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Abstract

- April 28, 2026 FDA press release: two RTCTs already live (AstraZeneca Phase 2 lymphoma, Amgen Phase 1b SCLC), Paradigm Health validated as ingestion layer, RFI open through May 29, pilot selection by summer, stated long term goal is continuous trials across all phases.

- Surface coverage: 20 to 40 percent timeline compression, AI in regulatory review, global competitive framing vs China.

- Real read: phase 1, 2, 3 were never properties of biology. They are properties of how long a paper based regulator took to review batched submissions. ~45 percent of drug development time is administrative dead time per FDA estimates surfaced in Reuters coverage.

- Knock on effects: tranched venture financing breaks, milestone licensing structures break, real options pricing of biotech assets breaks, catalyst calendar trading on the buy side breaks.

- Founder sized holes: continuous reg affairs OS, signal schema and aggregation layer, real time biostatistics, automated DSMB tooling, native streaming CRO, signal aware patient recruitment, regulator grade audit trail, streaming intelligence for the buy side, parametric trial insurance.

- Incumbent risk: large CROs and EDC platforms (IQVIA, Medidata, ICON, Veeva) make money on the latency they are about to lose. Retrofit loses to native architecture.

- Watch list: RFI responses May 29, pilot cohort August, first non pilot sponsor opt in early 2027, first big CRO M&A targeting a streaming native player.

Table of contents

1. The setup nobody is pricing in

2. Phase gates as latency artifacts, not biology

3. What actually breaks when streaming becomes default

4. The new control plane and where the value migrates

5. Companies that should exist and probably will

6. The CRO incumbent problem and why retrofit loses

7. The financing primitive rebuild

8. What to watch for in the next eighteen months

The setup nobody is pricing in

The FDA dropped a press release on April 28 that read like incremental modernization and was actually a quiet detonation of the phase gate construct that biotech has been trading around for forty years. Two real time clinical trials are already live. AstraZeneca’s Phase 2 lymphoma study and Amgen’s Phase 1b small cell lung program are streaming signals to the agency through Paradigm Health’s platform. The RFI is open through May 29. Pilot cohort selection is targeted for summer. The stated long term goal, written in the press release in plain English, is continuous trials across all phases. Most of the coverage led with the AI angle and the 20 to 40 percent timeline compression numbers, which is the surface read and also the wrong read. The deeper thing, the one that breaks valuation models and licensing deal templates and the entire muscle memory of how biotech finance works, is that phase 1, phase 2, and phase 3 were never properties of biology. They were properties of how long it took to clean and lock and ship batched data to a regulator who reviewed it as a document. Strip the latency out and the phases stop being a natural unit. That is what is actually being announced here, even though nobody on the FDA side has said it out loud yet.

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Two FDA draft guidances published in February and April 2026 represent the most significant structural shift in gene therapy regulation in over two decades. The Plausible Mechanism Framework (PMF) guidance from CBER and CDER creates a novel approval pathway for individualized therapies targeting ultra-rare genetic diseases where traditional RCTs aren’t feasible. The companion NGS safety guidance published April 14, 2026 operationalizes the genomic safety assessment requirements for GE products. Together, these create a coordinated regulatory architecture that has real implications for capital allocation, deal structuring, and founder strategy in health tech.

Key takeaways for the impatient:

- FDA now formally acknowledges single-patient and ultra-small-cohort studies can support marketing approval

- The “plausible mechanism” standard lets sponsors leverage mechanistic data and natural history as confirmatory evidence, replacing or supplementing traditional clinical endpoints

- Genome editing products can now bundle multiple mutation-targeting variants under a single IND/BLA

- NGS-based off-target analysis is now explicitly required pre-IND, with detailed methodology specs

- Both traditional and accelerated approval pathways remain available; the framework doesn’t mandate one

- This is a DOGE-era deregulatory signal with real scientific teeth, not just politics

Table of Contents

1. Why This Matters Right Now

2. The Plausible Mechanism Framework: What It Actually Says

3. The Clinical Evidence Problem It Solves (and Doesn’t)

4. CMC and Manufacturing Flexibility for Founders

5. The NGS Safety Guidance: What It Requires

6. Off-Target Analysis: The New Baseline

7. What This Means for Investors and Founders

8. The Regulatory Arbitrage Angle

9. Risks and Open Questions

Why This Matters Right Now

There are a lot of FDA guidance documents that get published every year and most of them don’t move the needle on anything. This one is different. In February 2026, FDA published the Plausible Mechanism Framework guidance for individualized therapies targeting specific genetic conditions with known biological cause. Two months later on April 14, 2026, FDA dropped the companion piece: a detailed draft guidance on NGS-based safety assessment for genome editing products. Commissioner Marty Makary called it a “forward approach to drive innovation” and CBER director Vinay Prasad described it as “revolutionary advance in regulatory science” in the press releases. That kind of language from FDA leadership doesn’t happen often and it’s worth taking seriously.

The broader context here is worth understanding before getting into the weeds. The Trump administration came in with an explicit deregulatory mandate and RFK Jr. at HHS has been vocal about cutting red tape in drug approval. Some of the deregulatory moves in health policy over the past year have been driven more by ideology than science. This one is actually different. The Plausible Mechanism Framework has been in development for years at FDA and reflects real scientific evolution in the field, specifically the growing maturity of CRISPR-based editing, ASO therapeutics, and next-gen sequencing tools that make it technically feasible to characterize individualized therapies with rigor even without large patient cohorts. The political winds accelerated the publication timeline, but the underlying science is solid. That combination of regulatory momentum plus scientific readiness is exactly the kind of setup health tech investors should be paying attention to.

To understand why this is significant, it helps to have a mental model of what the rare disease therapeutic development problem actually looks like. There are roughly 7,000 known rare diseases. Around 95 percent of them have no approved treatment. A large fraction of those are monogenic diseases with clearly identified pathogenic variants. For some of these conditions, the affected patient population might be a few hundred people globally, or fewer. In some cases it’s literally one child. The traditional drug approval pathway requires substantial evidence of effectiveness, which FDA has historically interpreted as requiring at least one adequate and well-controlled clinical investigation. That standard was developed for drugs treating large patient populations where you can enroll hundreds or thousands of subjects, randomize them, and power your study to detect statistically meaningful effects. It makes no sense applied to a disease affecting twelve people on the planet.

The Plausible Mechanism Framework: What It Actually Says

The PMF guidance is a long document with a lot of regulatory boilerplate, but the core intellectual contribution is relatively clean. FDA is formalizing a framework under which a drug or biologic can receive marketing approval based on a well-characterized mechanism of action, natural history data as external control, and confirmation that the therapy actually engaged its target, even when clinical evidence comes from a very small number of patients, potentially just one.

The five elements FDA identifies as constituting the plausible mechanism framework are worth stating precisely because the details matter for how you’d structure a development program around this. First, there needs to be a specific genetic, cellular, or molecular abnormality with a clear connection to the disease. Second, the therapy has to target the underlying or proximate pathogenic biological alterations, not just downstream symptoms. Third, you need a well-characterized natural history of the disease in untreated patients. Fourth, you need to confirm that the target was successfully drugged or edited. Fifth, you need to demonstrate improvement in clinical outcomes or course. That fifth element is where FDA has built in meaningful flexibility: “improvement” can be assessed against the natural history baseline rather than a contemporaneous control group, and in some cases surrogate endpoints or biomarkers can substitute for direct clinical benefit measures.

The guidance explicitly states that FDA anticipates substantial evidence of effectiveness for individualized therapies could be established based on a single adequate and well-controlled clinical investigation with confirmatory evidence. That’s the key sentence. It’s not new regulatory authority, FDA already had this, but it’s the first time the agency has put out a comprehensive framework explaining how they’ll apply existing standards to this class of products. The confirmatory evidence can come from mechanistic or pharmacodynamic data, confirmation of target engagement from nonclinical or clinical studies, or exposure-response relationships on biomarkers and clinical outcomes. That’s a dramatically wider definition of “confirmatory evidence” than what’s been operationally applied historically.

One of the more interesting structural innovations in the PMF guidance is the treatment of genome editing products with multiple variants. The guidance explicitly acknowledges that GE technologies are modular, meaning a CRISPR product can be thought of as composed of components, an editor protein, a guide RNA, a delivery vector, that can be modified somewhat independently. If a product is designed to correct different mutations within a single gene by swapping out the gRNA, FDA is saying those product variants can be included under a single IND and BLA. Clinical data from a defined set of mutations can support licensure of the platform, and a highly supported plausible mechanism of action can then be used to support adding new variant targets that weren’t in the original trial. This is potentially massive for platform-based gene therapy companies because it means you don’t need a separate approval for every mutation you can correct, you just need to demonstrate the editing activity and off-target risk profile for each new variant. The precedent this sets for scalable rare disease platforms is significant.

The Clinical Evidence Problem It Solves (and Doesn’t)

Let’s be real about what the PMF guidance solves and where the hard problems remain. The framework creates a viable regulatory path for the development of individualized therapies in ultra-small patient populations. That’s genuinely new and important. But it doesn’t make drug development easy or cheap, and it doesn’t eliminate the need for rigorous scientific work. What it does is change the nature of what rigorous looks like for this class of products.

The guidance is pretty direct about the fact that early planning is critical. Specifically, it recommends that sponsors initiate an observational protocol to collect baseline data as soon as potential study participants are identified, before manufacturing and nonclinical work is even complete. The idea is to pilot clinical outcome assessments, identify disease-relevant biomarkers, establish a lead-in baseline, and characterize disease trajectory during the time you’d otherwise be waiting around anyway. For investors, this is a hint about what early-stage development programs should look like: natural history data collection is not an afterthought, it’s a core asset that needs to be built in from day one.

The guidance also has a useful reminder about what makes an externally controlled trial credible. The natural history of the disease in the untreated population has to be well-characterized enough to distinguish a treatment effect from natural variability in the phenotype. For diseases with a highly variable or episodic course, FDA says they’ll consider longer follow-up durations or surrogate endpoint strategies. For diseases where the untreated natural history is essentially a well-defined decline to death or severe disability, the evidentiary bar for demonstrating that a treated patient is doing better than expected can actually be relatively low. Think about a disease where every untreated child is profoundly disabled by age two. If your ASO therapy results in a child reaching developmental milestones that no untreated child in the natural history literature has ever reached, that’s a pretty compelling case even without a contemporaneous control. FDA is essentially saying they’ll evaluate that kind of evidence on its merits.

What the PMF guidance does not solve is the manufacturing problem, the commercial problem, or the cost problem. Making an individualized therapy, one literally designed around a single patient’s mutation, is extraordinarily expensive. The guidance nods to this by noting that CMC development needs to happen concurrently with clinical development, and that sponsors should leverage prior manufacturing knowledge wherever possible to support validation and shelf life. But the per-patient economics of truly individualized GE or ASO products remain brutal. The guidance is realistic about this: it’s not a commercial scalability framework, it’s an approval framework. Figuring out reimbursement and manufacturing economics is left to others.

CMC and Manufacturing Flexibility for Founders

The CMC section of the PMF guidance is actually one of the more practically useful parts for founders building in this space. FDA is explicit about several areas where it intends to exercise flexibility, and knowing those going in can save meaningful time and money.

The guidance acknowledges that because the number of batches expected to be manufactured for individualized therapies is small, there are specific challenges around process validation and shelf-life determination that require adaptive strategies. Prior manufacturing knowledge from related products can be leveraged to support process validation of a similar product at the same manufacturing site. For GE products with drug product variants, CMC information including process performance qualification data can be shared across variants. This is directly connected to the platform licensing point above. If you’ve already done validation work for one gRNA variant, you don’t necessarily start from scratch for the next one.

On analytical methods, the guidance says that methods already qualified or validated for a closely related product may be appropriate with a suitability evaluation focused on product differences. That’s meaningful because method validation is time-consuming and expensive. The ability to bridge from an existing validated method to a new product variant rather than validating from scratch is a real cost and timeline advantage.

For shelf life, the guidance encourages sponsors to develop a strategy early in development and to leverage related product data to support the proposed shelf life. The implicit message for founders is: don’t treat these as separate problems to be solved sequentially. Build your CMC strategy around the platform from the beginning, accumulate stability data across every batch you make regardless of which variant it is, and document the comparability analysis between variants carefully. That documentation becomes an asset when you want to add the fifteenth variant to your BLA.

The NGS Safety Guidance: What It Requires

Published April 14, the NGS safety guidance is the operational companion to the PMF framework. Where the PMF guidance tells you what evidence you need, the NGS guidance tells you how to generate the genomic safety data that underpins that evidence for GE products specifically. It’s more technically detailed and less conceptually novel, but for anyone building in the GE space it’s essential reading.

The core question the NGS guidance is addressing is how you assess whether a genome editor is doing what you want it to do and nothing else. Every GE product has an intended on-target editing site. The safety concern is off-target editing: the editor acts on genomic sequences it wasn’t designed to target, either because those sequences have some homology to the intended target or because random factors result in activity elsewhere. Off-target edits can be benign, disruptive, or potentially oncogenic depending on where they occur and what they disrupt. Chromosomal translocations, which can occur when double-strand breaks happen at multiple locations and are repaired incorrectly, are a related concern.

FDA’s guidance establishes that NGS-based methods are the expected standard for characterizing this risk profile and specifies what those methods need to demonstrate. The guidance covers sequencing strategy, sample selection, off-target site nomination methods, confirmatory testing, analysis parameters, reporting requirements, and accounting for human genetic variation. The level of specificity is unusual for FDA guidance and that’s actually the point. One of the historical pain points for GE sponsors has been ambiguity about what the agency actually needs to see in an IND submission for off-target analysis. This guidance eliminates a lot of that ambiguity.

On sequencing strategy, the guidance distinguishes between short-read and long-read sequencing based on the nature of the edits being assessed. For edits affecting short stretches of DNA up to around 50 base pairs, short-read methods may be adequate. For larger insertions or deletions, long-read methods are required. The guidance is also clear that sequencing depth matters: you need to be sequencing at depth sufficient to detect off-target events occurring at frequencies lower than your on-target edit rate, because off-target events by definition occur less frequently if your product is working as intended. The guidance requires sponsors to provide data supporting the adequacy and sensitivity of their sequencing depth, either from internal validation experiments or peer-reviewed literature.

Off-Target Analysis: The New Baseline

The off-target analysis framework in the NGS guidance is the most practically important section for anyone doing diligence on a GE asset or building a company in this space. FDA lays out a two-stage process: off-target site nomination followed by confirmatory testing. Nomination is about identifying candidate off-target sites using computational and experimental methods. Confirmation is about actually measuring editing activity at those sites in appropriate cell types.

For nomination, FDA recommends using multiple approaches. The guidance distinguishes between modality-specific methods, biochemical assays and cell-based assays, and generally applicable methods including in silico computational algorithms and unbiased NGS-based methods. The choice of approach depends on the mechanism of action of the editor. Cell-based and biochemical assays were originally developed for editors that create double-strand breaks, like standard Cas9. Base editors and prime editors create nicks rather than breaks and may require modified or purpose-built assays. FDA is explicit that assays designed for double-strand break detection may not adequately capture off-target activity from nick-based editors and sponsors need to justify their assay selection with reference to the mechanism of their specific product.

The in silico nomination component requires scanning the reference human genome for sequences with homology to the guide RNA or target sequence, accounting for mismatches and bulges in both the DNA and gRNA, and considering PAM sequence requirements or other modality-specific recognition requirements. For CRISPR-Cas9, the canonical PAM is NGG but the guidance notes that spCas9 has been documented to recognize non-canonical PAM sequences and sponsors need to account for those in their search strategy. The guidance also introduces a whole section on off-target analysis accounting for human genetic variation, which is a relatively new wrinkle. Individual human genomes carry millions of nucleotide variants compared to the reference sequence, and some of those variants in a given patient could create new off-target sites that don’t exist in the reference genome. FDA recommends an in silico analysis using variant databases to identify potential variant-contributed off-target sites. For ultra-rare disease programs treating a single patient or patients from a specific genetic ancestry, the guidance suggests this analysis may not always be required with the original IND submission, but sponsors are encouraged to discuss this with FDA early.

On confirmatory testing, the guidance says all nominated off-target sites should ideally be confirmed, but FDA acknowledges sponsors may select a subset with scientific justification. The rationale for subsetting can include statistical cutoffs, editing rate cutoffs, or detection of sites across multiple samples. The guidance warns against overly stringent filtering criteria, meaning FDA wants to see a broad set of sites evaluated even if the final confirmed list is small. This is a practical tension for sponsors: the more conservative your nomination method, the larger the list of sites you need to confirm, which increases costs. The guidance implicitly encourages sponsors to work through this tradeoff explicitly and document their reasoning.

For chromosomal translocation analysis, the guidance requires that GE modalities known to create double-strand breaks have sensitive quantitative NGS-based assessment of chromosomal integrity in edited cells. If confirmed off-target sites are identified, FDA expects an additional analysis evaluating potential translocation events between on-target and off-target sites. The guidance recommends sequencing strategies that minimize bias and use sequencing depth adequate to detect low-frequency translocation events.

What This Means for Investors and Founders

The investment thesis angle here operates on a few different levels. The most direct play is in companies building GE platforms for rare disease indications that previously had no viable commercial pathway because of the small patient population problem. The PMF framework doesn’t make those programs easy, but it makes them viable in a way they weren’t before. Programs that were stuck in a pre-clinical holding pattern waiting for a clearer regulatory path now have one. That’s a catalyst.

For platform companies specifically, the modular product variant logic is a multiplier. If you can get a CRISPR platform approved for one mutation in a given gene and then extend to additional mutations via the plausible mechanism pathway without full re-approval, the per-variant commercial value calculation looks very different. Think about something like a company targeting multiple pathogenic variants in a single gene responsible for a severe pediatric neurological disease. There might be fifty variants across the patient population, each affecting a handful of kids globally. Under the old framework, that’s fifty impossible development programs. Under the new framework, it’s potentially one BLA with fifty variants. The clinical and regulatory work to get the first few variants approved is the hard part. After that, adding variants is primarily a CMC and NGS safety exercise. That’s a dramatically better unit economics model for the platform holder.

The natural history data piece is worth flagging as an investment theme in its own right. The PMF guidance leans heavily on well-characterized natural history as external control. For many ultra-rare diseases, that data doesn’t exist in usable form, or it exists in scattered case reports and small registries that aren’t structured for regulatory use. There’s a real opportunity for companies building natural history study infrastructure and real-world data assets in rare disease to become critical enablers of the PMF pathway. Patient registries, longitudinal outcome tracking, and disease-specific biomarker validation are all assets that become more valuable in a world where natural history data can serve as the control arm for a marketing approval.

