who BLS separates out, so this is cleaner for RNs but not perfect), and clinical nurse specialists. NP and PA means are pulled up by surgical and procedural subspecialty pay. The median is usually 5 to 10 percent below the mean and is a better number for thinking about the typical clinician’s experience. Becker’s published mean. The full BLS file has both. For workforce strategy purposes, the mean is usually more useful, because it reflects total payroll burden more accurately, but the median is what an individual clinician is comparing themselves against.

What this means for workforce strategy and digital health unit economics

The strategic implications fan out across a few different stakeholder groups. For health system CFOs running multi-state footprints, the cost-adjusted pay map is the closest publicly available proxy for real per-FTE labor cost burden, and the conclusion is unambiguous. Midwest, Plains, and parts of the South are where labor cost per unit of clinical capacity is lowest in real terms. Coastal markets, especially Boston, DC, Baltimore, and Honolulu, are where labor cost is highest. A multi-state system rationalizing its workforce footprint should be thinking very hard about which clinical functions it can centralize in lower-COL markets and serve via virtual or distributed care models, and which functions truly require physical presence in high-COL markets.

For digital health companies running a national clinician network, the same map says where to recruit. A virtual primary care platform building its NP bench from scratch in 2025 should be sourcing heavily from Oklahoma, Iowa, Kansas, New Mexico, and Indiana, where adjusted NP wages are in the top five but where nominal NP wages are still well below what a New York or California NP commands. The wage arbitrage between licensing those NPs to deliver care across state lines through the eNLC compact (for RNs) and via the APRN compact (which is in much slower rollout, with only a handful of states fully implemented as of 2025) versus paying CA or NY rates is structurally meaningful. The bottleneck is licensure, not labor supply. Any platform that can solve multi-state licensure efficiently can route capacity to the lowest real-cost labor market in the country and serve patients anywhere.

For value-based care enablers running capitated MA primary care models, the math is even sharper. The per-member-per-month margin in MA primary care is roughly $50 to $200 depending on risk adjustment, plan, and population, and clinical labor cost is the single biggest variable expense. A model that uses Midwestern NPs and PAs to deliver care, virtually or in physical clinics, has a structural cost advantage of 15 to 25 percent over a model staffed out of California or New York. The big VBC players (Oak Street pre-CVS, ChenMed, Iora pre-One Medical, Carbon Health, Galileo) have all been pulling clinical staff toward lower-COL markets for years for exactly this reason. The data here just quantifies what those operators already know.

For payers running in-home assessment programs and chronic care wraparound staffing, the labor pool question is more about availability than cost. The states with the best adjusted NP and PA pay are also, generally, the states where APP supply is most constrained relative to need, especially in rural areas. The interesting tension is that the states where it is easiest to make APP unit economics work are also the states where APP availability is lowest. Payers running national in-home assessment programs (Signify, prior to CVS, and Matrix, and PWN) have to balance the labor cost arbitrage against the difficulty of actually hiring APPs in a market like rural Iowa or western Oklahoma. The solution most of them have landed on is a hub-and-spoke model where APPs are concentrated in regional clusters and dispatched across MSAs.

For SNF and home health operators, the picture is darker. The states with the lowest adjusted clinical labor cost are also the states where Medicare reimbursement for post-acute care has been compressed the hardest, and the margin compression in SNF, home health, and hospice in 2024 and 2025 has been brutal. Operators are paying for clinical labor at rates that are competitive with hospitals but billing at rates set by the PDPM, PDGM, and hospice cap structures that have not kept pace. The cost-adjusted pay maps here suggest that the Midwest and Plains still have the best margin economics for post-acute, but the absolute margins are tight enough that even those markets are increasingly stressed.

For venture investors backing clinical staffing, telehealth, and care delivery models, the takeaway is to look hard at where the clinical labor base is sourced from and how it is licensed across markets. A company sourcing 80 percent of its clinicians from a five-state Midwestern footprint and serving patients in all 50 states has a structural margin advantage that is durable. A company sourcing clinicians from the California and New York markets and serving the same national patient base is fighting an uphill labor cost battle and is unlikely to ever achieve the kind of unit economics that justifies a venture-scale outcome. The same logic applies to behavioral health platforms, virtual urgent care, and chronic care management. Geography of clinical supply has become a first-order strategic question.

Bottom line for operators, investors, and policy folks

The cleanest one-sentence summary of the May 2024 BLS data, adjusted by the 2024 World Population Review COL index as Becker’s did in its April 2025 publications, is that the headline pay numbers in healthcare are mostly a function of cost of living, and once cost of living is stripped out, the Midwest and Plains pay clinicians more in real-dollar terms than the coasts do. That conclusion has been roughly true for a decade. It is more true in 2025 than it has ever been, because coastal housing inflation has continued to outpace nominal coastal wage growth, while Midwestern wages have caught up faster than Midwestern COL.