The ASO angle also deserves attention. The PMF guidance covers both GE and RNA-based therapies including ASOs, and the ASO case is in some ways more commercially near-term. ASO chemistry for certain chemical classes is well-characterized, the delivery problem for some tissue types is largely solved, and the target identification problem is mostly a sequencing and bioinformatics exercise. For a disease caused by a gain-of-function mutation in a highly expressed gene where the therapeutic strategy is knockdown of the mutant transcript, the PMF framework is almost tailor-made. You have a clear molecular target, a well-understood therapeutic mechanism, and a product class with established safety pharmacology. The main things you need to demonstrate are target engagement, which is often measurable directly from a biomarker, and clinical benefit against natural history. That’s a much shorter development timeline than anything involving a novel small molecule or biologic in a traditional indication.

The Regulatory Arbitrage Angle

This is where it gets interesting for sophisticated investors. The PMF framework and the NGS safety guidance together create a window of regulatory clarity that is temporally valuable. FDA has now published explicit standards, but the competitive landscape for ultra-rare GE and ASO programs hasn’t yet adjusted to those standards. Most of the capital in rare disease right now is still chasing programs that look like traditional drug development, relatively larger patient populations, established endpoints, proven delivery mechanisms. The PMF pathway opens up a class of programs that weren’t viable three years ago and are now genuinely viable, but haven’t yet attracted the capital and attention they will attract once the first approvals come through this pathway and people see it actually work.

The information asymmetry here is real. Reading and understanding two hundred pages of FDA draft guidance is not something most generalist investors do. The people who understand the specific implications for sample selection in ex vivo versus in vivo products, or the manufacturing comparability leverage for GE variants, or the difference in off-target nomination methodology between Cas9 and base editing, are a small community. That community is essentially being handed a regulatory roadmap for a class of assets that the broader market is underpricing.

There’s also a timeline dynamic worth flagging. Both guidances are in draft form and open for public comment, 60 days for the PMF guidance and 90 days for the NGS guidance. The comment periods close later this year. Finalization typically takes another 6-18 months depending on how many substantive comments are received and how much revision is warranted. The practical effect is that sophisticated sponsors are already building programs around the framework regardless of the finalization status, because the draft guidance signals FDA’s current thinking clearly enough to design around it. But the full force of investor attention won’t land until the first approval comes through this pathway, which is probably 2027 or 2028 at the earliest given where most programs are today. That timing gap is the arbitrage window.

Risks and Open Questions

No framework this novel comes without real risks and unresolved questions, and it would be sloppy analysis to leave those out.

The evidentiary standard for the PMF pathway, while clearly articulated in principle, is going to be worked out in practice through the review of specific programs. The guidance is explicit that it doesn’t provide recommendations on specific development programs, endpoints, or approval pathways. Those get resolved through the pre-IND and IND meeting process with the relevant review division. That’s not a problem exactly, but it means there will be program-specific variation in how strictly FDA applies the natural history external control standard and what constitutes adequate confirmation of target engagement. Early programs through this pathway will establish the precedents that define what’s actually required, and those first movers bear more regulatory risk than programs that follow once the playbook is clearer.

The off-target analysis requirements in the NGS guidance are technically demanding and potentially expensive for very early-stage programs. The requirement for biological replicates, the preference for patient-derived cells or cells engineered to harbor the target mutation, the need for confirmatory testing at nominated sites, and the accounting for human genetic variation all add meaningful cost and complexity to the pre-IND package. For a true single-patient program, FDA acknowledges that some of the population genetics analysis may not be necessary, but the core off-target nomination and confirmation work still needs to happen. The guidance encourages early FDA engagement through INTERACT and pre-IND meetings specifically to help sponsors scope these requirements appropriately for their specific product, and that’s genuinely useful advice.

The commercial pathway question also remains open. FDA approving an individualized therapy for a single patient is a remarkable scientific and regulatory achievement, but it doesn’t automatically create a business. Reimbursement for ultra-personalized therapies is genuinely unsolved. Payers have no established framework for valuing a drug with a patient population of one. The manufacturing economics for truly patient-specific products are punishing. The PMF framework is designed to create regulatory viability, not commercial viability, and those are different problems. The more interesting commercial model is probably the modular platform approach described above, where the individualized therapy pathway is used to establish proof of concept for a platform that can ultimately serve larger addressable populations through variant extension.

Finally, it’s worth noting that these are draft guidances, not final rules. The comment period process can result in meaningful changes. Industry will almost certainly push back on specific aspects of the NGS guidance, particularly around the breadth of off-target site confirmation requirements and the population genetics analysis. Academic stakeholders and patient advocacy groups will weigh in on the clinical standards in the PMF guidance. How FDA responds to those comments will matter for exactly how burdensome these pathways are in practice. The directional signal is clear and unlikely to reverse, but the specific parameters will evolve.

None of that changes the fundamental conclusion, which is that this regulatory shift is real, it’s significant, and it’s creating opportunities that a lot of the market hasn’t priced yet. The rare disease genomics space just got a lot more interesting.​​​​​​​​​​​​​​​​

- AbbVie filed suit against HRSA on April 8, 2026 in D.C. federal court, challenging the agency’s 30-year-old definition of “patient” under the 340B Drug Pricing Program

- The lawsuit is the first time a drug manufacturer has gone to court to narrow the statutory meaning of “patient” in 340B, using the post-Chevron legal landscape as its doctrinal weapon

- AbbVie alleges that HRSA’s 1996 guidance allows covered entities to claim 340B discounts on prescriptions for individuals with only cursory or outdated contact with the entity, enabling systemic diversion

- The complaint details specific covered entities (Barrio Comprehensive Family Health Care Center and Mount Sinai Hospital) whose purchasing patterns AbbVie characterizes as grossly anomalous

- 340B discounted purchases hit $81.4 billion in 2024, a 23% year-over-year increase and a compound annual growth rate of 23.5% from 2015 through 2024, now surpassing Medicaid drug spending

- AbbVie proposes a four-part patient definition requiring direct connection between prescription and care, substantive medical encounter, 12-month recency, and direct provider oversight

- If the court sides with AbbVie, hospitals and health systems face tightened eligibility standards, increased audit exposure, and potential repayment obligations on billions of dollars in annual 340B savings

- The case sits at the intersection of post-Loper Bright administrative law, health system economics, and pharma commercial strategy, with investment implications across digital health, health IT, compliance tech, and provider-side services

Table of Contents

1. The Setup: What Actually Happened

2. Why the Patient Definition Matters More Than You Think

3. The Numbers Behind the 340B Explosion

4. Post-Chevron: Why Now and Why This Matters Legally

5. Barrio and Mount Sinai: The Case Studies That Made AbbVie Move

6. What AbbVie Actually Wants the Definition to Be

7. The Genesis Precedent: A Complication

8. Downstream Consequences for Health Systems

9. Investment Implications for Health Tech

The Setup: What Actually Happened

On April 8, 2026, AbbVie filed a 72-page complaint in the U.S. District Court for the District of Columbia against HRSA, HHS, Secretary Kennedy, and HRSA Administrator Thomas Engels. The filing is styled as a request for declaratory and injunctive relief. In plain terms, AbbVie wants a federal judge to tell HRSA that its three-decade-old definition of “patient” under the 340B Drug Pricing Program is wrong, and then order the agency to let AbbVie audit covered entities using a stricter definition. This is not a typical 340B dispute about contract pharmacies or duplicate discounting or reimbursement mechanics. This one goes to the core of how the entire program defines eligibility, and if AbbVie wins, the ripple effects will be enormous.

The immediate trigger was pretty simple. AbbVie submitted audit workplans to HRSA targeting two covered entities whose purchasing behavior looked, to put it mildly, weird. HRSA reviewed those workplans and basically said: your proposed definition of “patient” goes beyond our 1996 guidance, and we will not enforce any findings that come from audits conducted under your definition. AbbVie argued back that the 1996 guidance is not the best reading of the statute. HRSA did not budge. AbbVie then did what large pharma companies do when the administrative process fails them. They sued.

Why the Patient Definition Matters More Than You Think

Here is the foundational problem. The 340B statute says a covered entity cannot resell or transfer a 340B-discounted drug to “a person who is not a patient of the entity.” That phrase does a lot of heavy lifting. But Congress never actually defined “patient.” So in 1996, HRSA issued non-binding guidance laying out what the agency thinks “patient” means. That guidance says an individual is a patient if the covered entity has “established a relationship” with the person such that the entity maintains records of their healthcare, the individual receives care from a professional employed by or contractually tied to the covered entity, and the service is consistent with the entity’s grant funding. That is basically it.

The problem, from AbbVie’s perspective (and honestly from the perspective of anyone who has spent time staring at 340B claims data), is that this definition is shockingly permissive. Under HRSA’s reading, a person who had one ten-minute phone call with a covered entity provider two years ago, about an unrelated condition, and then filled a prescription written by a completely different doctor at a completely different facility, can still be classified as a “patient” of the covered entity. As long as the entity keeps some record of that original phone call and the prescriber has some contractual or referral relationship with the entity, the prescription gets claimed at 340B pricing. The covered entity pockets the spread between the deeply discounted 340B price (sometimes pennies on the dollar) and whatever the insurer or the patient pays at full commercial price. There is no requirement to pass the savings along to the patient. And there is no time limit on how old the relationship can be, at least not one that HRSA has ever formally specified.

This is how a small federally qualified health center in San Antonio ends up with nurse practitioners who are among the top prescribers in the entire country for specific AbbVie immunology drugs, and how 71% of that entity’s 340B purchases end up being dispensed through pharmacies outside of Texas. The math does not make sense unless the patient definition is elastic enough to absorb essentially anyone who has ever touched the entity’s system.

The Numbers Behind the 340B Explosion

The growth trajectory of 340B is genuinely staggering and worth spending a minute on, because it is the backdrop against which this lawsuit makes strategic sense. In 2010, spending on drugs purchased through the program was about $6.6 billion. By 2021 it was $43.9 billion. By 2023 it was $66.3 billion. By 2024 it hit $81.4 billion, a 23% year-over-year increase. The compound annual growth rate from 2015 through 2024 was 23.5%. For context, manufacturers’ net drug sales over the same period grew at roughly 5% annually. The program is now the second-largest federal prescription drug program in the country, behind only Medicare Part D, and it is larger than Medicaid drug spending.

When the program launched in 1992, there were about 1,000 covered entities. By 2021 there were over 50,000. The number of contract pharmacies went from about 1,300 in 2010 to nearly 20,000 by 2017, and by mid-2023 there were more than 33,000 pharmacies with over 194,000 individual contracts. That is a 2,400% increase in contract pharmacy arrangements in 13 years. CBO attributed roughly one-third of the spending growth between 2010 and 2021 to marketwide drug price trends, and the remaining two-thirds to hospital-clinic integration, ACA-driven expansion of eligible entities, and that 2010 HRSA guidance change that let covered entities use unlimited contract pharmacies. A PMC-published study from 2025 decomposed 340B growth from 2018 through 2024 and found that utilization accounted for roughly 80% of growth on a list-price basis, with price increases accounting for only about 17%. In other words, this is not just drug prices going up. It is the program getting bigger because more prescriptions are being funneled through 340B-eligible channels.

The financial incentives are pretty transparent. A covered entity buys a drug at the 340B ceiling price, which can be a 99%+ discount off wholesale acquisition cost for some products. The entity then dispenses or has a contract pharmacy dispense that drug to an insured patient at full commercial price. Nobody is required to share the discount with the patient. GAO survey data from 2018 showed that 45% of covered entities using contract pharmacies admitted they do not pass along any 340B discount to any patient at any of their contract pharmacy locations. The HHS OIG found in 2014 that some contract pharmacies charge uninsured customers the full non-340B price. The entire economic structure of the program has shifted from “help poor people afford drugs” to “generate arbitrage revenue for entities and their pharmacy partners.”

Post-Chevron: Why Now and Why This Matters Legally

The timing of this lawsuit is not a coincidence. In June 2024, the Supreme Court decided Loper Bright Enterprises v. Raimondo, which overturned the Chevron doctrine after 40 years. Under Chevron, courts were supposed to defer to a federal agency’s “reasonable” interpretation of an ambiguous statute that the agency administered. Post-Loper Bright, courts must exercise their own independent judgment about what a statute means. Agency interpretations can still inform the analysis under the surviving Skidmore standard, but they no longer get automatic deference just because the statute is ambiguous and the agency has spoken.

This matters enormously for 340B because the entire patient definition framework rests on HRSA guidance, not on statutory text. Congress did not define “patient.” HRSA filled the gap through non-binding guidance in 1996. That guidance was never promulgated as a formal rule. Under the old Chevron framework, a court might have said: the statute is ambiguous, HRSA’s reading is reasonable, case closed. Post-Loper Bright, the question becomes: what is the best reading of the statutory text? And AbbVie’s argument is that a court doing that analysis from scratch, without deferring to HRSA, should land on a much tighter definition.

The complaint leans into this explicitly. AbbVie cites Loper Bright repeatedly and frames its entire case around the proposition that the court should identify the “single, best meaning” of the statutory term. AbbVie argues that HRSA’s 1996 guidance does not reflect the best reading and instead enables exactly the kind of program abuse that Congress built safeguards against. It is a clean post-Chevron play: the statute is silent, the agency filled the gap, the agency’s gap-filling is no longer entitled to deference, and a court should now determine the correct interpretation independently.

Whether it works is another question. But the legal environment is undeniably more favorable for this kind of challenge than it was two years ago. And AbbVie surely knows that even if this particular case takes years to resolve, the filing itself puts pressure on HRSA to revisit its guidance and signals to other manufacturers that the patient definition is now a live legal issue.

Barrio and Mount Sinai: The Case Studies That Made AbbVie Move

The complaint’s factual allegations are, frankly, a good read if you enjoy stories about regulatory arbitrage and creative interpretations of who qualifies as your patient. AbbVie details two covered entities whose purchasing patterns triggered the audit requests that HRSA eventually rejected.

Barrio Comprehensive Family Health Care Center is a HRSA-funded health center in San Antonio. It is not a large hospital. It is a comparatively small entity. Yet AbbVie’s data showed that Barrio’s 340B purchases of Humira, Skyrizi, and Rinvoq increased over 119% from 2021 to 2022 and over 53% from 2022 to 2023. Barrio was the top purchaser of AbbVie immunology products among all health centers and FQHCs nationwide. And here is the part that really caught AbbVie’s attention: while 80% of 340B purchases by Texas covered entities were dispensed through Texas pharmacies, 71% of Barrio’s purchases went through out-of-state pharmacies. When AbbVie dug in, Barrio explained that it does not require in-person visits. A short phone or video call is enough. An individual is considered a “patient” for 24 months after any medical encounter, and the prescription does not need to be related to whatever happened during that encounter. In Q1 2025, a single nurse practitioner at Barrio wrote approximately 225 prescriptions for Skyrizi 150MG that were claimed as 340B eligible, putting that individual at the very top of all 22,000 prescribers for that drug nationwide.

Mount Sinai Hospital, a DSH in New York City, presented a different flavor of the same problem. AbbVie found that Mount Sinai’s purchasing volume in the first three quarters of 2024 was 35% higher than all of 2023 combined. Claims data showed multiple instances where three separate Mount Sinai-affiliated covered entities submitted the exact same claim for the exact same drug for the exact same patient, with the same date of service, same provider ID, same prescription number, same NDC, and same quantity. The only difference was which Mount Sinai entity was claiming the discount. Mount Sinai told AbbVie it follows HRSA’s patient definition, that prescriptions written within 18 months of an eligible encounter are treated as 340B eligible, and that it does not require a minimum amount of time for a provider to practice at Mount Sinai to be considered a Mount Sinai provider.

What AbbVie Actually Wants the Definition to Be

AbbVie proposed a four-part definition of patient that is considerably narrower than HRSA’s 1996 guidance. Under AbbVie’s reading, someone is a “patient of the entity” only if the prescription was a direct result of a healthcare encounter with a professional who provides services at the covered entity (not a referral from an unrelated provider), the healthcare encounter involved substantive medical care sufficient to meet clinical practice standards for diagnosing and treating the condition for which the drug was prescribed (not a perfunctory ten-minute telehealth check-in), the encounter occurred within 12 months of when the drug was dispensed (not some open-ended lookback window), and the prescribing professional has direct oversight of the patient’s care for the condition being treated, with the covered entity maintaining primary responsibility for managing that care.

AbbVie builds this interpretation from dictionary definitions of “patient” (someone actively receiving medical care from a specific professional), the present tense of the statutory language (indicating a current active relationship), the highly detailed structure of the 340B statute itself (which Congress designed with precision and narrow eligibility categories), and the legislative history showing Congress intended the program to help a narrow set of safety-net providers serve indigent and uninsured populations.

The argument is textually coherent and structurally well-built. Whether a court will buy it is uncertain, but AbbVie clearly did its homework. The complaint is dense with statutory construction analysis and cites the AMA Code of Medical Ethics, multiple dictionary definitions, and a long trail of legislative history to support the proposition that Congress meant something narrower and more rigorous than what HRSA’s 1996 guidance allows.

The Genesis Precedent: A Complication

There is at least one prior case that dealt with the 340B patient definition, though it came from the opposite direction. In 2017, a federally qualified health center called Genesis Health Care sued HRSA because the agency’s audit had found that Genesis improperly diverted 340B drugs by filling prescriptions that did not originate with Genesis providers. Genesis argued the opposite of what AbbVie is arguing: that the definition should be broader, that covered entities should be able to claim 340B discounts even when the prescription came from a different provider, as long as the patient had received services from the covered entity.

Genesis won. In 2023, the U.S. District Court for South Carolina ruled that the statute does not require the covered entity to have initiated the healthcare service resulting in the prescription. The court did say an “ongoing patient relationship” is required, but it did not attach a specific time frame to that requirement. The ruling applied only to Genesis (not nationwide), and it remains to be seen whether the AbbVie court will find that reasoning persuasive or distinguishable. Interestingly, AbbVie did not reference the Genesis case anywhere in its complaint, which is either a deliberate strategic choice or an unusual omission.

Downstream Consequences for Health Systems

If AbbVie wins, even partially, the consequences cascade quickly. Start with compliance risk. Hospitals and health centers that have been operating under HRSA’s 1996 guidance, which is basically everyone, would potentially face retroactive audit exposure under a tighter definition. The complaint specifically seeks to enable AbbVie to audit using its proposed definition, and a court order directing HRSA to authorize and enforce such audits would be precedent-setting.

Then there is the revenue impact. DSH hospitals accounted for roughly $64 billion of the $81.4 billion in 340B purchases in 2024. Much of that spend generates arbitrage revenue. A narrower patient definition shrinks the eligible prescription pool. Fewer eligible prescriptions means fewer 340B purchases means less spread. For large health systems that have built contract pharmacy networks and telehealth referral pipelines to maximize 340B capture, this is a direct hit to operating margins.

Contract pharmacy arrangements get squeezed too. The replenishment model, where a pharmacy dispenses at full price and retroactively claims 340B eligibility, depends on a loose patient definition. Tighten the definition and a meaningful percentage of those retroactive claims fail. The pharmacy still dispensed the drug at full price, but nobody gets to claim the 340B discount on the replenishment purchase.

Telehealth prescribing for 340B purposes also comes under threat. AbbVie’s proposed definition requires “substantive medical care” sufficient for proper diagnosis and treatment. A ten-minute video consult that functions primarily as a patient-capture mechanism for 340B eligibility probably does not meet that standard. This matters because telehealth-driven 340B enrollment has been a growth vector for covered entities, especially FQHCs, since COVID-era flexibilities made virtual visits mainstream.

Investment Implications for Health Tech

For angel investors and entrepreneurs in health tech, this case creates both risk and opportunity across several vectors.

On the risk side, any portfolio company whose revenue model depends materially on 340B economics should be stress-tested against a world where the patient definition narrows. That includes specialty pharmacy platforms, contract pharmacy administrators, 340B third-party administrators, and health systems that use 340B arbitrage to fund uncompensated care or subsidize operating budgets. If the eligible patient pool contracts, volumes drop, and the unit economics of 340B-dependent business models deteriorate. Companies in the 340B TPA space, which includes players that help covered entities identify and claim 340B-eligible prescriptions, would see reduced addressable market. Contract pharmacy networks that have expanded aggressively on the assumption of continued growth may find themselves overbuilt.

On the opportunity side, tighter standards create demand for better compliance infrastructure. If covered entities need to demonstrate that every 340B-eligible prescription meets a four-part test including substantive medical encounter, 12-month recency, direct prescriber oversight, and care management responsibility, that is a data problem. You need systems that can track patient encounters, link prescriptions to specific clinical relationships, verify provider employment or contractual status, and timestamp everything within a defined lookback window. That sounds a lot like a software problem. Health IT companies and compliance-tech startups that can build tooling to help covered entities audit-proof their 340B programs under a stricter standard will find demand. Same for analytics companies that can help health systems model the financial impact of different patient definition scenarios on their 340B revenue.

There is also a secondary effect on value-based care and care management infrastructure. AbbVie’s proposed definition effectively requires that the covered entity maintain ongoing oversight of the patient’s care. That is not just a 340B compliance requirement; it maps to care coordination infrastructure that health systems need for value-based contracts anyway. Companies building longitudinal care management platforms, provider attribution models, and referral management systems might find their products suddenly relevant to a 340B compliance use case they had not been targeting.

The broader strategic question for investors is whether this lawsuit, regardless of its outcome, signals a structural shift in how pharma approaches 340B. If AbbVie is willing to spend the money and political capital to sue HRSA over the patient definition, other manufacturers are watching. The 340B program represents tens of billions in foregone pharma revenue. Any judicial precedent that gives manufacturers a tighter audit framework is going to get used. And every time a manufacturer successfully narrows the scope of 340B, the economics of contract pharmacy arrangements, 340B TPA services, and covered entity operating models shift.

The case is also a reminder that post-Loper Bright, the regulatory floor in healthcare is less stable than it used to be. Agency guidance that has functioned as settled law for decades can now be challenged on first principles. That creates uncertainty, which creates risk, but it also creates arbitrage for teams that can move faster than the regulatory environment. For early-stage health tech companies, the question is whether you are building for the regulatory regime that exists today or the one that might exist in 18 months. The smart money, as always, is on building for both.​​​​​​​​​​​​​​​​

Section 1: The Post-War Hospital Boom and Its Unintended Consequences (Hill-Burton, 1946)

Section 2: The Roemer Effect and the Certificate of Need Era (CON Laws, 1974)

Section 3: Medicare, Medicaid, and the Unleashing of Infinite Demand (1965)

Section 4: Patient Dumping and the EMTALA Surprise (1986)

Section 5: The Self-Referral Problem and the Rise of Pete Stark (Stark Law, 1989-2007)

Section 6: The ACA Slams the Door on Physician-Owned Hospitals (2010)

Section 7: The 340B Drug Discount Rabbit Hole (1992-Present)

Section 8: What This All Means for Investors and Builders

Abstract

This essay traces the regulatory history of U.S. healthcare from 1946 to the present, covering the Hill-Burton Act, Certificate of Need laws, EMTALA, the Stark Law, the ACA’s physician-owned hospital ban, and 340B. Key facts and figures:

- Hill-Burton (1946): $4.6B in grants and $1.5B in loans to roughly 6,800 healthcare facilities across 4,000+ communities

- CON laws: First mandated federally in 1974 via the National Health Planning and Resources Development Act (NHPRDA); federal mandate repealed in 1987; roughly 36 states still have some form of CON today

- EMTALA (1986): Unfunded mandate; uncompensated care represented 55% of emergency room care in 2009 and about 6% of total hospital costs

- Stark Law (1989, expanded 1993/1995): Strict liability statute; civil penalties up to $15,000 per violation, up to $100,000 per circumvention scheme; Adventist Health paid $118.7M (2015), Halifax Hospital $85M (2014)

- ACA (2010): Closed the “whole hospital” Stark exception for new physician-owned hospitals; CBO scored the closure at $500M in deficit reduction over 10 years

- 340B (1992): Originally a small safety-net drug discount program; by 2023, covered entity 340B purchases exceeded $66B/year; disproportionate share hospitals account for nearly $52B of that

The essay argues that each of these regulations was a rational response to a real problem but that collectively they created a locked regulatory architecture that shapes every major investment and go-to-market decision in health tech today. Understanding the causal chain that produced these laws is table-stakes knowledge for anyone deploying capital or building companies in this space.

How the Government Built a Cage Around Healthcare, One Law at a Time

The Post-War Hospital Boom and Its Unintended Consequences

Here is something worth sitting with for a moment. The fundamental architecture of American healthcare regulation, the stuff that determines who can own what, who can refer to whom, which drugs get discounted, and whether a hospital can build a new wing without government permission, was not designed by anyone. It was not the product of a master plan. It was a series of improvised responses to the unintended consequences of the previous improvised response, each one adding another layer of complexity onto a system that was already straining under the weight of the last fix. If that sounds familiar to anyone who has spent time in enterprise software or legacy financial services, the analogy is not accidental.

Start in 1946. World War II just ended, soldiers are coming home, the country is growing fast, and there is a serious shortage of hospital beds, particularly in the rural South and Midwest. More than 40% of the nation’s counties had no hospital at all. President Harry Truman wanted national health insurance, which was politically radioactive at the time. The American Medical Association was furiously lobbying against anything that smelled like socialized medicine. The compromise that emerged was the Hospital Survey and Construction Act, known as Hill-Burton after its Senate sponsors, Lister Hill of Alabama and Harold Burton of Ohio. It was elegant in its simplicity: the federal government would give states grants and loans to build hospitals, and in return those hospitals would serve the public regardless of ability to pay, at least for a “reasonable volume” of care, a term so vague it went basically unenforced for the first 20 years.

The scale of what followed was enormous. Between 1947 and 1971, Hill-Burton dispersed more than $3.7 billion in federal funding matched by $9.1 billion from state and local governments. By 2000, the program had directed over $4.6 billion in grants and another $1.5 billion in loans to roughly 6,800 facilities in over 4,000 communities. Alabama, of all places, created more Hill-Burton hospital beds per capita than any state except Mississippi during the program’s first decade. The South, which was loudly opposed to big government in principle, was quietly the biggest recipient of federal healthcare infrastructure dollars in practice. That tension between stated ideology and actual behavior would become a recurring theme in American health policy.

The “separate but equal” clause embedded in Hill-Burton is also worth a note. Hill, trying to secure Southern votes, added a provision explicitly permitting racially segregated facilities, which was the only time in the 20th century that racial segregation was codified in a federal statute. A federal court struck it down in 1963 and Hill-Burton became, somewhat ironically, a driver of hospital desegregation afterward. Healthcare policy producing outcomes its architects did not intend: a pattern established very early and never really broken.

By the late 1960s, the problem was no longer too few hospital beds. The problem was too many, and too many of them being filled. Researchers had identified what came to be called the Roemer Effect, named after the health economist Milton Roemer, which was basically the observation that hospital bed supply strongly predicts hospital bed utilization. Build the beds, fill the beds. This was not purely a supply-demand phenomenon. Fee-for-service reimbursement meant that hospitals were paid for every service they rendered, and the introduction of Medicare and Medicaid in 1965 injected massive new demand into a system that was already oversupplied. Healthcare spending started climbing in ways that alarmed policymakers, and the question of how to pump the brakes became urgent.

The Roemer Effect and the Certificate of Need Era

The answer Congress arrived at in 1974 was Certificate of Need, or CON, laws. The logic was straightforward even if the execution was not. If you control the supply of healthcare facilities and equipment, you can prevent the kind of duplicative, wasteful overbuilding that was driving up costs. If a hospital wanted to add beds, build a new wing, or buy a major piece of diagnostic equipment above a certain dollar threshold, it had to prove to a state planning agency that the community actually needed it. The National Health Planning and Resources Development Act of 1974 made this mandatory across all states, creating a nationwide network of Health Systems Agencies charged with reviewing and approving capital expenditure proposals. States that did not comply risked losing federal healthcare funding.

The theory was sound enough. The problem was the evidence never really supported it. A 1976 study by Salkever and Bice found that no significant savings in hospital costs had been achieved through CON programs, and their data actually suggested that in the first five states to adopt the laws, costs may have increased, possibly because hospitals responded to bed restrictions by investing in more expensive equipment and technology instead. A 1980 study by Schwartz and Joskow found that duplicative services, which CON was specifically designed to eliminate, were only responsible for a small fraction of the medical cost inflation that had occurred in the preceding decades. Congress, faced with mounting evidence that the whole framework was not working, repealed the NHPRDA in 1987.

But here is the thing. Roughly 36 states kept their CON laws anyway. State health planning bureaucracies had been built up, incumbent hospital systems had discovered that CON was an extraordinarily effective tool for blocking competitors, and the political economy of repeal was unfavorable to anyone who wanted to build something new. CON became, in practice, a regulatory moat. If you were an existing hospital system in a CON state and a competitor wanted to build a new surgical center nearby, you could challenge their CON application, delay the process for years, and raise the cost of entry dramatically. The stated goal was patient welfare. The actual function was incumbent protection. Any venture-backed health services company that has tried to scale in a CON state has encountered this firsthand.

Today the CON landscape is genuinely inconsistent. Some states have eliminated CON almost entirely. Others, particularly in the Southeast, have comprehensive programs covering dozens of services and equipment categories. Florida has CON requirements for some services but not others. Texas eliminated its CON laws for most services in 1985. The result is that if you are building a company that touches facility capacity, your regulatory environment varies dramatically by state in ways that bear essentially no relationship to the actual quality of care delivered in those states.

Medicare, Medicaid, and the Unleashing of Infinite Demand

It would be impossible to understand any of the regulations that follow without understanding what Medicare and Medicaid did to the healthcare cost curve. Before 1965, most Americans either paid out of pocket for healthcare, had private insurance through an employer, or went without. The poor and elderly were largely uninsured. When Lyndon Johnson signed Medicare and Medicaid into law in July 1965 as part of the Social Security Act amendments, the government became the dominant payer in the American healthcare market essentially overnight.

The actuarial estimates at the time were, in retrospect, almost comically wrong. The House Ways and Means Committee projected that Medicare would cost $12 billion by 1990. The actual cost in 1990 was over $110 billion. Healthcare utilization exploded because the marginal cost to patients dropped dramatically, and hospitals, paid on a fee-for-service basis, had strong incentives to deliver more services. The combination of federally funded demand and Roemer-style supply expansion created a feedback loop that has never fully been resolved. Every major regulatory intervention in healthcare from 1966 onward can be understood as an attempt to address consequences, direct or indirect, of those 1965 amendments.

The prospective payment system introduced in 1983, which replaced cost-based Medicare reimbursement with fixed payments by Diagnosis Related Groups, was one such attempt. Instead of paying hospitals whatever they spent on a patient, Medicare would pay a fixed amount based on the diagnosis. This was genuinely transformative and did slow cost growth for a period. But it also created strong incentives for hospitals to discharge patients quickly, sometimes too quickly, and to code diagnoses in ways that maximized reimbursement. It shifted the arms race rather than ending it.

Patient Dumping and the EMTALA Surprise

By the mid-1980s, a different consequence of the fee-for-service and private insurance system was becoming visible in emergency departments, particularly at public hospitals in major cities. Private hospitals were routinely transferring uninsured or Medicaid patients to public hospitals without treating them, sometimes without even stabilizing them. The practice had a name: patient dumping. Cook County Hospital in Chicago documented that 89% of patients transferred to it for financial reasons were minorities, 87% lacked employment, only 6% had consented to the transfer, and 24% arrived in unstable condition. Patients who were transferred were twice as likely to die.

Congress responded in 1986 with the Emergency Medical Treatment and Active Labor Act, EMTALA, tucked inside the Consolidated Omnibus Budget Reconciliation Act. EMTALA required any hospital with an emergency department that accepted Medicare payments, which was virtually every hospital in the country given that Medicare and Medicaid together represented roughly 44% of all medical expenditures, to provide a medical screening examination and stabilizing treatment to anyone who presented, regardless of ability to pay. The law was four pages long and barely noticed at the time.

What no one quite appreciated was that EMTALA was an unfunded mandate. The federal government required the service without providing the funding. By 2009, uncompensated emergency care represented 55% of all emergency room care and about 6% of total hospital costs. The law effectively made emergency departments the safety net of last resort for the uninsured, a role they were never designed to fill and for which they are spectacularly ill-suited from an efficiency standpoint. Some 1,200 hospitals eventually closed their emergency departments in part because of the financial pressure. The costs that hospitals could not absorb were passed on to insured patients through higher prices, which drove up insurance premiums, which increased the number of uninsured, which increased EMTALA utilization. Another feedback loop.

For health tech investors, EMTALA is not usually the regulation that comes up first in board discussions. But it is the silent backdrop to enormous portions of the market. The entire emergency care management software space, the complex billing reconciliation problem around uncompensated care, the strategic behavior of hospital systems around emergency department capacity, and the pressure on safety net hospitals are all downstream of a four-page law that Ronald Reagan signed without fanfare in 1986.

The Self-Referral Problem and the Rise of Pete Stark

By the late 1980s, it had become increasingly clear that doctors who owned financial stakes in facilities to which they referred patients were referring more patients to those facilities. This is not surprising in retrospect. If a physician owns a piece of a lab, they order more labs. If a physician has an ownership stake in an imaging center, they order more imaging. Studies of physician referral patterns showed this effect clearly, and it was costing the Medicare program real money. Enter Pete Stark, a Democratic congressman from California with a reputation for aggressive healthcare policy positions and a genuine talent for making the medical community uncomfortable.

Stark I, passed in 1989 as part of the Omnibus Budget Reconciliation Act, prohibited physicians from referring Medicare patients for clinical laboratory services to any entity in which the physician or an immediate family member had a financial interest. It was relatively narrow and reasonably well-received, in part because clinical labs were a specific and documented problem area. Stark II came in 1993, expanding the prohibition to a much broader list of “designated health services,” including physical therapy, occupational therapy, radiology, radiation therapy, durable medical equipment, home health services, outpatient prescription drugs, and inpatient and outpatient hospital services itself. Medicaid patients were added to the covered population. The medical community pushed back hard, arguing that this was government intrusion into clinical practice. Stark III followed in 2007, largely clarifying and tightening the exceptions framework.

The Stark Law, as it exists today, is a strict liability statute, meaning that intent does not matter. A physician who inadvertently refers a Medicare patient to an entity in which a family member has a financial interest has violated the law even if they had no idea the interest existed and derived no personal benefit. The penalties are serious: up to $15,000 per service provided in violation, up to $100,000 per circumvention scheme, and exclusion from Medicare and Medicaid participation, which is a potential death sentence for a physician’s practice. Settlements have been enormous. Adventist Health System paid $118.7 million in 2015. Halifax Hospital Medical Center paid $85 million in 2014. Wheeling Hospital paid $50 million in 2020.

The strict liability aspect of Stark created a compliance industry unto itself. Hospitals and health systems spend tens of millions of dollars annually on Stark compliance programs, physician compensation analyses, and fair market value assessments, all designed to ensure that physician compensation arrangements are not influenced by the volume or value of referrals. CMS itself acknowledged in a 2020 rulemaking that “ambiguities in the Stark law have frozen many providers in place, fearful that even beneficial arrangements might violate the law.” The 2020 rulemaking attempt to add exceptions for value-based care arrangements was specifically motivated by the observation that Stark was making it harder, not easier, to build coordinated care models.

For investors and founders, Stark is the regulatory context for almost every deal structure that involves physicians and facilities. It is why physician-owned specialty hospitals look different from general hospitals. It is why physician employment arrangements at health systems are structured the way they are. It is why the contractual scaffolding around any technology company that sits between a physician and a referral destination needs very careful legal review. The law is trying to solve a real problem, which is that physicians have a genuine conflict of interest when they own the facilities to which they refer, but it does so in a way that creates enormous transaction costs for everyone.

The ACA Slams the Door on Physician-Owned Hospitals

The Stark Law had always contained what was called the “whole hospital exception,” which allowed physicians to refer patients to a hospital in which they had an ownership interest, as long as the ownership was in the entire hospital rather than just in a specific service line. This exception had enabled the growth of physician-owned hospitals, which were typically focused on highly profitable service lines like orthopedics, cardiac care, and surgery, and which competed with general community hospitals for the most profitable patients and procedures while avoiding the money-losing ones.

The hospital lobby, led by the American Hospital Association, had fought physician-owned hospitals for years on the grounds that they cherry-picked the healthiest and most profitable patients, leaving community hospitals stuck with the sicker, poorer, and more complex cases. The evidence supported this critique. The Government Accountability Office, CMS, and the Medicare Payment Advisory Commission all found that physician-owned hospitals’ patients tended to be healthier than patients with the same diagnoses at general hospitals. MedPAC and GAO found that physician-owned hospitals treated significantly fewer Medicaid patients. By directing high-margin cases to physician-owned facilities while relying on publicly funded emergency services for complex or uncompensated cases, physician-owned hospitals were, depending on your politics, either an efficient market innovation or an elaborate free-rider strategy.