For a hospital system operator, the strategic question is whether to keep building expensive coastal capacity or to shift the center of gravity toward markets where labor cost per unit of clinical output is structurally lower. The answer for most national-scale systems is the latter, and the M&A activity of the last three years (Sanford-Marshfield in the Upper Midwest, AdventHealth growth in the Carolinas, CommonSpirit consolidation across the South and Midwest, and the wave of Texas and Tennessee deals) reflects that. For payers, the takeaway is that MA, Medicaid managed care, and the commercial small-group market are easier to serve profitably in markets where the clinical labor base is cheaper in real terms, which is why the high-MA-penetration Midwest is where every payer is pushing hardest.

For digital health and care delivery startups, the implications are concrete. Recruit clinicians from low-COL markets. Solve multi-state licensure aggressively. Build the operations stack that lets a clinician sitting in Tulsa or Des Moines serve a patient in Boston or San Francisco without friction. Do the math on what the same FTE costs in five different states and pick the cheapest one where the labor pool is deep enough to scale. For policy folks, the takeaway is that the workforce shortage narrative is partly a price signal problem. The clinicians exist. They are concentrated in lower-COL markets where their dollars stretch further. The challenge is getting their capacity to where the patients are, and that is a licensure, technology, and reimbursement design problem more than it is a raw supply problem.

And for the clinicians themselves, the data says what every Reddit thread on r/nursing and r/physicianassistant has been saying for years. California pays the most on paper. Indiana, Oklahoma, Iowa, and New Mexico pay the most in reality. Hawaii looks like a vacation in the offer letter and a budget crisis on day one. The Midwest does not have the brand, but it has the bank account. The numbers are right there in the BLS table. They tell a different story than the recruiting brochure does.​​​​​​​​​​​​​​​​

Adjusted NP pay: Oklahoma is first at roughly $148K annualized. Iowa second. Kansas third. California does not crack the top ten. The coastal sweep inverts completely.

Indiana has the highest …

Read more

Adjusted NP pay: Oklahoma is first at roughly $148K annualized. Iowa second. Kansas third. California does not crack the top ten. The coastal sweep inverts completely.

Indiana has the highest cost-adjusted PA wage of any state for any role in the BLS May 2024 dataset. $76.61 per hour adjusted. California PAs, nominally highest, land at $53.78 adjusted.

That gap between Indiana and California PAs is about $23 per hour. Annualized, it is a $48K swing in real purchasing power. That is a structural arbitrage, not noise.

Subscribe to www.onhealthcare.tech for free and paid articles, podcasts, and more.

Welcome to Healthcare Markets & Technology.

Rigorous analysis of AI, policy, capital, technology, and clinical operations across U.S. healthcare — written for the people who build, invest in, and lead it.

Free subscribers get 2 public articles per week. Upgrade to paid → for the full 7 articles/week, paid podcast episodes, deal breakdowns, and the complete 538-deep-dive archive.

Subscribe or upgrade here →

One thing to bookmark: the searchable Knowledge Base at kb.onhealthcare.tech isn’t in Substack’s menu. Save it now — on mobile, tap share → “Add to Home Screen.”

Reply to any email with questions. I read every one.

— Trey

Video Preview

🎧 Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Cuban Joins Trump at TrumpRx to Add 600 Generics via Cost Plus, Amazon, GoodRx: Why Cash Pricing That Skips Deductibles Misses Most Insured Americans & How PBM Formulary Threats Block Brand Adoption

Mark Cuban stood next to Trump in the Roosevelt Room on Monday. Cuban spent 2024 campaigning against him. Drug pricing breaks politics in weird ways. Here is what the TrumpRx expansion actually means for patients…

Listen now

4 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

1. The Monday Photo Op No One Predicted

2. What TrumpRx Actually Is Under the Hood

3. The Deductible Trap and the 85 Percent Problem

4. Why Cuban Has Been Locked Out of Brand Drugs

5. The Employer Formulary Problem

6. The Generic Math, Including What the Studies Say

7. The Good, the Bad, the Ugly

8. So Will This Actually Work

Abstract

• Monday 5/18/26: WH announces TrumpRx expansion w/ Mark Cuban Cost Plus Drugs, Amazon Pharmacy, GoodRx, adding 600+ generics on top of the original 43-brand-name Feb 2026 launch.

• TrumpRx is a coupon aggregator, not a pharmacy. Routes patients to manufacturer DTC sites or generates coupons for retail pickup. Built on GoodRx tech.

• Cash-pay only. Purchases generally don’t count toward deductibles or OOP maxes. Not accepted in CA or MA. Most manufacturer DTC deals exclude Medicare, Medicaid, TRICARE, VA enrollees.

• KFF and STAT analyses: for the ~85% of Americans with Rx coverage, insurance copays will usually beat TrumpRx cash prices, especially once deductibles are met. Real beneficiaries: uninsured (~8% under 65), high-deductible patients who never hit their deductible, and patients with non-covered drugs (GLP-1s for weight, IVF, fertility).

• Cost Plus model: drug cost + 15% markup + $5 pharmacy labor + $5.25 shipping. Cuban added ~500 generics to TrumpRx.

• Big 3 PBMs (CVS Caremark, Express Scripts, OptumRx) control ~90% of US Rx claims per FTC 2024 report. Cuban’s struggle on brands is structural: PBMs threaten manufacturers w/ portfolio-wide formulary deprioritization if they sell to Cost Plus at competitive brand pricing.

• Employer channel struggle = rebate addiction + contracting complexity + network access. Humana CenterWell partnership (April 2026) is Cuban’s workaround.

• FTC settlement w/ Express Scripts requires “covered access to TrumpRx as part of its standard offering upon relevant legal and regulatory changes.” Potential wedge to fix the deductible-credit problem.

• Net read: Generic expansion is a real win for cash-pay segment. For 85% of insured, math still favors insurance. Real reform is upstream of the website.

The Monday Photo Op No One Predicted

Mark Cuban standing in the Roosevelt Room next to Donald Trump on May 18, 2026, was not on anyone’s bingo card last year. Cuban spent the 2024 cycle campaigning for Kamala Harris, calling Trump’s economic ideas incoherent, and going on every podcast that would have him to argue the case. Trump, asked about Cuban’s appearance Monday, told reporters Cuban had made “a big mistake” in 2024, then went on to praise Cost Plus Drugs as one of the best things to happen to American patients. Cuban, in turn, told Newsweek after the event, “Anything that reduces the cost of medications for Republicans, independents, and Democrats is a win. I’ll align with anybody, I don’t care. If you’re going to make healthcare cheaper, you’re my new best friend.”

That’s a clean summary of the politics, and also a useful reminder that drug pricing breaks party lines in weird ways. Both ends of the spectrum want to swing at the same PBM piñata. It also signals where Cuban thinks his actual leverage sits: not in fighting CVS Caremark or OptumRx through op-eds, but in plugging Cost Plus into whatever distribution rails happen to have eyeballs and government endorsement attached. TrumpRx is the largest single distribution channel on offer that doesn’t require running through a vertically integrated PBM-insurer. So he plugged in.

Standing next to Cuban Monday were execs from Amazon Pharmacy and GoodRx, plus HHS adviser Chris Klomp (one of the original TrumpRx architects) and CMS Administrator Mehmet Oz. Oz repeated the MAHA-friendly stat that almost one in three Americans, when they go to a drugstore, cannot afford to pick up the medications their doctor prescribed. The number is directionally supported by various access surveys, but adherence drop-off can come from copays, prior auth, step therapy, formulary exclusions, and pure cash-pay all at once. TrumpRx only addresses the cash-pay piece, which is the smallest of those buckets for most insured patients.

What TrumpRx Actually Is Under the Hood

Read more

TrumpRx is not a pharmacy. It is a coupon aggregator built on GoodRx tech. The Monday update added 600+ generics via Cost Plus Drugs, Amazon, …

Read more

Welcome to Healthcare Markets & Technology.

Rigorous analysis of AI, policy, capital, technology, and clinical operations across U.S. healthcare — written for the people who build, invest in, and lead it.

Free subscribers get 2 public articles per week. Upgrade to paid → for the full 7 articles/week, paid podcast episodes, deal breakdowns, and the complete 538-deep-dive archive.

Subscribe or upgrade here →

One thing to bookmark: the searchable Knowledge Base at kb.onhealthcare.tech isn’t in Substack’s menu. Save it now — on mobile, tap share → “Add to Home Screen.”

Reply to any email with questions. I read every one.