The Affordable Care Act in 2010 resolved this debate by simply closing the whole hospital exception for new physician-owned hospitals. Any physician-owned hospital that did not have a Medicare provider number as of December 31, 2010 could not be created under the exception. Existing facilities were grandfathered but subject to strict disclosure requirements, patient safety rules, and growth restrictions. The CBO scored the closure of this exception at $500 million in deficit reduction over 10 years. The door was closed, and it has stayed closed despite repeated attempts by legislators to reopen it, including the Patient Access to Higher Quality Health Care Act of 2023.

This is the proximate answer to the question posed by this essay’s title. Doctors are not “banned” from owning hospitals in any sweeping categorical sense. What happened is that the one legal mechanism that had allowed them to do so at scale, the whole hospital Stark exception, was eliminated by the ACA. Pre-2010 physician-owned hospitals still exist and still operate. New ones cannot be created for Medicare-participating physicians who want to refer patients there. The distinction matters, especially for anyone who has heard “doctors can’t own hospitals” in a pitch meeting and wondered if that was technically accurate. It is mostly accurate for practical purposes, even if the legal reality is more nuanced.

The 340B Drug Discount Rabbit Hole

The 340B Drug Pricing Program is one of the more quietly consequential regulations in the healthcare system and one that most people outside of hospital finance and pharma market access teams did not pay much attention to until it started generating $66 billion in annual spending. It began modestly. Congress created it in 1992 through the Veteran’s Health Care Act, signed by George H.W. Bush, as a response to an unintended consequence of the 1990 Medicaid Drug Rebate Program. The Medicaid rebate program required drug manufacturers to give Medicaid programs their best price. That sounds fine, but the best price rule meant that manufacturers, worried about triggering lower Medicaid prices, stopped giving safety net hospitals the informal deep discounts they had previously enjoyed. Safety net hospitals suddenly lost access to affordable drugs for their poorest patients.

340B fixed this by requiring drug manufacturers who participate in Medicaid to also offer outpatient drugs to “covered entities,” primarily safety net hospitals and federally qualified health centers, at discounted ceiling prices. The spread between the discounted acquisition cost and the reimbursement rate the entity receives from insurers or Medicare when it dispenses those drugs flows back to the covered entity, subsidizing operations and services for underserved populations. That is the stated theory. The 340B ceiling price typically represents discounts of 20% to 50% off the average manufacturer price.

The program was small for years. Then the ACA in 2010 massively expanded the list of eligible covered entities to include children’s hospitals, cancer treatment facilities, critical access hospitals, rural referral centers, and sole community hospitals. Hospital participation tripled. By 2021, total program sales reached approximately $44 billion, a 15% increase over 2020. By 2023, covered entity 340B purchases exceeded $66 billion per year, with disproportionate share hospitals alone accounting for nearly $52 billion. HRSA estimates that 340B sales constitute roughly 7.2% of the entire U.S. drug market.

The program’s explosive growth attracted scrutiny from all directions. Senator Charles Grassley, reviewing the data, documented hospitals profiting from the 340B program by purchasing drugs at the 340B discount and then dispensing them to Medicare and privately insured patients at full reimbursement rates, keeping the spread. The program’s integrity problems are structural: there are no requirements on how 340B revenue is used, most hospitals have minimal reporting obligations, and HRSA has limited regulatory authority to enforce the program’s original intent. Drug manufacturers responded by trying to restrict 340B discounts to in-house pharmacies rather than contract pharmacies, triggering years of litigation that continued through 2024. The Supreme Court weighed in with a 2022 decision in American Hospital Association v. Becerra that limited HHS’s ability to reduce Medicare reimbursement for 340B drugs without further process.

For health tech investors, 340B is operationally relevant because it affects drug procurement strategy for any company touching hospital pharmacy or outpatient drug dispensing. It is financially material to the unit economics of any business that touches covered entity revenue. And it is a compliance minefield for anyone building in contract pharmacy, specialty pharmacy, or hospital revenue cycle. The gap between the program’s stated purpose and its current function is wide enough to drive a fairly large vehicle through.

What This All Means for Investors and Builders

All of the above is more than a history lesson, though it is that too. What it actually is, taken as a whole, is the operating manual for why healthcare looks the way it does today. The ownership restrictions, the referral rules, the facility licensing requirements, the drug pricing architecture, the emergency care mandates, none of these emerged from first principles. They are the sedimentary layers of a system that has been repeatedly patched at the point of failure without ever being redesigned from the foundation.

For investors, the key insight is that regulation in healthcare is not fundamentally about preventing harm, though that is the stated rationale for all of it. It is about managing externalities in a market where the normal price signals do not work. Patients are not rational consumers of healthcare in most circumstances. Physicians have information advantages that create massive agency problems. Payers are separated from providers by layers of intermediary structure that would horrify anyone trained in microeconomics. When you add government as a dominant payer, the distortions compound. The regulations described above are attempts to constrain those distortions. They succeed partially and create new distortions in the process.

The practical consequence is that every major category of health tech investment is shaped by at least one of these regulatory layers. Physician-facing software lives inside the Stark compliance reality. Hospital technology purchasing is filtered through CON constraints in many states. Drug-adjacent businesses have to navigate 340B. Any company that touches emergency care is working with the cost structure created by EMTALA. Revenue cycle and billing infrastructure is shaped by all of it simultaneously. This is not a complaint. It is an observation. Markets with regulatory complexity are markets where information asymmetry is high, switching costs are elevated, and the companies that build durable workflows around compliance requirements tend to generate defensible positions that are hard for new entrants to replicate.

The other thing worth saying is that the regulatory architecture is not static. CON laws are being challenged and repealed in more states. The Stark exceptions for value-based care are being expanded. 340B is heading toward a more adversarial manufacturer relationship that could materially change program economics for covered entities. EMTALA faces legal pressure in the context of post-Dobbs abortion restriction conflicts. The rate of change in the underlying regulatory framework is accelerating, which creates both risk and opportunity for anyone building in this space. Understanding where the laws came from, what problems they were originally trying to solve, and what constituencies they now serve is the prerequisite for having any informed view about where they are going.

The cage was not built all at once. It was assembled incrementally, one improvised fix at a time, by legislators and regulators responding to real problems with imperfect tools under political constraints. The result is a system that is genuinely difficult to navigate, genuinely resistant to disruption in places, and genuinely in need of the kind of technological and organizational innovation that is the whole point of the health tech investment thesis. The map of how it got this way is worth having before you start trying to figure out how to change it.​​​​​​​​​​​​​​​​

Who absorbs liability when an AI-assisted clinical decision causes patient harm, and what does the current legal ambiguity mean for founders, investors, and health systems deploying these tools at scale?

Key findings and data points:

- FDA has cleared over 1,300 AI-enabled medical devices as of late 2025, with 295 cleared in 2025 alone; 62% are software as a medical device (SaMD)

- 66% of U.S. physicians used AI in clinical practice in 2024, up from 38% in 2023 – a 78% single-year jump

- Malpractice claims involving AI tools increased 14% between 2022 and 2024; most involved diagnostic AI in radiology, cardiology, and oncology

- No existing federal law assigns liability to AI developers when an AI tool contributes to patient harm; current courts default to the treating physician

- The Federation of State Medical Boards recommended in April 2024 that clinicians, not vendors, be held liable for AI-generated errors

- GPT-4 outperformed physicians using GPT-4 in complex diagnostic cases in a 2024 study, suggesting the human-AI hybrid may actually underperform pure-AI in some contexts

- AI healthcare market estimated at $39.25B in 2025, projected to reach $504B by 2032 at ~44% CAGR

- AI-focused digital health deals in H1 2025 ran 83% larger than non-AI deals; $3.95B of the $6.4B raised went to AI companies

- Only 2% of U.S. radiology practices had integrated AI reading tools by 2024 despite hundreds of FDA-cleared options

The argument: The AI diagnostics revolution is outrunning the legal infrastructure designed to govern it. Physicians bear all the liability, vendors bear almost none, and the incentive structures this creates are deeply misaligned with both patient safety and long-term enterprise value for companies in the space. The founders who figure this out early – and build accountability into their products rather than contract it away – will have a significant structural advantage.

Table of Contents

The Setup: 950 Cleared Devices, 2% Adoption, and a Liability Cliff Nobody’s Talking About

What the FDA Has and Hasn’t Done

The Physician Gets the Bill: How Malpractice Law Currently Assigns Blame

Deskilling, Overdependence, and the Colonoscopy Problem

The Black Box Defense and Why It Won’t Hold

What the EU Is Doing That the U.S. Isn’t

Founder Implications: Build the Accountability Layer Now

Investment Thesis: The Liability Arbitrage Window Is Closing

The Setup: 950 Cleared Devices, 2% Adoption, and a Liability Cliff Nobody’s Talking About

Here’s a number that should make every health tech founder pause: as of late 2025, the FDA has cleared more than 1,300 AI-enabled medical devices. Radiology alone accounts for the overwhelming majority. And yet, a 2024 Associated Press survey found that only about 2% of U.S. radiology practices had actually integrated AI reading tools into their workflows. Think about that gap for a second. It’s not a regulatory problem. It’s not a technical problem. The tools exist, they’re cleared, and in many cases the clinical evidence behind them is legitimately strong. A large Swedish randomized trial found 17.6% higher cancer detection rates with AI-assisted mammography screening. Viz.ai’s stroke detection algorithm hits AUC above 0.90 on retrospective datasets. Aidoc’s intracranial hemorrhage tool reports sensitivity above 90% with low false-positive rates. The performance is real.

So what’s the holdup? Some of it is workflow friction, integration headaches, and the usual institutional inertia that makes healthcare adoption timelines look like geological epochs. But a big, underappreciated chunk of it is liability terror. Clinicians are watching a legal landscape where they get to absorb all the downside of an AI error, while the vendor takes the upside in the form of a recurring SaaS contract. That is a structurally bad deal, and the smarter physicians are figuring that out fast.

The broader picture is even more interesting. U.S. digital health startups raised $6.4B in H1 2025, with AI-focused companies capturing roughly $3.95B of that, and those deals ran 83% larger than non-AI deals on average. Eight healthcare AI unicorns were minted in 2025. The money is flowing in fast and hard. Meanwhile, the legal infrastructure governing what happens when one of these tools contributes to a missed diagnosis or a treatment error is essentially non-existent in any coherent, codified sense. There is no federal statute. There is almost no AI-specific case law. There are strongly worded recommendations from medical boards telling physicians they’re on the hook, and there are vendor contracts full of indemnification language that makes the physician’s counsel wince. That combination – massive capital inflows, real clinical deployment, and a liability vacuum – is the setup for a genuinely consequential legal and market reckoning.

What the FDA Has and Hasn’t Done

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- The Setup: What Is This Bill and Why Does It Matter Right Now

- How We Got Here: A Very Brief, Very Grim History of Vertical Integration

- What the Bill Actually Does (In Plain English)

- The Divestiture Math: Assets Looking for Owners

- Opportunity Map Part 1: The Independent Provider Stack

- Opportunity Map Part 2: PBM Alternatives and Drug Distribution

- Opportunity Map Part 3: MSO Infrastructure

- What the Bears Get Right

- How to Think About Timing and Passage Probability

- Closing Take

Abstract

Introduced February 10, 2026 by Sens. Elizabeth Warren (D-MA) and Josh Hawley (R-MO), the Break Up Big Medicine Act is the most significant structural health policy proposal since the ACA. It is explicitly modeled on the Glass-Steagall Act, which separated commercial and investment banking after the 1929 crash.

Key provisions:

- Prohibits common ownership of a medical provider or MSO and an insurer or PBM

- Prohibits wholesale drug distributors (think McKesson, Cencora, Cardinal Health) from owning medical provider organizations

- One-year compliance window for violations

- Automatic disgorgement of profits and forced asset sales for non-compliance

- FTC, DOJ, HHS, state AGs, and private citizens all have standing to sue

- FTC and DOJ can review and block future actions that would recreate banned structures

Companies immediately affected: UnitedHealth Group (owns Optum, 2,000+ provider orgs, 10% of US physician workforce), CVS Health (Aetna, Caremark, Oak Street), Cigna/Evernorth (Express Scripts), Elevance Health (Carelon), McKesson, Cencora, Cardinal Health.

Scale of concentrated market power:

- Three PBMs process 79-80% of all US prescription drug claims for ~270M Americans

- Three drug wholesalers control 98% of US drug distribution

- Nearly 80% of US physicians now work for a corporate parent

- ~4,000 independent pharmacies have closed since 2019

- Healthcare spending approaching 20% of GDP, over $14,500/person/year

- FTC found Big Three PBMs paid affiliated pharmacies up to 7,736% more than unaffiliated competitors

- UnitedHealth pays affiliated providers 17% more on average, up to 61% more in markets where it holds at least 25% share

Bipartisan momentum: Warren-Hawley previously teamed up on PBM legislation in 2024. The bill has House co-sponsors across both parties including a physician (Murphy, R-NC) and a pharmacist (Harshbarger, R-TN). New PBM regulations were included in the recent appropriations package signed by President Trump. As of January 2026, over 77,000 Americans have signed onto the Break Up Big Medicine initiative.

Investment thesis in brief: Even if this bill does not pass in its current form, it is already reshaping the competitive landscape for health tech. The arc of regulation is bending toward structural separation, and the assets being shed or stranded create multi-billion dollar whitespace for founders and capital.

The Setup: What Is This Bill and Why Does It Matter Right Now

There is something almost funny about Elizabeth Warren and Josh Hawley teaming up on anything. These two senators occupy basically opposite corners of the American political universe. Warren has spent her career going after Wall Street; Hawley built his brand by going after Big Tech. The fact that they found each other on healthcare should tell investors something important: the political risk calculus on vertical integration in health has flipped. When the populist right and the progressive left converge, the corporate center gets squeezed. That is not a partisan observation, it is just how legislative momentum works.

The Break Up Big Medicine Act dropped on February 10, 2026, and the concept it rests on is dead simple. One company should not be on both the payer side and the provider side of the same healthcare transaction. The bill draws an explicit parallel to Glass-Steagall, the 1932 Depression-era law that forced commercial banks to separate from investment banking. The analogy is more apt than it sounds. In both cases, you had giant institutions that were essentially judging their own homework, self-dealing through affiliated entities, gaming regulations designed to protect the public, and doing it all at scale under a veneer of market efficiency. The banking sector did not fix that voluntarily, and healthcare will not either.

The sheer scale of what these companies have assembled is worth sitting with for a second. UnitedHealth Group is simultaneously the country’s largest insurer, its largest private physician employer, its largest claims clearinghouse (via Change Healthcare), and the third-largest PBM through Optum Rx. It has contractual or employment ties to roughly 10% of the entire US physician workforce. CVS owns Aetna, Caremark, and now runs Oak Street Health, a primary care chain. Cigna’s Evernorth division houses Express Scripts, Accredo specialty pharmacy, and behavioral health services. Together, Caremark, OptumRx, and Express Scripts process somewhere around 79-80% of all US prescription drug claims for approximately 270 million Americans. On the distribution side, McKesson, Cencora, and Cardinal Health collectively control 98% of drug distribution in this country, and McKesson has quietly become the largest owner of community oncology clinics in the US while also being the distributor shipping drugs to those same clinics.

These are not coincidences or organic outcomes of competitive markets. They are the product of roughly two decades of acquisitions that regulators mostly waved through, and the resulting conflict of interest is structural, not incidental. When you own the insurer, the PBM, the pharmacy, and the doctor’s office, you are not a healthcare company, you are a toll booth at every single intersection of the patient journey.

How We Got Here: A Very Brief, Very Grim History of Vertical Integration

Read more

Source Rule: CMS-9883-P, HHS Notice of Benefit and Payment Parameters for 2027 (2027 Payment Notice Proposed Rule)

Comment Deadline: March 11, 2026

Codification: 42 CFR Part 600; 45 CFR Parts 153, 154, 155, 156, 158

Key provisions covered in this essay:

- State Exchange Enhanced Direct Enrollment (SBE-EDE) option

- Non-network QHP certification

- Standardized plan option repeal

- Non-standardized plan option limit removal

- ECP threshold reduction (35% to 20%)

- Multi-year catastrophic plan terms (up to 10 years)

- Bronze plan cost-sharing restructuring

- SEIPM improper payment program for state exchanges

- SEP pre-enrollment verification (75% threshold)

- Agent/broker marketing crackdown and consent standardization

- HHS-RADV methodology updates

- MLR standard comment solicitation

Who should care:

- Founders building in ACA marketplace infrastructure

- Investors with portfolio exposure to payer tech, benefits navigation, enrollment tech, or direct primary care

- Anyone building in the commercial insurance adjacent space who keeps confusing “the market” with “Medicare” (it’s not just Medicare, promise)

Table of Contents

Why this rule matters more than the usual CMS boilerplate

The SBE-EDE option and what it does to distribution tech

Non-network plans get QHP certification: the direct care play

Plan design liberation: standardized options out, creativity in

ECP threshold cut and what that means for safety-net adjacent tech

Multi-year catastrophic plans: the chronic care engagement opportunity

Enrollment integrity crackdown: compliance tech gets its moment

Risk adjustment updates and the data infrastructure angle

The MLR wildcard: what CMS is actually signaling

How to think about the opportunity stack as an investor or founder

Why this rule matters more than the usual CMS boilerplate

Every year CMS drops a payment notice and every year most people in health tech skim the press release, nod along to the risk adjustment user fee update, and move on with their lives. The 2027 Payment Notice proposed rule is different, and not in a performative “this one changes everything” kind of way. It is different because several provisions in it represent a genuine, structural shift in how individual market insurance is designed, distributed, and administered, which creates real greenfield for entrepreneurs who are paying attention.

The backstory here matters. The current administration came into office with a deregulatory posture, and the Working Families Tax Cut legislation (Pub. L. 119-21), enacted July 4, 2025, functionally reshaped who gets subsidized on the ACA exchanges. The 2027 rule is largely a downstream codification exercise implementing WFTC’s eligibility restrictions, but it is also using that vehicle to push through a bunch of structural changes that are not just technocratic cleanup. Non-network QHP certification, the SBE Enhanced Direct Enrollment option, the repeal of standardized plan requirements, the multi-year catastrophic plan framework, the ECP threshold reduction from 35 to 20 percent – these are real product market implications.

The individual market is currently covering somewhere north of 21 million people through ACA exchanges, with that figure having grown significantly over the enhanced APTC period that began under the American Rescue Plan. The WFTC changes will compress eligibility somewhat, particularly by removing APTC access for undocumented immigrants and certain lawfully present noncitizens below 100 percent FPL. But the market is still enormous, still growing in certain demographics, and still deeply underserved by modern product design. That context is the backdrop against which every provision discussed below should be read.