— Trey

Video Preview

Podcast, Part I (Free)

Thoughts on Healthcare Markets & Technology

Part I: Hardware Hits Mega-Round Season Again: Whoop’s $575M at $10.1B, Neuralink at $9.7B, BrainCo’s $1.3B Unicorn Birth, eMed’s $200M Series A at $2B, and Whether Consumer Hardware Is Back

Q1 2026 produced four hardware-driven digital health mega-rounds in one quarter. Whoop at $10.1B. Neuralink at $9.7B. BrainCo at $1.3B unicorn birth. eMed at $2B. Either hardware is back or this is the top…

Listen now

5 days ago · Thoughts on Healthcare

Podcast, Part II (Paid)

Thoughts on Healthcare Markets & Technology

Part II: Hardware Hits Mega-Round Season Again: Whoop’s $575M at $10.1B, Neuralink at $9.7B, BrainCo’s $1.3B Unicorn Birth, eMed’s $200M Series A at $2B, and Whether Consumer Hardware Is Back

Q1 2026 produced four hardware-driven digital health mega-rounds in one quarter. Whoop at $10.1B. Neuralink at $9.7B. BrainCo at $1.3B unicorn birth. eMed at $2B. Either hardware is back or this is the top…

Listen now

5 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Abstract

Q1 2026 minted four hardware-driven digital health mega-rounds in a single quarter. Whoop closed $575M at $10.1B, Neuralink got marked at $9.7B, BrainCo raised $272M at $1.3B in a unicorn-birth round, and eMed pulled a $200M Series A at $2B. Add Oura sitting at $11B and Verily’s $300M from Alphabet’s syndicate, and four of the top ten digital health unicorns by valuation now run on atoms rather than bits. The bull pitch is different this cycle: subscription stacks on top of hardware, FDA-cleared diagnostics moving over-the-counter, sovereign BCI plays out of China, and platform partnerships with academic medical centers. The bear pitch is that capital is concentrating violently (mega-rounds took ~61% of all digital health funding across just 19 deals) and this is what cycle tops have always looked like. Either consumer hardware is back or this is the marker top.

Table of Contents

1. How the last hardware cycle ended

2. The Q1 receipts

3. Whoop, Oura, and the wrist-versus-ring fight

4. The brain interface bracket

5. eMed, Verily, and the at-home diagnostic comeback

6. What the hiring data is whispering

7. Either hardware is back or this is the top

How the last hardware cycle ended

The hardware bear thesis was earned, not invented. Peloton peaked around $171 a share in early 2021 and was trading under $4 by mid-2024, with three CEOs cycled through and a strategic review that produced no buyer at any price anybody could justify to a board. Lululemon paid roughly $500 million for Mirror in 2020, wrote the whole thing down a couple of years later, and quietly shuttered the standalone Mirror operation entirely. Fitbit got absorbed into Google, where its hardware roadmap promptly went on the kind of long hiatus that always precedes a Google product cull. Pear Therapeutics, which sat adjacent to hardware as a prescription digital therapeutics distributor, ran out of cash in 2023. 23andMe went into bankruptcy and is currently in the awkward process of having its one and only valuable asset, a few million spit-tube genomes, parceled out by a court. Theranos and the IPO-era diagnostics startups had poisoned the well for at-home dx years before that, and the consumer hardware category as a whole spent most of 2022 through 2024 in what one tier-one healthcare investor privately described as the penalty box.

The lesson VCs took from that wreckage was the usual one. Hardware is hard. Margins compress, return horizons stretch, channel costs eat you alive, FDA pathways turn into years of cash burn, and the moment you hit any scale the platform companies decide whether you live or die. Apple was the chief executioner here. Each new Apple Watch generation rolled up another adjacent health category. Blood oxygen, EKG, sleep tracking, atrial fibrillation detection, fall detection, sleep apnea screening, and most recently FDA-cleared hypertension monitoring all came stock on the wrist. Every release left some hardware startup wondering whether they had just been Sherlocked.

By late 2024 the consensus take was that any consumer-facing health device without either a regulatory moat (FDA-cleared diagnostic claim, ideally with a CPT code attached) or a closed clinical loop (real provider workflow, real reimbursement) was going to get bundled into oblivion. So when the first quarter of 2026 produced a string of nine and ten-figure hardware rounds, the question wasn’t whether the data was real. The question was whether late-stage capital had seen something the prior twenty-four months of consensus had missed, or whether the dollars had simply run out of better places to go.

The Q1 receipts

Start with the Whoop number because it’s the loudest. The Boston company closed $575M in a Series G at $10.1B, with Collaborative Fund and Institutional Venture Partners leading, Accomplice and Foundry alongside, and a sprinkle of athletes including Cristiano Ronaldo, which is the kind of cap table that either signals the end of an era or the start of one and is rarely ambiguous in retrospect. Whoop’s last public mark was around $3.6 billion in mid-2021. So this is roughly a 2.8x in five years, which doesn’t sound spectacular on its own, until anyone remembers that essentially every peer consumer health company is sitting at a meaningful discount to its 2021 marks. Whoop is up. Valuation per employee here lands around $7.7 million, on a headcount of roughly 1,300.