The SBE-EDE option and what it does to distribution tech

The State Exchange Enhanced Direct Enrollment option is probably the single most interesting provision in this rule from a distribution technology standpoint. Here is what it actually does: it allows a state-based exchange to forgo operating its own consumer-facing eligibility and enrollment website entirely, and instead route everything through HHS-approved web brokers. Read that again. A state exchange could become functionally invisible to the end consumer, with the entire enrollment experience running through a private sector web interface.

This builds on existing Enhanced Direct Enrollment infrastructure that already exists in the federal exchange context, but it dramatically expands the surface area of private sector involvement. Today the EDE model allows web brokers to offer an enrollment experience as an alternative to [healthcare.gov](http://healthcare.gov) in FFE states. The SBE-EDE proposal would allow states to make that the only experience. No state portal. Just web brokers.

For founders building in enrollment technology, this is significant. The incumbent enrollment tech landscape is dominated by a handful of legacy platforms that were built for the pre-EDE world and have been retrofitting to accommodate modern UX. A market where states are actively choosing to fully privatize the enrollment interface is a market where new distribution technology can actually gain traction against entrenched players without having to fight through state procurement. The barrier to winning in this space shifts from government contract relationships toward consumer experience quality and data fidelity, which is a far more favorable battlefield for startups.

Simultaneously, the rule proposes tighter standards of conduct for agents, brokers, and web brokers, including mandating HHS-approved consent forms, prohibiting cash inducements, and cracking down on misleading marketing around zero-dollar premiums. This is not incidental. CMS has been dealing with a wave of fraudulent enrollment activity, and the enhanced distribution pathway comes paired with enhanced compliance infrastructure. That creates a two-sided opportunity: build the distribution UX, and build the compliance infrastructure underneath it. Both are real businesses.

The vendor training program is also being sunset under this rule, with agents and brokers moving to direct access through the Marketplace Learning Management System. This further decentralizes the training ecosystem and opens surface area for new education and credentialing products in the broker enablement space.

Non-network plans get QHP certification: the direct care play

This is the provision that should have every DPC founder and DPC-adjacent investor paying close attention. CMS is proposing to allow non-network plans to receive QHP certification beginning in plan year 2027. That is a structural change, not a marginal one.

Currently, every QHP on an exchange has to use a contracted provider network. That is the foundational assumption baked into how plan design, network adequacy review, ECP contracting, and essentially the entire regulatory scaffold of exchange certification works. Removing that requirement, even for a subset of plans, rewrites the rules of engagement for how care delivery and insurance can intersect.

Under the proposed framework, a non-network plan would instead need to demonstrate “sufficient choice of providers that accept the plan’s benefit amount as payment in full.” The plan sets a benefit amount. Consumers can go to any provider who accepts that amount. CMS frames this as a price transparency and competition play – let consumers shop, let providers compete on price, let the market clear. That is very much the current administration’s ideological lane.

The commercial analogy here is the reference-based pricing model that has gotten traction in self-funded employer markets over the last decade. Companies like MultiPlan, Zelis, and others have built significant businesses helping payers execute on non-network or reference-based payment logic. Now that framework has a potential pathway into the individual exchange market, which was previously off-limits for this approach. That is a new market.

For DPC operators specifically, this is worth watching carefully. A QHP designed around a reference-based payment structure, paired with a DPC membership as the primary care layer, could clear a much simpler regulatory path than has historically existed. The DPC world has been largely orthogonal to the exchange market because DPC memberships are not insurance and the exchange market required comprehensive QHP coverage. If non-network QHPs can be certified, and if those plans are designed to pair with direct care relationships for primary care functions, you start to see a path toward a bundled product that could actually clear QHP certification. That is not trivially easy to build and it would face meaningful pushback from the insurance and hospital lobbies, but the regulatory door is now at least ajar in a way it was not before.

Plan design liberation: standardized options out, creativity in

The repeal of standardized plan option requirements is getting less attention than it deserves. Since 2022, CMS has required issuers on federal exchange states to offer standardized plan options at each metal level, with specific cost-sharing structures dictated by CMS. This was a consumer protection and simplicity measure – fewer, more comparable plans make for easier shopping. It also was a meaningful constraint on product design innovation.

The 2027 rule proposes to eliminate the standardized plan requirement entirely. It also proposes to eliminate the cap on non-standardized plan options. Previously, issuers were limited in how many non-standardized variants they could offer, with an exceptions process for chronic and high-cost condition plans. All of that goes away under this proposal.

From a payer technology standpoint, this opens up significant design space. Actuarial teams at regional carriers, CO-OPs, and insurtech companies will now have more freedom to build products tailored to specific population segments, condition-specific cost sharing structures, or value-based benefit designs. The flip side is that more plan complexity typically means consumers need more help navigating it, which means the guidance and decision support layer of the market gets more valuable.

A few specific builds become more interesting here. Condition-specific plan designs that have been constrained by the standardized option framework can now be brought to market with more creative cost-sharing structures. Think about an insurer that wants to offer a plan with zero cost-sharing for GLP-1 medications combined with structured lifestyle intervention, building the incentive for adherence into the plan design itself. The standardized option framework made that kind of targeted design hard to execute cleanly at the exchange level. Without that constraint, the design space gets genuinely interesting.

The decision support technology angle is also worth flagging. When every plan looks similar because of standardization requirements, choosing among them is still hard but not dramatically differentiated by condition-specific factors. In a world with more plan variation, consumers with specific chronic conditions, specific utilization patterns, or specific preferred provider relationships need significantly better decision support tools. The benefits navigation and plan selection space – already populated by companies like Picwell, ALEX, and similar players – gets structurally more important in a less standardized market.

ECP threshold cut and what that means for safety-net adjacent tech

The reduction in essential community provider contracting thresholds from 35 percent to 20 percent is a somewhat technical provision that has real downstream consequences. ECPs are providers who disproportionately serve low-income and medically underserved populations – FQHCs, family planning providers, Ryan White HIV clinics, and similar organizations. The current rule requires QHP issuers to contract with at least 35 percent of ECPs in their service area, with separate sub-thresholds for FQHCs and family planning providers at the same 35 percent minimum.

Dropping that to 20 percent gives issuers more flexibility and less administrative burden around ECP contracting, but it also means that exchange plans are structurally less obligated to maintain deep relationships with safety net providers. That shifts the connectivity burden. If a large portion of a health plan’s membership historically accessing care through FQHCs and similar facilities is on an exchange plan that now has weaker contractual threads to those facilities, you get care fragmentation and care coordination challenges at the population level.

For health tech founders building in the community health center or FQHC space, this is relevant in two ways. First, FQHCs and similar providers need better infrastructure to maintain patient relationships through insurance transitions as plan networks become more variable. The care continuity and panel management challenge at FQHCs is already significant, and it gets more complicated if exchange plan network participation becomes more volatile. Second, the data sharing and attribution infrastructure between payers and safety net providers becomes more important precisely when the contractual foundation for that relationship gets weaker. Startups helping ECPs with payer contracting negotiation, credentialing, and claims optimization have a bigger value proposition in a world where those relationships are harder to maintain.

There is also a FQHC network aggregation play here. Several companies have been building aggregated FQHC network models for payer contracting, essentially doing for safety net providers what physician practice management companies have done for independent physicians. Lower ECP thresholds combined with more plan design flexibility creates a market where an aggregated FQHC network could be a meaningful asset for an insurer trying to cost-effectively hit even the lower 20 percent threshold while maintaining some community health credibility.

Multi-year catastrophic plans: the chronic care engagement opportunity

The multi-year catastrophic plan proposal is genuinely novel and probably the most underappreciated provision in the rule. CMS is proposing to allow catastrophic plan terms of up to 10 consecutive years. That is not a typo. Ten years. And embedded within that structure are some mechanics that are worth understanding in detail.

Multi-year catastrophic plans under the proposal would be allowed to offer value-based insurance design benefits for preventive services before the deductible is met. They would also be allowed to pool cost-sharing limits across the full term of the plan, rather than resetting annually. An issuer could structure a 10-year catastrophic plan where the annual cost-sharing limits are effectively spread as a monthly figure across the contract lifetime. CMS also proposes allowing issuers to make plan-level index rate adjustments for multi-year contracts, which gives actuarial flexibility to price a long-term enrollment commitment differently than a single-year contract.

The population this provision targets is the relatively young, healthy, income-constrained individual who qualifies for catastrophic coverage – essentially under-30 or over-30 with a hardship or affordability exemption. These are people who are chronically under-enrolled or cyclically churning through insurance coverage. A 10-year plan with embedded preventive service coverage before the deductible creates something that functionally resembles a long-term engagement product rather than a one-year transactional insurance purchase.

From a health tech standpoint, this is an engagement infrastructure opportunity. If an insurer is writing a 10-year catastrophic plan, the actuarial logic of investing in prevention and chronic disease interception changes dramatically. A single-year plan has almost no actuarial incentive to invest in behavioral change or early chronic disease management because the odds of retaining the member long enough to see the cost offset are low. A 10-year contract changes that math entirely. Suddenly it makes sense to build longitudinal engagement infrastructure, invest in remote monitoring, deploy condition management programs, and generally treat the member as a long-term relationship rather than a short-term cost exposure.

Companies building in the health engagement, remote patient monitoring, or chronic disease management space should be thinking about what a product looks like designed specifically for the long-term catastrophic plan population. That population has been historically hard to build for because the insurance infrastructure did not support multi-year relationships. This rule starts to create that infrastructure.

Enrollment integrity crackdown: compliance tech gets its moment

There is a compliance and fraud prevention theme running through this entire rule that deserves its own focused discussion. CMS is not being subtle about the fact that they believe the current enrollment ecosystem has significant improper enrollment problems, and the 2027 rule is deploying multiple mechanisms simultaneously to address it.

The pre-enrollment SEP verification requirement is back, reproposed after being stayed by a federal district court following its finalization in June 2025. The new version would require Exchanges on the federal platform to conduct pre-enrollment verification for at least 75 percent of new enrollments through special enrollment periods, not just loss of minimum essential coverage SEPs. The goal is to prevent unauthorized plan switching and enrollment fraud, which has been a documented problem in the enhanced APTC environment. When subsidies are generous, the incentive for brokers or consumers to game SEP eligibility increases.

The mandatory HHS-approved consent forms and eligibility application review forms are another piece of this. Rather than allowing brokers to use their own templates, everyone moves to a standardized federal form. This sounds mundane but it is actually a meaningful data standardization event. Standardized consent documentation creates a uniform data trail that is much easier to audit algorithmically than the current patchwork of broker-specific forms.

The State Exchange Improper Payment Measurement program is also in this rule, establishing a structured process for measuring APTC improper payments at state-based exchanges, mirroring what already exists at the federal exchange. State exchanges have historically not been subject to the same improper payment measurement infrastructure as the federal platform, and this closes that gap.

For health tech founders, the compliance infrastructure theme translates to several specific opportunities. Broker workflow software that natively supports HHS consent form requirements and creates auditable documentation trails is now more valuable. The market for agent and broker compliance tooling has been somewhat sleepy, but as enforcement standards tighten and documentation requirements become more prescriptive, purpose-built compliance workflow tools get a real value proposition. Companies like Quotit, AgentLink, and various smaller broker CRM players are already in this neighborhood, but the functionality demanded by the new standards is substantially more rigorous than what most of these platforms currently deliver.

The 75 percent SEP verification threshold also creates infrastructure demand. Someone needs to build the verification logic, the identity verification integrations, and the eligibility determination workflow that allows exchanges to actually hit that threshold without creating massive consumer friction. The technical build for that is non-trivial, and it sits at the intersection of identity tech, eligibility data, and enrollment workflow – exactly the kind of multi-stakeholder data problem that purpose-built health tech companies are well positioned to solve.

Risk adjustment updates and the data infrastructure angle

The HHS risk adjustment model recalibration for 2027 is one of those provisions that is easy to skip over because it reads like actuarial wallpaper. But for investors with portfolio companies in payer analytics, value-based care infrastructure, or health data platforms, it is worth understanding.

CMS is recalibrating the 2027 models using 2021, 2022, and 2023 EDGE data – the three most recent consecutive years available at rulemaking. The model update methodology is stable and well-understood at this point, using blended coefficients across three years of enrollee-level data. The more interesting piece is the HHS-RADV methodology update, which adds a scaling factor to the error estimation calculation to account for the removal of no-HCC enrollees from the initial validation audit sample. This is a technical fix to a methodological gap that the 2026 Payment Notice created when it excluded those no-HCC enrollees from the audit sample, which introduced a denominator problem in how error rates were estimated.

For investors, the relevant takeaway is that risk adjustment remains the central financial mechanism through which commercial insurers on the ACA exchange live or die, and the RADV audit process is the enforcement mechanism that keeps risk score accuracy honest. As plan design flexibility increases under this rule, the actuarial and data governance burden on issuers also increases. Plans that are innovating on benefit design, contracting with non-network providers, and serving new population segments have a harder time maintaining clean, auditable risk score logic than incumbents running vanilla silver plans with well-established documentation practices.

That creates demand for risk adjustment analytics and data validation infrastructure. The market for RADV audit preparation, HCC coding quality, and risk adjustment data governance has been growing steadily for years, but the combination of new plan types and tighter RADV enforcement creates a more acute need. Companies in the clinical coding intelligence space – leveraging NLP and machine learning against clinical documentation to improve HCC capture – should be looking at how non-network plan designs specifically create new documentation challenges that their existing products may or may not address well.

The MLR wildcard: what CMS is actually signaling

The medical loss ratio solicitation is not a proposed rule change – it is a comment solicitation. CMS is asking whether the 80 percent MLR floor in the individual market should be adjusted, and whether the administrative process for states to request MLR adjustments should be simplified. That is not nothing.

The MLR requirement, finalized under the ACA, mandates that insurers in the individual market spend at least 80 percent of premium revenue on medical care and quality improvement. If they do not hit that threshold, they owe rebates to consumers. The rule has been reasonably effective at constraining administrative overhead and profit margins at larger insurers, but it has also been a structural constraint on smaller market entrants, including insurtech startups, because it limits the capital available for technology investment and customer acquisition.

An adjustment to the MLR standard would have significant second-order effects on the competitive landscape. If the minimum drops from 80 percent to, say, 78 percent, the incremental administrative and tech budget unlocked for a mid-size regional insurer is not trivial. It also potentially makes the unit economics of building on the exchange more viable for smaller, technology-forward issuers that currently struggle to hit the 80 percent threshold while also investing in the infrastructure needed to compete on care quality and consumer experience.

The more important signal here is directional. CMS soliciting comment on MLR flexibility is a sign that the regulatory appetite exists for adjustments that have historically been considered politically untouchable. Entrepreneurs and investors building in the payer or payer infrastructure space should watch how this comment process unfolds and what the eventual final rule signals about the administration’s willingness to use MLR as a competitive lever.

How to think about the opportunity stack as an investor or founder

Pulling back to the 30,000-foot view, the pattern across this rule is fairly coherent. The 2027 Payment Notice is essentially a deregulatory document dressed in administrative compliance language. It expands distribution pathways, loosens plan design constraints, reduces ECP contracting minimums, creates new plan structures for long-term engagement, and simultaneously tightens up fraud and integrity standards. That combination – more design freedom, higher compliance expectations – is actually a fairly ideal environment for technology-enabled market participants relative to pure administrative incumbents.

The opportunity stack, roughly in order of time-to-market and clarity of business model, looks something like this. The enrollment and distribution technology plays are the most near-term. The SBE-EDE option, once states begin adopting it, creates immediate demand for consumer-facing enrollment experiences, broker enablement platforms, and compliance workflow tools. These are businesses that can be built and sold relatively quickly and that have a clear buyer in either the broker distribution channel or the exchange administration layer.

The plan design and analytics plays are medium-term. Repealing standardized options and non-standardized plan limits creates demand for actuarial modeling tools, plan configuration software, and consumer decision support technology. The non-network QHP pathway, if it matures, creates a whole new category of plan design and network management tooling that does not currently exist in the exchange context. These are businesses with longer development timelines but potentially more durable competitive positions because the infrastructure being built is genuinely new.

The long-term engagement and chronic care plays are the most speculative but potentially the most valuable. The multi-year catastrophic plan framework, if it gains adoption, creates the actuarial foundation for investing in long-term member health at a population level in a way that has not been viable in the single-year exchange context. Building the engagement infrastructure, remote monitoring, and chronic disease interception programs designed specifically for that context is a 3-5 year build, but the total addressable market is real and the competitive landscape is greenfield.

For early-stage investors, the most interesting thing about this rule is not any single provision but the overall direction of travel. The trend across the last several payment notices under the current administration has been consistent: more private sector involvement, more state flexibility, more design freedom for issuers, and stronger enforcement at the fraud and integrity layer. That regulatory environment rewards technology-enabled market entrants who can execute on the new design possibilities while managing the heightened compliance burden. The old model of winning on distribution through regulatory capture of the enrollment infrastructure is getting harder. The new model rewards genuine product differentiation and execution quality.

One practical note for anyone tracking this: the comment period closes March 11, 2026. These are proposed rules, not final rules, and several provisions – including the non-network QHP certification and the SBE-EDE option – are significant enough to attract substantial comment from incumbent insurers, hospital systems, and consumer advocacy groups who will push back hard. The final rule will look different from what CMS proposed, and specific provisions may be modified or pulled. Anyone building against these provisions as market assumptions should be tracking the comment process and the final rule publication closely before committing significant capital or engineering resources to bets that depend on specific regulatory outcomes.