Oura is at roughly $11 billion as a private out of Finland and has now earned the unofficial title of unicorn-that-wouldn’t-die. Neuralink got marked at $9.7 billion. BrainCo, a Chinese brain-computer interface company that started its life selling EEG focus headbands and has been steadily migrating toward more medically serious territory, closed $272M in a Series B at a $1.3B unicorn-birth valuation, with IDG Capital, Walden International, Dowstone, and Huazhu Group writing checks. eMed, the at-home diagnostic platform that started life verifying COVID rapid tests over proctored video, raised $200M in a Series A at a $2 billion valuation with Aon, Antonio Gracias, Jeff Aronin, Ara Cohen, and Joe Lonsdale on the cap table, which reads more like a private aviation manifest than a typical healthtech investor list. Verily, the Alphabet life sciences spinout that has pivoted somewhere between three and six times depending on whose count is canonical, raised $300M from a syndicate including Series X Capital, Alphabet itself, the University of Colorado Anschutz Medical Campus, and UCHealth.

Round out the picture with Ultrahuman’s $48M Series C out of India, Stairmed’s $73M Series C in China (another BCI play, this one backed by Alibaba, Lilly Asia, OrbiMed, and FountainBridge Capital), Monteris Medical’s $28M Series E for laser-based brain surgery, XCath’s $30M Series C, Omniscient Neurotechnology’s $13M Series D out of Australia, and a handful of smaller pickups in surgical AR/VR and neuromodulation. Hardware founders did not skip lunch this quarter.

Whoop, Oura, and the wrist-versus-ring fight

The thing about Whoop and Oura is that neither one really sells hardware in any economic sense a traditional consumer electronics company would recognize. Whoop’s pitch since the original strap launched has been that the device is the cost of entry to a subscription. The hardware itself is included with the membership at no marginal sticker price, the company makes its money on recurring revenue, and the underlying unit economics look closer to a SaaS business with a logistics overhead than to a Fitbit. Oura takes the opposite packaging approach (the ring sells outright at full retail, the boxes show up in Best Buy displays and on the Amazon home page) but has been steadily layering subscription on top, with the current product effectively requiring an Oura Membership to unlock the analytics that justify the device in the first place.

The point is the same. Both companies own the post-purchase relationship with the user, both have data feedback loops that compound over time, and both have started building clinical research and enterprise partnerships that traditional CE companies would never get near. Oura’s relationships with the Cleveland Clinic, the Department of Defense, and a long list of academic research groups suggest the ring is quietly becoming a research-grade data collection platform whether the company set out to be one or not. Whoop sells into pro sports and special operations the way Bloomberg sells into hedge funds, which is to say at full price, with a deeply embedded customer base, and with switching costs that compound the longer the data history runs.

Both have kept growing in the face of Apple Watch feature creep precisely because they are not actually competing for the same purchase decision. The Apple Watch wins as a smartwatch with health features attached. Whoop and Oura win as health devices with no smartwatch features at all. That distinction looks pedantic until somebody actually wears a device that doesn’t beep at them during a meeting. The hard question is whether either company can grow into a $10 billion or $11 billion mark on subscription economics alone, or whether the implicit thesis baked into these valuations is that they will need to expand into adjacent revenue streams. Continuous glucose monitor integrations, longevity testing kits, women’s health analytics, enterprise wellness contracts, and clinical research data licensing are all on the menu. Both companies have been telegraphing platform ambitions for a couple of years. The Q1 round size and timing suggests their investors believe they are close enough to inflection on at least one of those adjacencies to justify writing big checks now rather than waiting another twelve months. If that read is right, Whoop and Oura are not really wearables companies anymore. They are health data platforms with wearables as the customer acquisition mechanism, which is a very different multiple regime than the one Fitbit got priced on the way out the door.

The brain interface bracket

Then there is the BCI cohort, which is its own animal entirely. Neuralink crossed the first-patient threshold in early 2024 with Noland Arbaugh, has done additional implants since, and got marked at $9.7 billion in this cycle. The clinical milestone matters less here than the narrative milestone. For roughly a decade, BCI was a category that had to apologize for itself in front of LPs. Every BCI deck had to spend three slides explaining why this time was different from the DARPA-funded BCI cycle of the late 2000s. Neuralink’s clinical demonstrations, combined with Elon Musk’s willingness to absorb regulatory and reputational risk that other founders simply could not, forced the category back onto the agenda whether the rest of the field liked it or not.