The comments from hospital systems on the ECP threshold reduction alone will be substantial. The comments from consumer advocates on the standardized plan repeal will be vigorous. CMS has proposed this package, but the distance between a proposed rule and a final rule in a politically contested regulatory space can be significant. Build awareness of the opportunity, do the technical diligence on the feasibility, and calibrate the go-to-market timing to the regulatory calendar. That is the right posture for founder and investor alike.​​​​​​​​​​​​​​​​

Abstract

Introduction: The Slowdown Nobody Saw Coming

The Four Patterns That Defined 2009-2019

Pattern One: Utilization Decline and Care Substitution

Pattern Two: Price Growth Deceleration Despite Consolidation

Pattern Three: The Medicaid Long-Term Care Puzzle

Pattern Four: Administrative Cost Compression

Market Sizing Implications for New Ventures

Hospital Services: The Shift to Lower Acuity Settings

Physician Services: The Rise of Non-Physician Practitioners

Pharmaceutical Spending: Generic Acceleration and New Drug Dynamics

Long-Term Care: Demographics vs Reality

Where the Money Is: Identifying High-Potential Markets

Care Delivery Transformation Plays

Workforce Optimization Solutions

Administrative Efficiency Tools

Chronic Disease Management Platforms

Regulatory Tailwinds Worth Riding

Building Businesses Around Stubborn Trends

Conclusion: Forecasting the Future of Health Spending

Abstract

Between 2009 and 2019, US healthcare spending growth slowed to 1.7 percent annually in real per capita terms, less than half the historical rate of 3.7 percent from 1970-2008. A recent Health Affairs study by Glied and Lui decomposes this slowdown, revealing four main patterns:

- Declining utilization with substitution toward lower-cost alternatives across hospitals, physicians, and pharmaceuticals

- Slower private hospital and physician price growth despite accelerating consolidation

- Unexpected decline in home health utilization among oldest Medicaid beneficiaries

- Greater-than-projected compression in private insurance administrative costs

Key market opportunities emerge from these trends:

- $167.8B private physician spending residual suggests massive market for care substitution platforms

- $78.9B hospital spending reduction points to ambulatory and virtual care plays

- $58.6B pharmaceutical underspend indicates generic acceleration and specialty drug competition opportunities

- $34.1B Medicaid home health reduction reveals aging-in-place and family caregiver support gaps

- $66.6B insurance administrative savings shows potential for continued automation

The data suggests regulatory tailwinds, large TAMs, and clear ROI cases exist for: non-physician workforce enablement platforms, hospital-to-home transition tools, specialty pharmacy cost management solutions, and administrative automation across claims processing, prior authorization, and care coordination.

Introduction: The Slowdown Nobody Saw Coming

Healthcare spending has been the bogeyman of American fiscal policy for decades. Every projection showed costs marching inexorably upward, consuming an ever-larger share of GDP until the entire economy would theoretically consist of nurses and administrators shuffling papers about other nurses and administrators. Except that’s not what happened.

The 2009-2019 period saw inflation-adjusted per capita health spending grow just 1.7 percent annually, compared to the 3.7 percent historical rate. More remarkably, this slowdown persisted even as 20 million people gained insurance coverage through the ACA. The healthcare share of GDP basically flatlined around 17-18 percent. And the kicker - this moderation appears to have continued post-COVID, with 2019-2023 showing the same 1.7 percent growth rate.

The Glied and Lui paper does something clever. They took CMS’s 2009 projection of what 2019 spending would look like (pre-ACA), subtracted out all the forecasted effects of actual policies that got implemented (ACA, sequestration, various Medicare payment changes, etc), and were left with a $783 billion residual. That’s $783 billion less spending than expected even after accounting for known policy changes. The question is why, and more importantly for our purposes, what does this tell us about where to build businesses.

The answer matters because the conventional wisdom about healthcare cost drivers has been wrong. The assumed iron laws - that reducing public payment rates would just shift costs to private payers, that price controls would be offset by volume increases, that slowing one sector would just balloon another - turned out not to be laws at all. This creates opportunity. When the fundamental assumptions change, new business models become viable that previously would have been dismissed as economically impossible.

The Four Patterns That Defined 2009-2019

Pattern One: Utilization Decline and Care Substitution

The first major pattern was straightforward utilization decline across major categories. Medicare and Medicaid inpatient stays per beneficiary both fell by roughly 10-30 percent depending on the program. Private insurance saw a smaller but still meaningful 11 percent drop in inpatient utilization. This wasn’t just fewer hospital admissions - prescription fills dropped significantly for Medicare (from 30.8 to 23.9 per beneficiary) and private insurance (from 7.2 to 6.0 per beneficiary), though Medicaid held steady.

At the same time, care was actively substituting to lower-cost alternatives. Outpatient visits increased across all payers even as inpatient declined. More strikingly, office visits shifted from physicians to non-physician practitioners. Medicare saw non-MD office visits jump from 3.5 to 5.7 per beneficiary, Medicaid from 1.0 to 2.8, and private insurance from 1.9 to 3.0. Meanwhile physician visits declined across all three payers.

The pharmaceutical story followed similar dynamics. Generic prescribing rates jumped from 75 percent in 2009 to 90 percent in 2019. Several high-volume categories went over-the-counter (antihistamines, PPIs, nasal sprays), which likely accounts for some of the prescription decline. And expensive specialty drugs like hepatitis C treatments saw rapid generic competition after initial launches, preventing the cost explosion many forecasters expected.

This utilization pattern reveals something important - the healthcare system can actually reduce consumption without apocalyptic consequences. Fewer hospital stays, fewer physician visits, fewer prescriptions. And patients seem to be doing fine or better, given that we saw no corresponding spike in mortality or morbidity. The substitution toward lower-cost alternatives suggests the previous level of high-intensity, high-cost care delivery was producing diminishing returns.

Pattern Two: Price Growth Deceleration Despite Consolidation

The second pattern is genuinely surprising. Real (inflation-adjusted) costs per inpatient hospital stay grew 2.1 percent annually from 2010-2019, down from 2.9 percent in 2000-2009. This deceleration happened during a period of accelerating hospital consolidation. About a third of hospitals were involved in mergers between 2010-2019, and conventional wisdom said this would give them pricing power over private insurers.

But the price effects of mergers actually declined during this period. Mergers between 2010-2015 averaged just 1.6 percent price increases, lower than historical patterns. Why? Because more of the consolidation involved combinations of small and large systems, which produce smaller price effects than mergers between already-large systems. Do the math: one-third of hospitals involved in mergers with 1.6 percent average price effect translates to maybe 0.5 percent total impact on 2019 spending, or around $2 billion on a $430 billion private insurance hospital spending base. Basically a rounding error.

The physician price story is even more dramatic. Real wages for high-earning physician specialties fell between 1.8 and 8.2 percent from 2009 to 2019. Across all specialties, real wage and salary earnings declined 0.4 percent during 2012-2019. Some of this reflects shifting compensation from salary to business income, but the overall moderation is clear. Meanwhile nurse practitioners saw real earnings growth of 9.8 percent during 2012-2019, though they still earn about half what physicians make.

This creates an obvious arbitrage opportunity. If you can deliver care with nurse practitioners, PAs, and other non-physician providers instead of physicians, you’re capturing real wage cost savings plus the substitution gap. The data shows patients are already voting with their feet - non-MD visits are growing rapidly across all payers. The market is saying it’s okay to see an NP for a sinus infection or diabetes follow-up.

Pattern Three: The Medicaid Long-Term Care Puzzle

Here’s something basically nobody predicted. Home health utilization among the oldest Medicaid beneficiaries fell dramatically. The share of beneficiaries 85+ with any home health use dropped from 46 percent in 2008 to 32 percent in 2018. This contributed to Medicaid home health spending coming in 27 percent ($34.1 billion) below 2009 projections.

CMS actuaries correctly predicted a shift from institutional to home-based long-term care, but completely missed that home-based utilization rates would decline. The paper identifies several potential explanations: declining smoking rates across birth cohorts (7.8 percent among 75-84 year-olds in 1998 down to 6.3 percent in 2008), fewer elderly living alone (49 percent in 2009 down to 47 percent in 2019), and rising real average family incomes (from $44,909 in 2008 to $50,451 in 2018).

Translation: older Americans are healthier, wealthier, and more likely to have family support than forecasters assumed. The cohort aging into their 80s and 90s during this period had lower lifetime smoking exposure, more financial resources, and better family structures than previous generations. This reduced their need for paid home health services.

The business implication is subtle but important. The long-term care crisis everyone predicted hasn’t materialized in the form expected. Yes, the population is aging, but they’re aging better. The opportunity isn’t necessarily building massive institutional or home health capacity - it might be tools that help healthy, financially stable elderly people age in place with minimal formal healthcare intervention. Think less “build more nursing homes” and more “help families coordinate care for aging parents.”

Pattern Four: Administrative Cost Compression

Private insurance administrative costs came in 26 percent ($66.6 billion) below CMS’s post-ACA projection. The ACA’s medical loss ratio requirements mandate that insurers spend at least 80-85 percent of premiums on actual medical care, with the remainder available for administration and profit. By 2016, 90+ percent of enrollees across individual, small group, and large group markets were covered by plans meeting these standards.

Now, there’s some gaming happening here. Insurers have incentives to shift costs to related businesses or manipulate administrative cost definitions to meet MLR requirements. But even accounting for that, the compression is real. Administrative spending as a percentage of premiums has declined meaningfully.

This matters for two reasons. First, it suggests the ACA’s regulatory approach worked better than expected at constraining administrative costs. Second, it means there’s continued pressure on insurers to find genuine efficiency gains. You can only game the MLR requirements so much before you need actual administrative productivity improvements.

The opportunity is in building tools that deliver legitimate administrative savings - claims processing automation, prior authorization streamlining, care coordination platforms, member communication systems. Insurers face regulatory pressure to reduce admin costs and can’t just shift everything to related entities. They need real solutions.

Market Sizing Implications for New Ventures

Hospital Services: The Shift to Lower Acuity Settings

Private insurance hospital spending came in $78.9 billion below projections, Medicare $54.3 billion below, Medicaid $54.3 billion below. Across all payers, that’s about $187 billion in hospital spending that didn’t materialize. Some of this reflects lower utilization, some reflects price moderation, but the core message is that traditional inpatient hospital care is a shrinking market in real terms.

The growing market is everything else. Outpatient visits grew across all payers. Ambulatory surgical centers, urgent care centers, telehealth, hospital-at-home programs - these are all capturing volume that historically went to inpatient settings. The data suggests we’re still in early innings of this transition.

Market size estimates for hospital substitution plays should be based on the portion of that $187 billion residual that’s addressable by lower-cost alternatives. Not all inpatient care can move - you can’t do major trauma or complex surgery outside a hospital. But a lot of observation stays, short-term acute care for manageable conditions, post-surgical monitoring, etc can shift to other settings.

If you assume 30-40 percent of the hospital spending reduction is addressable by new care models (probably conservative), that’s a $55-75 billion TAM. And it’s a TAM with regulatory tailwinds, clear ROI cases (lower cost per episode with similar or better outcomes), and stubborn underlying trends (patients prefer convenience, payers prefer lower costs, providers need margin improvement).

Physician Services: The Rise of Non-Physician Practitioners

Private insurance physician spending shows a $167.8 billion residual, by far the largest single category. This represents 43 percent of the total private insurance residual and 21 percent of the overall spending slowdown. Medicare and Medicaid physician spending also came in below projections, though not as dramatically.

The substitution pattern is clear - physician visits declining, non-physician visits rising sharply. This isn’t just cost shifting, it’s a fundamental change in care delivery. Scope of practice regulations for NPs, PAs, and other advanced practice providers expanded during this period. More states granted independent practice authority. Professional identity and patient acceptance both increased.

The market opportunity is in platforms that enable this transition. Workforce optimization tools that match patient acuity to provider type. Supervision and collaboration software for states that still require physician oversight. Training and credentialing systems to scale non-physician provider supply. Care protocols and clinical decision support designed for NP/PA workflows rather than just adapted from physician patterns.

Size this market by thinking about the wage differential and visit differential. NPs earn roughly 50 percent of physician wages while handling visit volumes that grew from 1.9 to 3.0 per private insurance enrollee (a 58 percent increase). The efficiency gains from optimal deployment of non-physician workforce are massive. Even capturing a small percentage of the $167.8 billion physician spending residual represents a multi-billion dollar TAM.

Pharmaceutical Spending: Generic Acceleration and New Drug Dynamics

Pharmaceutical spending came in $18.1 billion below projections for Medicare, $38.5 billion below for Medicaid, $58.6 billion below for private insurance. Total pharmaceutical residual around $115 billion. The generic substitution story (75 percent to 90 percent of prescriptions) explains a lot of this, as does the rapid generic competition for expensive specialty drugs like hepatitis C treatments.

But there’s another dynamic worth noting. New drug introductions slowed between 2016-2019, and new drugs never exceeded 5 percent of retail specialty prescriptions. The pharmaceutical innovation pipeline didn’t deliver the constant stream of expensive new therapies that forecasters assumed. This creates a lumpy opportunity landscape.

When blockbuster new drugs do launch (think GLP-1s for weight loss currently), they create massive short-term spending increases but also trigger rapid competitive responses. Ozempic and Wegovy dominated early, but now there are multiple competitors and biosimilars in development. The pattern seems to be: expensive new drug category emerges, prices spike, competition intensifies, prices moderate.

The business opportunity is in tools that accelerate this cycle. Specialty pharmacy platforms that create price transparency and drive competition. Biosimilar manufacturing and distribution. Patient access programs that reduce out-of-pocket costs while maintaining volume. Pharmaceutical benefit management that’s actually aligned with cost reduction rather than rebate maximization.

The TAM is significant because brand to generic conversion and specialty drug competition apply across all payers. The $115 billion pharmaceutical residual suggests there’s massive value in solutions that accelerate competitive dynamics and generic substitution.

Long-Term Care: Demographics vs Reality

The Medicaid long-term care story deserves its own analysis. The $34.1 billion home health residual (27 percent below projections) runs counter to every demographic forecast. Population aging is real - the 65+ cohort is growing rapidly and will continue to do so. But the relationship between age and healthcare utilization is more complex than simple headcount projections.

The data shows three factors driving lower than expected utilization: better health status (lower smoking rates), more financial resources (higher real incomes), and stronger family structures (lower rates of living alone). Each of these reduces the need for paid home health services among the oldest cohorts.

This suggests the market opportunity is not in replicating traditional home health models at larger scale. Instead, look for adjacent plays. Technology that helps family caregivers coordinate with professional services. Financial products that help middle-income elderly pay for care without qualifying for Medicaid. Home modification and safety solutions that extend independent living. Remote monitoring that catches problems before they require intensive intervention.

The TAM might actually be larger than the $34 billion residual suggests, because you’re serving a population that’s staying healthier longer and has more resources. They won’t qualify for Medicaid home health, but they’ll pay out-of-pocket or through insurance for solutions that maintain independence. Think of it as capturing the delta between “needs institutional care” and “could use some support” - a much bigger cohort with purchasing power.

Where the Money Is: Identifying High-Potential Markets

Care Delivery Transformation Plays

The hospital and physician utilization data points to massive opportunity in care delivery transformation. We’re talking about $187 billion in hospital spending and $167 billion in physician spending that came in below projections. Some of this is better outcomes, some is deferred care, but a lot represents structural changes in how and where care gets delivered.

High-potential areas include hospital-at-home programs (acute care delivered at patient homes with remote monitoring and periodic visits), ambulatory surgical centers for procedures that historically required inpatient stays, specialty telehealth for conditions that don’t require physical exam, and hybrid models that combine in-person and virtual care.

The key is understanding the regulatory landscape and reimbursement dynamics. Hospital-at-home gained permanent Medicare coverage in 2023, creating huge tailwind for these models. ASCs have strong Medicare and commercial reimbursement in many categories. Telehealth reimbursement remains uncertain but is stabilizing around hybrid care rather than pure virtual.

Build businesses that have clear cost savings vs traditional care delivery (minimum 20-30 percent), equal or better outcomes, and positive patient experience. If you hit all three, payers will pay and patients will choose you. The TAM is whatever percentage of hospital and physician spending is addressable by your model - even a few percent of $350+ billion combined residual is a big market.

Workforce Optimization Solutions

The non-physician provider trend is one of the most durable patterns in the data. Every payer, every year, more NP/PA visits and fewer MD visits. This reflects regulatory changes (scope of practice expansion), economic incentives (wage differentials), and patient acceptance (people are fine seeing NPs for most things).

The opportunity is in platforms that help healthcare organizations optimize workforce mix and deployment. Think scheduling software that routes patients to appropriate provider types based on acuity. Clinical protocols designed for non-physician workflows. Supervision and collaboration tools for states requiring physician oversight. Credentialing and training systems to scale NP/PA supply.

Size the market by thinking about the productivity gains from optimal deployment. If a healthcare system can handle 30 percent of its patient volume with NPs instead of MDs, and NPs cost 50 percent as much, that’s a 15 percent direct labor cost savings on a huge denominator. US physician services spending was around $800 billion in 2019. Fifteen percent is $120 billion in potential savings, of which tools enabling this transition could capture meaningful share.

The regulatory tailwinds are strong. More states are granting independent practice authority to NPs. Federal rules increasingly allow non-physician billing for services that previously required physician involvement. Professional organizations are supporting scope expansion. Build tools that ride these trends rather than fighting them.

Administrative Efficiency Tools

The $66.6 billion insurance administrative cost residual suggests continued opportunity in automation and efficiency. MLR requirements create sustained pressure on insurers to reduce admin costs without sacrificing service quality. This means investments in technology that genuinely reduces work rather than just shifting it around.

High-potential areas include prior authorization automation (using clinical logic to approve straightforward cases without manual review), claims processing straight-through processing (no human touch for clean claims), member communication automation (chatbots and self-service for routine inquiries), and care coordination platforms (managing complex patients across providers and settings).

The key is demonstrating real cost reduction with maintained or improved outcomes. Insurers are sophisticated buyers who will measure whether your tool actually reduces FTEs or just moves work from one department to another. Show clear before/after metrics on manual work required, processing time, error rates, and member satisfaction.

TAM sizing is tricky because you’re selling to a concentrated buyer base (a few hundred health plans and TPAs), but the spending base is enormous. Private health insurance administrative costs were around $250 billion in 2019. If your tool can credibly claim to reduce admin costs by 5-10 percent for a specific function, and that function represents 10-20 percent of admin spending, you’re looking at a $1-5 billion addressable market. That supports meaningful venture-scale businesses.

Chronic Disease Management Platforms

The utilization data shows fewer physician visits but more non-physician visits and stable to declining inpatient stays. This suggests chronic disease management is shifting to lower-intensity, more distributed models. Fewer quarterly physician checks, more frequent NP or care manager touchpoints, remote monitoring, patient self-management with periodic clinical oversight.

The business opportunity is in platforms that enable this model. Remote patient monitoring for diabetes, hypertension, heart failure, COPD - conditions that are high-prevalence, high-cost, and manageable with proper monitoring and intervention. Care management platforms that help nurses and community health workers coordinate across multiple patients and providers. Patient engagement tools that drive behavior change and medication adherence.

The TAM is based on chronic disease prevalence and cost. Diabetes affects 37 million Americans and costs around $400 billion annually in direct and indirect costs. Hypertension affects 120 million and contributes to cardiovascular disease costs. Heart failure affects 6 million and costs $30+ billion. Even modest improvements in management (reducing hospitalizations by 10-20 percent, improving medication adherence, catching deteriorations earlier) represent billions in savings.

The regulatory environment is increasingly supportive. CMS has created dedicated payment codes for remote patient monitoring and chronic care management. Commercial payers are following suit. Value-based care arrangements reward population health management. Build tools that integrate with existing clinical workflows, produce measurable outcomes, and have clear ROI for payers and providers.