BrainCo’s $1.3 billion mark sits in an interesting place. The company started its commercial life selling EEG-based focus-training headbands into Chinese schools, which got it some unflattering international press a few years back when photos surfaced of classrooms of kids wearing what looked like compliance-monitoring devices. The medical roadmap has since migrated toward more serious targets including stroke rehab, prosthetic control, post-injury neural retraining, and clinical-grade neural monitoring. The capital backing in this round is almost entirely Chinese, which is part of the story. China is running a deliberate parallel BCI development effort that does not depend on FDA timelines or American patient enrollment, and BrainCo and Stairmed together are the most visible expressions of that policy. Stairmed has reportedly done its own first human implant with an invasive electrode system, with partnership backing from Tsinghua, which puts the Chinese BCI cohort meaningfully closer to Neuralink in clinical progress than most American investors have priced into their models.

The bull case for the bracket is that BCI is roughly where medical robotics sat around 2010, when Intuitive Surgical was the only name with a credible installed base and nobody quite believed there would be a serious second-mover ecosystem. There is now a second-mover ecosystem in surgical robotics worth tens of billions in aggregate enterprise value, with Stryker, Medtronic, Smith and Nephew, and a long tail of category-specific players. If BCI follows even a fraction of that arc, the names hitting unicorn status in 2026 are candidates for the Stryker-of-BCI position a decade out. The bear case is that BCI carries device-failure modes that have no real analogue in surgical robotics. Electrode degradation over multi-year horizons, biocompatibility of materials sitting in cortical tissue, the small matter of cracking open a skull every time the firmware needs a redesign, and a regulatory pathway that has barely been written. Reimbursement is somewhere between hypothetical and aspirational. The companies pricing in this quarter are pricing in a future state that may or may not show up on the timeline the cap tables are betting on.

eMed, Verily, and the at-home diagnostic comeback

eMed is a more confusing valuation to assess. The company built its initial revenue base verifying at-home COVID antigen tests over proctored video, which was an extraordinarily clean monetization line during the pandemic and largely went away with it. The Series A at $2 billion with 604 employees and an investor lineup of Aon plus a who’s who of the Joe Lonsdale and Antonio Gracias orbit suggests the company has reassembled around a broader at-home diagnostics platform play. Best read of the strategy is some combination of GLP-1 prescribing workflows, weight loss and metabolic health programs, and verified at-home testing across categories that insurers and self-insured employers will actually reimburse. Valuation per employee here lands around $3.3 million, which is on the high side for a services-adjacent business but not absurd if the recurring contract book with payers and employers is as deep as the round size implies.

Verily is the original Alphabet life sciences vehicle and at this point the running joke in any conversation about pivot economics. The contact lens that was supposed to measure glucose in tears did not work. The mosquito releases did. Onduo got folded back into a partner. Coefficient Insurance got sold off. The current incarnation centers on a clinical research platform plus precision health offerings to health systems, and the $300 million round with UCHealth and Anschutz participating points toward that focus. Whether the round is a vote of confidence in the latest strategy or a bridge structured to keep optionality open while Alphabet figures out what it actually wants to do with the asset is genuinely ambiguous, and the syndicate composition can be read either way.

The bigger at-home diagnostics story across this quarter sits underneath the eMed and Verily headlines, which is the mainstreaming of continuous glucose monitors for non-diabetic use. Dexcom’s Stelo and Abbott’s Libre Rio both launched over-the-counter products targeting metabolic health rather than glycemic control. Function Health, Hims, Hers, Levels, and a growing tail of metabolic health subscription products run software wrappers on top of lab panels and CGM data. The hardware companies underneath those wrappers, namely Dexcom, Abbott, Roche, and BD, are public, large-cap, and not in any private market data set, but their wholesale shift toward consumer channels is the most important atom-level story in consumer health right now. It is also what allows the eMed and Verily rounds to look defensible rather than ridiculous. Hardware-derived diagnostic data has finally crossed a usability threshold where consumers will pay for the readouts and providers will accept the inputs into clinical decisions. Five years ago that was not true.

What the hiring data is whispering

The other thing worth pulling out is what the headcount data has been doing in the background while the deal headlines were busy. Healthcare humanoid robot developers ran roughly a thirty-four percent year-over-year headcount increase. Neuromodulation device companies were up about twenty-five percent. Fitness tracking apps and sleep tracking devices clocked something close to twenty-four percent each, and AR/VR surgical guidance and navigation companies were up around twenty-two percent. Healthcare AI model developers led the entire digital health tape at almost fifty-nine percent year-over-year headcount growth, but the hardware-adjacent categories were sitting right behind them. That pattern is notably different from the three prior years, where headcount growth was concentrated almost entirely on the software side and hardware companies were quietly thinning out engineering rosters.

The engineering labor flow tells the bull story better than the deal data does because it’s harder to fake. Mid-career poach activity in mechanical engineering, electrical engineering, firmware, embedded ML, and biomedical signal processing has visibly picked up. Stock-comp dilution at private hardware companies has reset upward, which is the kind of move only made when a company genuinely believes it is staging for a multi-year product roadmap and needs to compete for talent with model-developer roles that pay top of market. Hardware companies that thought they were hitting a wall in 2023 reversed course in 2025, and the talent market is reflecting that reversal in roughly real time.

Either hardware is back or this is the top

Which leaves the actual question, which is whether this is the start of a multi-year hardware cycle or the kind of late-cycle exuberance that gets remembered as a marker top once funding conditions turn. The honest answer is that both reads are defensible and that the meaningful resolution will not come from another quarter of data.

The bull case rests on two structural shifts that did not exist in the prior cycle. The first is that ambient and continuous sensing technology actually works now in a way it did not in 2017 or even 2021, because the on-device ML compute and the model architectures finally caught up to what the sensors can produce. The second is that the regulatory and reimbursement landscape for hardware-derived data has loosened materially. CGM over-the-counter clearance, Apple Watch hypertension clearance, FDA’s continued willingness to greenlight software-as-a-medical-device built on top of sensor outputs, and the slow but real movement of CMS toward reimbursing remote patient monitoring built around consumer-grade devices have all moved the underlying unit economics. Hardware companies are finally able to charge for what they produce, and increasingly they can charge two or three different counterparties for the same data stream, which is what a real platform business looks like in practice.

The bear case is that valuation discipline rarely survives a quarter like this one. Mega-rounds captured something like sixty-one percent of all digital health funding in Q1, a record concentration, and that capital crowded into just nineteen deals. That kind of concentration tends to produce its own narrative ratification. The investors writing the Whoop check believe Whoop is worth ten billion because their fellow investors believe Whoop is worth ten billion, and the private secondary markets that would normally provide an external sanity check are themselves dominated by the same fund managers writing the primary checks. None of these private hardware companies have disclosed financials, and the mark-to-mark mechanics across cap tables can absorb a lot of optimism before reality intervenes. Hardware has always required more capital and longer return horizons than software, and the temptation to mark up paper at the top of a cycle is exactly the kind of behavior that produces the Peloton stories in a few years.

The most honest read might be that the names raising in this quarter are not the ones to worry about. Whoop’s subscription book, Oura’s enterprise pipeline, Dexcom’s and Abbott’s CGM channel work, and Neuralink’s clinical pipeline all point to real underlying progress that the capital is rewarding. The companies to watch for the top signal are the second derivatives, the pile-in rounds that will appear in the next two quarters across the same categories at the same prices without the same earned right to be there. That’s where the top will get marked, if it gets marked at all. For now the wearable and BCI and at-home dx categories all look meaningfully more durable than they did eighteen months ago, and the dollars are flowing accordingly. Whether they should be is a question whose answer is two years away and probably won’t be obvious until somebody’s lead investor stops returning calls.​​​​​​​​​​​​​​​

​

Context: Peloton went from $171 to under $4. Lululemon wrote off Mirror entirely. Fitbit got swallowed by Google. The consensus by late 2024 was that…

Read more

Context: Peloton went from $171 to under $4. Lululemon wrote off Mirror entirely. Fitbit got swallowed by Google. The consensus by late 2024 was that consumer health hardware without a regulatory moat was getting bundled into irrelevance by Apple Watch.

What changed: Whoop and Oura are not hardware companies in any traditional sense. The device is the acquisition cost for a subscription and a compounding data relationship. Oura has clinical partnerships with the Cleveland Clinic and the Department of Defense. These are data platforms.

The underappreciated piece: BrainCo and Stairmed, both backed by major Chinese capital, are running BCI development programs that do not need FDA timelines. Stairmed has reportedly completed its own first human implant. The Chinese BCI cohort is closer to Neuralink than most Western investors know.

Subscribe to www.onhealthcare.tech for free and paid articles, podcasts, and more.

Welcome to Healthcare Markets & Technology.

Rigorous analysis of AI, policy, capital, technology, and clinical operations across U.S. healthcare — written for the people who build, invest in, and lead it.

Free subscribers get 2 public articles per week. Upgrade to paid → for the full 7 articles/week, paid podcast episodes, deal breakdowns, and the complete 538-deep-dive archive.

Subscribe or upgrade here →

One thing to bookmark: the searchable Knowledge Base at kb.onhealthcare.tech isn’t in Substack’s menu. Save it now — on mobile, tap share → “Add to Home Screen.”