Regulatory Tailwinds Worth Riding

Several regulatory trends from the 2009-2019 period create persistent tailwinds worth building businesses around. The ACA’s MLR requirements aren’t going anywhere regardless of political changes - they’ve become embedded in how health insurance works. This creates sustained pressure for administrative efficiency.

Medicare payment reforms (bundled payments, value-based purchasing, hospital readmission penalties) have proven stickier than expected. Despite initial skepticism, these programs have driven real changes in provider behavior and created infrastructure for continued quality/cost measurement. New businesses that help providers succeed under value-based payment have clear value proposition and willing buyers.

Scope of practice expansion for non-physician providers continues across states. Even states that haven’t granted full independent practice are expanding supervision requirements and billing rules. The long-term trend is clear - NPs and PAs will have increasing autonomy. Tools that assume this future rather than fighting it will age better.

Telehealth reimbursement reached an inflection point during COVID but has stabilized around hybrid care models rather than pure virtual. This is actually better for businesses - pure virtual has limited use cases, but hybrid models (combining virtual and in-person based on patient needs) can address much broader populations. Build assuming hybrid is the steady state.

Hospital price transparency requirements are creating new data availability. Knowing what hospitals actually charge different payers for specific services enables all sorts of price shopping, reference-based pricing, and competitive dynamics. Businesses built on transparent pricing data have regulatory wind at their backs.

Building Businesses Around Stubborn Trends

The most valuable businesses get built on trends that persist regardless of policy changes or economic cycles. The 2009-2019 period included major policy upheaval (ACA), economic crisis (Great Recession), and significant market changes (accelerating consolidation), yet certain patterns held throughout. These are the trends to bet on.

Care delivery shifting from high-intensity to lower-intensity settings and providers appears structural. Patients prefer convenience, payers prefer lower costs, new technology enables remote and asynchronous care. This trend survived policy changes, economic fluctuations, and pandemic disruption. Build businesses assuming inpatient stays, traditional physician offices, and brick-and-mortar delivery continue declining.

The shift from brand to generic pharmaceuticals accelerated throughout the period despite new expensive specialty drugs. Generic substitution reached 90 percent and shows no signs of reversing. Every new branded drug faces faster generic competition than the previous generation. Build businesses assuming continued generic dominance and rapid erosion of specialty drug pricing power.

Administrative cost pressure on health insurers intensified and stayed intense. MLR requirements, competitive pressure, employer demands, political scrutiny - all push toward leaner administration. This survived changes in administration, market concentration, and business model evolution. Build businesses assuming insurers face sustained pressure to reduce administrative costs.

Non-physician provider utilization grew across all payers, all settings, all conditions. This reflects scope of practice changes, wage differentials, professional identity evolution, and patient acceptance. It survived physician opposition, quality concerns, and regulatory uncertainty. Build businesses assuming continued growth in NP/PA/other advanced practice provider utilization.

The absence of cost shifting from public to private payers represents a major change from historical patterns. Private prices didn’t spike when Medicare reduced payment rates. Hospital consolidation didn’t produce the price increases expected. This suggests market dynamics have changed in ways that make regulation more effective. Build businesses assuming price discipline persists rather than reverting to historical cost escalation.

Conclusion: Forecasting the Future of Health Spending

The 2009-2019 healthcare spending slowdown happened because fundamental assumptions about cost drivers turned out to be wrong. Cost shifting between payers was minimal. Volume didn’t offset price controls. One sector’s savings didn’t balloon other sectors. The iron laws weren’t laws, they were just lazy assumptions.

For entrepreneurs and investors, this creates opportunity. When the fundamental model changes, new businesses become viable that previously would have been dismissed as economically impossible. You can build on substitution because volume offsets aren’t eating all the savings. You can build on price moderation because cost shifting isn’t inevitable. You can build on one sector’s efficiency without worrying about balloon effects elsewhere.

The highest-value opportunities combine large TAM, stubborn trends, regulatory tailwinds, and clear customer ROI. Care delivery transformation plays (hospital-at-home, ASCs, hybrid virtual care) hit all four. Workforce optimization (enabling NP/PA substitution) hits all four. Administrative automation (prior auth, claims processing, care coordination) hits all four. Chronic disease management platforms hit all four.

Market sizing should be aggressive but grounded in the data. The $783 billion total residual represents real spending that didn’t happen. Not all is addressable by new business models, but meaningful percentages are. A business that can credibly capture even one percent of an addressable market in the tens of billions is venture-scale and then some.

The regulatory environment matters more than founders sometimes acknowledge. MLR requirements, scope of practice rules, reimbursement policy, price transparency mandates - these shape what’s possible and profitable. The best businesses ride regulatory tailwinds rather than fighting them. Understand the policy landscape and build with it rather than against it.

The most important insight from the data is that health spending growth can moderate without catastrophe. Fewer hospital stays, fewer physician visits, fewer prescriptions, less intensive care delivery - and outcomes stayed stable or improved. This proves the system was operating with significant slack and waste. The opportunity is in capturing that slack through better models.

One final thought on forecasting. The paper notes that simple transparent algorithms (like five-year moving averages) predict better than complex expert-driven forecasts. This should humble entrepreneurs who think they can precisely predict market evolution. Build for multiple scenarios. Stay flexible. Focus on solving real problems with clear ROI rather than betting on specific future states.

The healthcare system is changing in ways that create genuine opportunity for new business models. The 2009-2019 data shows which changes are durable vs transient, which assumptions are broken vs still valid, and where the money is vs where people think it is. Pay attention to what actually happened rather than what people predicted would happen. That’s where the next generation of successful healthcare companies will find their openings.​​​​​​​​​​​​​​​​

GUARD and GLOBE represent CMS dragging pharmaceutical pricing into the data age through brute force regulation. GUARD tackles Part D drugs, GLOBE targets Part B drugs, but both share DNA: mandatory participation, international reference pricing benchmarks, quarterly reconciliation hell, and a ten-day suggestion of error window that makes tax season look relaxed. The proposals create new infrastructure demands around benchmark replication, rebate forecasting, invoice validation, coinsurance adjustment mechanics, and audit defense. Winners won’t be dashboards. Winners will be boring systems that become source of truth for model eligibility determination, unit normalization across countries, PPP adjustments, accrual-grade forecasting, and reconciliation workflows that don’t melt under Federal Register scrutiny.

Table of Contents

What CMS Actually Did and Why It Matters

The Mechanical Reality of Both Models

Why These Models Create Actual Markets Instead of Consulting Theater

Business Model One: Benchmark Replication as Mandatory Infrastructure

Business Model Two: Rebate Accrual Systems That Survive Quarter Close

Business Model Three: Provider-Side Coinsurance Correctness Tools

Business Model Four: Unit Normalization as Boring Infrastructure

Business Model Five: Shortage Intelligence and Supply Chain Disruption Tracking

Business Model Six: International Data Submission Management

Technical Architecture That Passes Federal Audit

Where This Breaks in Practice and How to Build Around It

Why Boring Infrastructure Wins When Policy Gets Specific

What CMS Actually Did and Why It Matters

On December 23, 2025, CMS dropped two proposed rules that most health tech will misread as “international drug pricing policy” and move on. That would be wrong. GUARD (Guarding U.S. Medicare Against Rising Drug Costs) and GLOBE (Global Benchmark for Efficient Drug Pricing) are infrastructure specs disguised as payment policy. They mandate specific computational workflows, define data exchange formats, establish reconciliation calendars, and create compliance windows measured in days not months.

GUARD applies to Part D drugs covering oral medications typically picked up at retail pharmacies. Performance period runs January 1, 2027 through December 31, 2031, with payment years extending through December 31, 2033 for final reconciliation. The model targets sole-source drugs and sole-source biological products in specific USP therapeutic categories including analgesics, anticonvulsants, antidepressants, antineoplastics, antivirals, blood glucose regulators, cardiovascular agents, immunological agents, and respiratory agents. Approximately 25 percent of Part D enrollees selected by geographic randomization using ZCTAs.

GLOBE applies to Part B drugs covering physician-administered medications in outpatient settings. Performance period runs October 1, 2026 through September 30, 2031, with payment years through September 30, 2033. The model targets single-source drugs and sole-source biological products in USP categories including antigout agents, antineoplastics, blood products, central nervous system agents, immunological agents, metabolic bone disease agents, and ophthalmic agents. Only drugs with over $100 million in annual Medicare Part B FFS spending included. Approximately 25 percent of Medicare Part B FFS beneficiaries selected by ZCTA randomization.

Both models use the same mechanics. Manufacturers calculate a Medicare net price by taking WAC minus manufacturer rebates and discounts. CMS identifies an international benchmark using data from 19 reference countries (Australia, Austria, Belgium, Canada, Czechia, Denmark, France, Germany, Ireland, Israel, Italy, Japan, Netherlands, Norway, South Korea, Spain, Sweden, Switzerland, United Kingdom). The benchmark gets adjusted by GDP PPP factors. If Medicare net price exceeds the benchmark, manufacturers owe a rebate. The rebate calculation happens quarterly with invoicing occurring no later than 6 months after the end of each calendar quarter. Manufacturers have 30 days to pay. Regular reconciliation occurs within 12 months. Additional reconciliations can happen if CMS identifies errors or manufacturer misreporting. Suggestion of error window is 10 calendar days. Administrative and judicial review explicitly precluded.

This is not about whether international reference pricing is good policy. This is about the fact that CMS just mandated quarterly benchmark calculations, HCPCS unit normalization, PPP adjustments, multi-country data integration, and deterministic reconciliation processes for a chunk of Medicare drug spend starting in less than 18 months. Every manufacturer affected needs systems. Every actuary forecasting liability needs systems. Every provider reconciling patient responsibility needs systems. That creates markets.

The Mechanical Reality of Both Models

The rebate calculation requires four computational steps. First, identify the Medicare net price by subtracting manufacturer rebates from DIR and Manufacturer Discount Program discounts from WAC. Second, compute the international benchmark using either Method I (lowest GDP-adjusted country-level price from existing data sources) or Method II (volume-weighted average of manufacturer submitted net pricing data). Third, compare Medicare net price to benchmark. Fourth, if Medicare net price exceeds benchmark, calculate per unit rebate amount, multiply by total billing units during the quarter, apply any shortage reductions, and generate invoice.

For GLOBE, there’s an extra step. When the rebate triggers, beneficiary coinsurance gets reduced. If the benchmark plus add-on is lower than the specified amount, coinsurance percentage drops below 20 percent using a formula that scales by the ratio. Providers must reduce what they charge patients and Medicare payment adjusts upward. That means claims processing systems need logic to detect GLOBE drugs, identify GLOBE beneficiaries, calculate adjusted coinsurance, modify remittance, and track patient collections. This happens in real time at point of payment, not quarterly during rebate reconciliation.

For GUARD, the model only applies to Part D drugs dispensed to beneficiaries in model geographic areas. That means utilization forecasting requires estimating cohort penetration not just national volume. Manufacturers can’t just assume pro-rata splits. They need to infer from claims patterns which percentage of their drug spend flows through the model cohort. For Part D, claims data shows beneficiary address but manufacturers don’t see individual addresses. They see aggregate utilization. Forecasting model liability becomes a segmentation problem with structural uncertainty.

Both models use billing units as atomic calculation unit. For GLOBE, that’s HCPCS billing units based on the HCPCS Level II code descriptor. For GUARD, that’s units tied to NDC-9 level expressed in NCPDP units. Unit conversion matters because international data sources use different packaging standards, dose measurements, and strength conventions. A drug approved as 50mg/2mL vial in US might appear as 25mg/mL solution in Europe. Benchmark calculation requires converting all presentations into common unit basis, applying that consistently across countries, and defending the math when manufacturer submits suggestion of error.

The reference country set is fixed. CMS identified 19 countries in October 2025 using CIA World Factbook data for 2024. Those 19 countries remain unchanged for entire model duration even if economic indicators shift. That reduces policy volatility but creates data sourcing problems when pricing information for specific drugs becomes unavailable in specific countries during performance years. The regulations explicitly allow CMS to use the most recent available pricing data going back to certain earlier periods, but manufacturers forecasting rebates need to model scenarios where benchmark relies on stale information.

GDP PPP adjusters are static. CMS uses 2024 values from CIA World Factbook and applies them throughout model period. Currencies fluctuate. Purchasing power changes. But the adjuster stays locked. That simplifies calculation but means benchmark no longer represents real purchasing power by performance year 5. It represents purchasing power as of 2024 adjusted by nominal exchange rates when data source converts to USD. This matters for Method II submissions where manufacturers report net prices in local currency and CMS applies adjuster.

Why These Models Create Actual Markets Instead of Consulting Theater

Most health tech funding goes to companies solving problems that are vague enough to require selling rather than obvious enough to require buying. GUARD and GLOBE reverse that. The problems are specific. Manufacturers must calculate benchmarks or face penalties. Finance teams must accrue quarterly liability or fail audit. Providers must adjust coinsurance or over-collect from patients. The regs define what compliance looks like and when it’s due. That makes the buying decision obvious to the people with budget authority.

The enforcement mechanism is real. GUARD and GLOBE both exclude administrative and judicial review for most decisions. Suggestion of error is discretionary and narrow. CMPs apply for failure to pay. Manufacturers can’t litigate their way out. They have to operationalize. That means systems get purchased not to make internal stakeholders feel innovative but because legal and finance won’t sign off on manual processes for multi-million dollar quarterly liability calculations where the audit trail is “we used a spreadsheet.”

The timelines are compressed. For GLOBE, model starts October 2026. First rebates invoice February 2027. First reconciliation happens within 12 months. For GUARD, model starts January 2027. First rebates invoice by July 2027 for Q1 usage. Manufacturers affected by both models can’t wait for requirements to stabilize. Requirements are in Federal Register now. Procurement cycles for enterprise software take 6-12 months. That means buying decisions happen in first half of 2026 for delivery later that year.

The data doesn’t exist yet in usable form. Manufacturers have ASP submissions but those don’t map cleanly to international analog identification. They have DIR data but that’s in PBM formats not benchmark calculation formats. They have pricing information from some countries but not consistent coverage across all 19 reference countries. They have costing systems but those were built for GAAP reporting not per unit Medicare net price calculation by NDC-9 or HCPCS. The gap between existing systems and compliance requirements creates implementation revenue.

Business Model One: Benchmark Replication as Mandatory Infrastructure

The first market is benchmark replication engines. These are not analytics. These are calculation systems that produce audit-defensible benchmark values quarterly, with version control, with provenance, with ability to replay historical calculations exactly as run, and with explanation output that legal counsel can submit during suggestion of error window without needing to translate “the model said so” into regulatory language.

For Method I benchmarks, the system needs data ingest from whatever sources CMS uses (IQVIA MIDAS, GlobalData POLI, Eversana NAVLIN, or similar), NDC/HCPCS to international analog mapping with strength and dosage form normalization, presentation-level unit conversion to billing units, price aggregation by country with volume weighting if available, GDP PPP adjuster application, outlier filtering below 5 percent of US average price for GLOBE, country-level price identification, minimum selection, and rounding to specified decimal places. Every step needs to be traced. Every input needs timestamp and source citation. Every calculation needs to be replayable.

For Method II benchmarks, the system needs secure enclave for manufacturer submitted data under data agreement terms, validation logic to verify completeness and reasonableness, volume-weighting across countries, GDP PPP adjuster application, sanity checks against Method I to flag submissions that seem wrong, and calculation of across-country average. Then comparison of Method II to Method I to identify which is greater. All of this with audit logs showing who accessed what when and what validation checks ran.

The customer is government pricing and market access finance teams at manufacturers, controllership teams responsible for quarterly accruals, and advisory firms handling CMS model compliance. The wedge is “model liability estimate for quarter close” which ties into existing financial reporting cadence. The defensibility comes from being able to explain benchmark deltas at unit level when invoices arrive and manufacturer has 10 days to submit suggestion of error or 30 days to pay.

Pricing is annual subscription based on number of covered NDC-9s or HCPCS codes plus model coverage (GUARD only, GLOBE only, or both), with implementation fee for portfolio mapping and system integration. The sticky upsell is managed services for rapid response during suggestion of error windows because that’s where customer pain concentrates into acute incidents with executive visibility.

The moat is not UI. The moat is regulatory knowledge embedded in calculation rules. When CMS updates guidance or issues clarifications, the system updates calculation logic automatically with version tracking. When manufacturers upgrade, they don’t just get new features, they get continued compliance with latest CMS interpretation. The switching cost is explaining to audit committee why the company fired the system that had institutional knowledge about how CMS interprets “presentation level” and hired a new vendor that has to learn from scratch.

Business Model Two: Rebate Accrual Systems That Survive Quarter Close

The second market is rebate accrual and cash planning systems. Manufacturers need subledgers that estimate quarterly liability, book accrual entries, track expected invoices, post actual invoices, manage variance analysis, handle reconciliation adjustments over multi-year tail, and survive SOX controls audit.

The complexity is temporal. GUARD payment period extends through 2033 even though performance period ends 2031. GLOBE payment period extends through 2033 even though performance period ends 2031. That’s because invoicing lags quarters by up to 6 months, reconciliation happens within 12 months after invoice, and CMS can reopen for errors or misreporting. An accrual booked in Q1 2027 might get reconciled in Q2 2028, reopened for CMS error in Q4 2028, and finalized in Q1 2029. The system has to carry open items through the tail without losing transaction history.

The second complexity is estimation uncertainty. For GUARD, manufacturers must forecast utilization within cohort. For GLOBE, manufacturers must forecast which beneficiaries receive drugs in model geographies. Both require actuarial modeling not just trend analysis. The system needs to support scenario planning, confidence intervals, and documentation of assumptions because when actual diverges from estimate by material amount, management needs to explain why in earnings calls and audit committee meetings.

The technical architecture is event sourced ledger with rules engine. Minimum viable version uses quarterly events keyed by drug, model, quarter, estimated benchmark, estimated units, calculated accrual, actual benchmark when received, actual units when determined, actual invoice amount, variance, reconciliation adjustments, and final settlement. The mature version integrates with ERP via journal entry APIs, supports role-based approval workflows, provides immutable audit trail, and generates evidence packets tying each accrual to input datasets and calculation versions.

The customer is controllership and government pricing finance. The pitch is “survive quarter close without heroics” which resonates because right now most companies run these calculations in spreadsheets that break when formulas cascade errors through linked worksheets. The ROI is avoiding material weakness findings from external audit and reducing unplanned write-offs when reconciliation reveals accruals were wrong.

Pricing is annual subscription with implementation fee, potentially with shared savings component tied to variance reduction (that is, if the system’s forecasts reduce the average absolute error between estimated and actual invoices, customer shares in the working capital benefit). The defensibility comes from being the system of record where every rebate artifact lives: estimates, invoices, payments, reconciliations, CMS error corrections, shortage adjustments.