Reply to any email with questions. I read every one.

— Trey

Video Preview

🎧 Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

What contracting with Epic for an EHR license really involves: license structure, module pricing, affiliate rights, hosting, termination economics, and the unwritten obligations behind the signature

Epic’s contract is widely called the most one-sided in healthcare IT. The reputation is earned. Here is what the License and Support Agreement actually involves…

Listen now

6 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

The setup, and why Verona paper looks the way it does

The license grant and what Program Property actually covers

Licensed volume, comprehensive pricing, and the module sprawl

Affiliate rights, sublicensing, and the Connect economy

Hosting, third party software, and the cloud question

Implementation, certification, and the consulting cottage industry

Termination, transition, and the cost of leaving

The unwritten contract that Verona expects every customer to know

What it means for buyers, sellers, and everyone caught in the middle

Abstract

• Epic’s standard LSA is widely viewed as one of the most one-sided contracts in healthcare IT, and the reputation is largely earned

• Public versions (Ardent Health 2019 and 2024 S-1 exhibits, TriZetto 1999 S-1/A, SF DPH Resolution 430-17, NC DHHS audit reports) show the structure but redact the dollars

• License grant: non-exclusive, US-only, convertible to perpetual at customer’s option after a defined runway, Affiliate rights flow through related entities

• Licensed Volume drives pricing and varies by module: inpatient beds, ED visits, ambulatory encounters, member months for health plan, claims for payer, lives covered for pop health

• Comprehensive bundle covers inpatient and ambulatory core; ASAP, OpTime, Beaker, Beacon, Willow, Stork, Radiant, Cupid, Cadence, MyChart, Cosmos, Caboodle layer on as additions

• Connect program creates a sublicensing pass-through that can run $20-40M/yr for large host systems but pushes compliance risk to the host

• Hosting historically self-managed or partner-hosted; ACE on Microsoft Azure now available, often at all-in cost matching or exceeding self-hosting

• Implementation runs through Epic; third-party consultants must be Epic-certified at $200-400/hr

• Termination is asymmetric: mid-size system replatforming runs $50-150M+ over 18-36 months with no source escrow remedy

• Honor Roll, KLAS, badges, UGM, and the Verona executive sessions form an unwritten contract that often shapes the relationship more than the signed one

• Real negotiation leverage exists at the edges (module scope, training credits, affiliate definitions, transition assistance); the core form barely moves

The setup, and why Verona paper looks the way it does

Epic was started in 1979 by Judy Faulkner with about $70,000 of seed money and a basement office in Madison. The company has never raised meaningful outside capital, has never gone public, and has never been acquired. That single fact does more to explain the contract than anything else does. There is no quarterly close that pushes a deal desk to discount in the final week of a fiscal period. There is no sales VP whose number is short and who will throw concessions at a buyer to make plan. There is no PE sponsor pushing for ARR growth that requires creative terms. The paper looks the way it does because Verona doesn’t need the deal more than the buyer needs the deal, and Verona has been comfortable acting that way for forty-plus years.

The campus itself is the tell. Storybook architecture, the Lord of the Rings buildings, the Harry Potter buildings, the actual treehouses, the campus that gets called the intergalactic headquarters as a half-joke. The kindergarten-meets-monastery vibe is real. None of it is whimsy for whimsy’s sake. It’s a deliberate signal that the place is run on its own terms, and the legal posture in the contract is downstream of that culture.

The agreement customers actually sign is the License and Support Agreement, the LSA. It comes with appendices for definitions, exhibits for licensed Program Property and pricing, an SLA, and an order form for modules added later. The cleanest public look at the form comes from SEC EDGAR. Ardent Health Partners filed the agreement as an exhibit to its 2019 S-1/A and again to its 2024 S-1 when it actually went public. TriZetto filed an earlier vintage of the same form in 1999, which is useful as a historical artifact because it shows just how much heavier the form has gotten over twenty-five years. The dollars are redacted across all of it. Public-sector contracts, like the San Francisco DPH agreement with Epic City Government LLC under Resolution 430-17 in 2017, and the NC DHHS contract referenced in legislative audit reports in 2023, give partial visibility into the numbers but only for a slice of the deal.

The setup matters because it determines what can and cannot be negotiated. Knowing that going in saves a lot of bruised feelings on the buyer side and a lot of wasted hours from outside counsel trying to redline things that will not get redlined.

Read more

Epic was founded in 1979, has never gone public, never raised outside capital, never been acquired. That single fact explains more about the contract than any legal analysis. There is no quarter…

Read more