Business Model Three: Provider-Side Coinsurance Correctness Tools

The third market is provider-side coinsurance compliance for GLOBE. Providers aren’t model participants but they feel the model when claims process with adjusted coinsurance and remittances show different patient responsibility than expected. The revenue cycle systems need logic to detect model drugs, confirm model beneficiary status, calculate adjusted coinsurance, validate remittance matches expectation, handle secondary payer coordination, and identify over-collections requiring refunds.

The challenge is that providers don’t get advance notification of beneficiary model status. CMS updates GLOBE Eligible Beneficiary List weekly but providers don’t receive the list. They find out retrospectively when claim processes and remittance shows adjusted amount. That means the detection logic has to infer from remittance patterns and claim adjudication markers rather than checking against beneficiary roster.

The second challenge is coinsurance percentage varies by drug and quarter because it depends on benchmark calculation. A drug with low benchmark has higher coinsurance reduction. A drug with high benchmark might have no adjustment. The system can’t hard-code percentages. It needs to learn from remittances, build lookup table of drug-quarter-coinsurance mappings, and apply those to estimate expected patient responsibility before claim processes so front desk can quote accurate amounts.

The product is claims and remittance analytics that compares actual patient responsibility to expected patient responsibility, flags discrepancies, generates refund workflows, provides patient statements with correct amounts, and produces audit reports showing coinsurance compliance by drug, by quarter, by location. The data sources are claim files, remittance files, and potentially GLOBE Model website information if CMS posts drug lists and benchmark amounts.

The customer is revenue cycle operations at large health systems and large specialty practices (oncology, ophthalmology, rheumatology) that administer high volumes of GLOBE drugs. The pitch is “avoid over-collecting from patients and avoid under-collecting from secondary payers” which matters because state insurance commissioners care about provider billing accuracy and patients complain when statements are wrong.

Pricing is per facility or per million in allowed charges with potential shared savings component tied to detected over-collections and prevented refunds. The defensibility comes from building high-fidelity map of remittance behaviors and adapting quickly when CMS issues operational clarifications about how coinsurance adjustment appears in claim processing.

Business Model Four: Unit Normalization as Boring Infrastructure

The fourth market is HCPCS/NCPDP unit truth. Every benchmark calculation requires converting international presentation-level quantities into Medicare billing units. Every drug data vendor claims they can do this. The reality is it’s a minefield of packaging changes, descriptor updates, strength differences, dosage form variations, and quarter-specific mapping that breaks when manufacturers reformulate or when FDA updates HCPCS descriptors.

The product is versioned mapping service with API that returns, for any drug identifier and presentation description, the expected billing code, units per package, conversion logic, effective dates, and provenance. This is not about clinical interoperability. This is about billing semantics. It’s infrastructure that makes “50mg/2mL vial maps to 25mg billing unit” into a service call not a manual lookup in NDC directory.

The technical architecture is knowledge graph or relational mapping store with temporal validity, backed by curation workflow that captures evidence for each mapping, and test harness that replays known examples and flags drift when source descriptors change. The audit value is that when benchmark calculation divides presentation-level price by billing units per presentation, the system can explain why that divisor is what it is with citations to FDA documentation, manufacturer labeling, and NDC directory entries.

The customer is benchmark replication vendors (who need reliable unit conversion as dependency), manufacturers (who need conversion for internal forecasting), and potentially CMS contractors (who need conversion for invoice calculation validation). The pitch is “don’t let unit conversion errors blow up your reconciliation” which matters because suggestion of error window is 10 days and arguing about unit definitions takes longer than 10 days.

Pricing is usage-based API with premium support for exceptions and portfolio onboarding. The defensibility comes from being citational. Every mapping has evidence. Every conversion has audit trail. When disputes happen, the system produces the receipts.

Business Model Five: Shortage Intelligence and Supply Chain Disruption Tracking

The fifth market is shortage adjustment intelligence. Both GUARD and GLOBE reduce rebate amounts for drugs in shortage and for severe supply chain disruptions using formulas that count days in shortage during quarter and apply percent reductions. A single shortage event can create large financial swing for high-spend drug and large operational scramble.

The product is daily tracking of shortage list status with effective date ranges, mapping to drug concepts and billing codes, quarter day fraction calculation, push to accrual systems, and scenario planning. If drug enters shortage mid-quarter, what’s the expected reduction to rebate liability? This is specialized data product but it has value because it ties directly to defined formulas in regulations and influences cash.

The technical architecture is time series event ingestion with strong entity resolution and quarter calendar engine. The defensibility comes from clean mapping and evidence capture. When accrual changes because shortage status changed, finance teams need to document why variance occurred and what data supported the adjustment.

The customer is government pricing and controllership at manufacturers and potentially payers who need to forecast manufacturer behavior. The pitch is “know your shortage exposure before quarter close” which matters because unplanned adjustments create earnings surprises.

Pricing is annual subscription with optional scenario planning tier. The defensibility is being single source of truth for shortage status with historical tracking and forward alerts.

Business Model Six: International Data Submission Management

The sixth market is international data submission management for Method II benchmarks. Manufacturers who elect to submit net pricing data must aggregate sales by country, apply volume weighting, calculate across-country averages, apply GDP PPP adjusters, attest to accuracy, submit within 30 days after applicable ASP calendar quarter end for GLOBE or 180 days after performance year end for GUARD, and maintain data agreement compliance.

The challenge is manufacturers don’t have systems built for this. Their pricing databases are regional or country-specific. Their rebate tracking is separate from revenue accounting. Their cost systems don’t align to Medicare billing units. Building submission capability requires integrating across multiple source systems, implementing currency conversion, applying regulatory calculation logic, generating attestation reports, and maintaining audit trails.

The product is data aggregation engine that pulls from manufacturer systems, normalizes to GLOBE/GUARD requirements, calculates required values, validates outputs against reasonableness checks, generates submission files, tracks submission status, and maintains history for reconciliation support. This is ETL plus compliance logic plus workflow automation.

The customer is government pricing operations and IT at manufacturers who decide submitting Method II data is worth potential rebate savings. The pitch is “submit Method II without building internal capability” which matters because manufacturers evaluating whether to elect optional submission need to factor implementation cost into ROI calculation.

Pricing is annual subscription with implementation fee for source system integration. The defensibility is regulatory knowledge embedded in calculation logic and validation rules. When CMS updates submission requirements, the system updates automatically.

Technical Architecture That Passes Federal Audit

The architecture pattern that survives is replayable calculation pipeline with immutable inputs, versioned rules, deterministic outputs, and human-readable explanation.

For benchmark calculation, this means ingest layer storing raw pricing data, HCPCS descriptors, NDC directories, GDP PPP values, and claims-derived utilization as append-only datasets with effective dates and source metadata. Normalization layer transforms raw inputs into canonical entities (country, drug, presentation, billing unit, quarter). Calculation engine is pure functions where same inputs and same rules version always produce same outputs. Evidence generator outputs calculation traces that legal can read.

For Method I, the calculation engine explicitly executes the steps CMS describes: compute billing units in presentation, compute price per billing unit, filter outliers, compute country averages, apply GDP PPP adjusters, select minimum. Even if some steps only apply to certain data source types, the system represents them explicitly so it can adapt when data sources change.

For rebate calculation, the engine supports branching logic: compare benchmark-based difference to inflation-adjusted difference, use greater value, compute incremental amount, multiply by billing units, apply shortage adjustments. That branching produces subtle bugs if implemented ad hoc. It should be declarative rule set or heavily unit tested code with golden datasets.

On workflow side, the system designs around reporting sequence CMS describes. There’s Medicare Part B Drug Inflation Rebate Program invoice, then GLOBE/GUARD invoice for incremental amounts if applicable, then payment due within 30 days, then reconciliation reporting. The product treats each report as event object triggering tasks: validate inputs, compute expected amount, compare to received invoice, open discrepancy case, prepare suggestion of error packet within 10-day window if needed.

The system does not pretend it can appeal. The regulations explicitly preclude administrative and judicial review. The product focuses on mathematical correctness and evidence collection not legal theater.

Where This Breaks in Practice and How to Build Around It

The big risk is not that teams can’t compute benchmarks. The risk is that everyone computes slightly different benchmarks and spends money arguing about whose rounding is more spiritual. CMS specifies rounding at various points (fifth decimal place for GDP-adjusted prices in GLOBE). The more the system locks down those details and replays them exactly, the fewer disputes arise.

The second risk is cohort estimation. For GUARD, CMS randomly selects 25 percent of Part D enrollees by ZCTA. For GLOBE, CMS randomly selects 25 percent of Part B FFS beneficiaries by ZCTA. Manufacturers don’t get the beneficiary list. They must infer from claims patterns which percentage of their utilization flows through model cohort. Forecasting model liability requires forecasting cohort penetration not just national volume. Good products treat that uncertainty honestly with ranges and drivers, not magic precision.

The third risk is data availability. For Method I benchmarks, CMS uses existing data sources. If pricing information for GLOBE/GUARD drug isn’t available in existing sources for certain countries, benchmark gets calculated with remaining countries. Manufacturers forecasting rebates need to model scenarios where benchmark relies on subset of reference countries or stale information from prior quarters.

The fourth risk is unit conversion errors. International packaging differs from US packaging. Strength varies. Dosage forms diverge. Converting everything to common billing unit basis requires judgment calls about whether presentations are truly comparable. Those judgment calls become disputes during suggestion of error. Products that document reasoning and cite sources for each conversion decision survive disputes better than products that treat unit conversion as automatic.

The fifth risk is timeline pressure. Manufacturers have 30 days to pay after receiving invoice. Suggestion of error is 10 calendar days. That’s not much time to validate invoice, identify discrepancies, gather evidence, and submit formal challenge. Products that automate invoice validation and pre-populate suggestion of error templates help manufacturers meet deadlines.

The sixth risk is regulatory drift. CMS issues guidance, clarifications, and corrections. Calculation logic needs to adapt. Products that treat regulatory content as versioned data that drives calculation rules can update automatically when guidance changes. Products that hard-code logic based on initial reading of Federal Register freeze technical debt into architecture.

Why Boring Infrastructure Wins When Policy Gets Specific

The health tech funding market consistently rewards storytelling over execution because most healthcare problems are ambiguous enough that you can sell vision without proving delivery. GUARD and GLOBE reverse that dynamic. The problem is specific. The deliverable is defined. The timeline is compressed. The penalty is real.

That creates market for companies that do the boring work. Map NDCs to international analogs. Normalize presentation quantities to billing units. Apply GDP PPP adjusters correctly. Track shortage status daily. Maintain immutable audit logs. Generate calculation traces that legal can submit. Integrate with ERP. Support SOX controls. Do it all deterministically so the same inputs produce the same outputs every time.

The winners won’t be companies that build beautiful dashboards showing international price comparisons. The winners will be companies that become systems of record for benchmark calculations, invoice validation, reconciliation tracking, and coinsurance adjustment. They’ll be companies where switching cost is explaining to auditors why the firm fired the system that had two years of calculation history and hired a new vendor starting from scratch.

The market size is every manufacturer affected by GUARD or GLOBE or both, every provider administering GLOBE drugs and handling coinsurance adjustments, every PBM managing DIR for GUARD drugs, and every advisory firm helping clients navigate model compliance. That’s not a niche. That’s a forcing function where regulatory specificity creates mandatory infrastructure demand.

The funny part is the market will try to fund shiny analytical tools first because that’s what health tech investors pattern match to. The boring part is that the real revenue will go to whoever becomes the source of truth for calculations that must happen quarterly and must be correct because the invoice is due in 30 days and the suggestion of error window is 10 days and nobody wants to explain to the CFO why we’re accruing unplanned rebate liability because our unit conversion was wrong.​​​​​​​​​​​​​​​​

Why This Rule Exists and Why It Looks the Way It Does

What Actually Changes in the Public Files

The Quiet but Massive Shift to Network Level Truth

Why CMS Is Obsessed With Context All of a Sudden

Utilization Data as the Missing Multiplier

Taxonomy Disclosure and the End of Contracting Theater

Out of Network Data Finally Becomes Usable

Quarterly Cadence and the Death of Panic ETL

Findability as a Regulatory Weapon

The Phone Requirement and the Operational Reckoning

Costs, Burden, and What the Government Is Accidentally Telling Investors

The Market Level Aggregation Gambit

The Percentage of Billed Charges Cleanup

What This Means for Founders

Where Venture Capital Fits Cleanly

Where Private Equity Has the Edge

The Service Provider Play and Why It Matters

The Compliance Software Stack Nobody Sees Coming

Deep Analysis of Investment Opportunities by Vertical

How to Underwrite This Wave Without Getting Burned

Closing Thoughts on What Actually Gets Built

Abstract

This essay breaks down the latest Transparency in Coverage proposed rule and why it matters far more to investors and operators than most people realize. The rule is not about consumer shopping tools, and it is not about ideology. It is about converting a chaotic flood of pricing disclosures into a structured, contextualized, and operational dataset that can actually be used. The government is forcing plans and issuers to publish network level negotiated rates, utilization context, adjudication logic, and explicit change tracking, while also making the data easier to find and slower to change. This combination quietly unlocks a new generation of infrastructure, analytics, and compliance driven business models. The real opportunity sits with enterprise buyers, not patients, and favors companies that reduce operational cost, create negotiating leverage, and turn regulatory burden into recurring software and services revenue. Venture and private equity both have room to win here, but only if they underwrite the boring parts correctly.

The proposed rule also creates explicit permission structures for third party aggregation at unprecedented scale, legitimizes taxonomy management as a regulated business process, and forces phone based disclosure that will drive massive call center software spend. The government estimates over nine hundred million dollars in one time implementation costs and nearly seventy million in annual ongoing costs. That is not a compliance headache. That is total addressable market spelled out in Federal Register pages.

Why This Rule Exists and Why It Looks the Way It Does

The easiest way to misunderstand this proposed rule is to think it is a policy statement about transparency as a moral good. It is not. It is a technical correction to a system that technically worked but practically failed. The first wave of Transparency in Coverage did exactly what it was supposed to do in the narrowest sense. It forced the release of negotiated rates, allowed amounts, and drug pricing data that had never been public before. What it also did was create files so large, duplicative, and context free that only a handful of well capitalized data engineering teams could even open them without lighting money on fire.

CMS and the other agencies are not subtle about this in the proposal. They openly acknowledge that the in network rate files in particular became enormous because contracts enumerate every possible item and service for every provider regardless of whether that provider would ever be paid for that service. The result was petabyte scale data that mixed meaningful rates with nonsense combinations. Researchers complained. Engineers complained. Plans complained. Even the people publishing the data complained because storing and serving it was expensive and error prone.

The numbers tell the story. Some issuers are serving multiple terabytes per month for a single coverage entity. Individual files routinely exceed local storage and processing capabilities. The Departments conducted an internal analysis in 2024 sampling in network rate files market wide and found that eighty three percent of issuers were already using table of contents structures to reduce duplication, which means the industry has been screaming for relief through their implementation choices.

So this rule is not a philosophical pivot. It is an engineering intervention. The agencies are trying to shrink file size, reduce duplication, add context that downstream users have been reverse engineering anyway, and align plan disclosures more closely with how hospital price transparency already works. The goal is not prettier files. The goal is to make the data usable enough that market pressure can actually happen.

That framing matters for investors because it tells you what kind of companies win. This is not a moment for glossy front ends and consumer delight decks. This is a moment for plumbing, tooling, and operational software that assumes pricing data exists and focuses on making it reliable, explainable, and actionable.

What Actually Changes in the Public Files

At a high level, the rule does five big things. It reorganizes in network rates around provider networks instead of duplicating the same rates across every plan that uses them. It requires new contextual machine readable files that explain what changed, what was actually used, and how adjudication logic works. It expands and stabilizes out of network allowed amount data so it is less sparse and more analyzable. It slows the update cadence for most files from monthly to quarterly. And it forces issuers to make the files easy to find by standardizing discovery.

Each of these changes sounds incremental. Together they change what kinds of products can be built without heroic effort.

On top of that, the rule also tightens participant facing disclosure by explicitly requiring that cost sharing estimates be available by phone, not just online or on paper, and clarifies that doing so satisfies the No Surprises Act price comparison tool requirement. That piece is less about data and more about operations, but it is where a lot of real money will move.

The Quiet but Massive Shift to Network Level Truth

One of the most consequential changes in the rule is also one of the least flashy. In network rate files will now be organized by provider network, not by plan or coverage option. If ten plans all use the same PPO network, the rates for that network get published once, not ten times.

This matters because network is the real unit of negotiation and decision making in commercial health care. Employers choose networks. Brokers sell networks. Consultants benchmark networks. Contracting teams negotiate networks. Plans have historically hidden behind plan proliferation as a way to make comparison harder. This rule strips that away.

By forcing network level files and requiring the common network name to be disclosed, the rule creates a cleaner identity layer for pricing data. It becomes much easier to say this network pays this much for these services, weighted by how often they are used. That is exactly the framing employers and advisors want when they are trying to understand spend.

The technical implementation matters here. Plans and issuers will be required to identify for each provider network every coverage option that uses that network. This maintains the plan to rate connection but removes the duplication. A researcher analyzing a specific network can grab one file instead of dozens. An employer comparing two networks for next year’s renewal can diff two files instead of hunting through hundreds.

There is also a subtle but important pricing representation change. In network rates must be expressed as dollar amounts except in the narrow case where the contract is explicitly a percentage of billed charges and cannot be translated into a dollar amount ahead of time. That pushes the data toward computable reality. Percent of billed charge contracts still exist, but they are increasingly treated as an edge case rather than the default.

The rule text is explicit that plans and issuers should define what constitutes a separate provider network according to their current business practices. The Departments are not imposing a taxonomy of networks. They are forcing disclosure of the taxonomy plans already use internally. That is significant because it means the data will reflect operational reality rather than a regulatory construct.

Why CMS Is Obsessed With Context All of a Sudden

The biggest conceptual shift in the proposal is the idea that raw rates are not enough. CMS is now explicitly requiring context to travel alongside the numbers. That context comes in three new machine readable files for each in network rate file.

The first is a change log file. This file identifies what changed since the last version of the in network file. From a developer perspective, this is a gift. Instead of re ingesting everything and computing diffs yourself, you get an official record of changes. From a compliance perspective, it creates a trail. Plans cannot quietly change rates without it being obvious. That alone creates a market for monitoring and alerting.

The second is a utilization file. This file documents which covered items and services actually had claims submitted and reimbursed over a defined twelve month period, ending six months before publication. It includes provider identifiers and place of service. This is the bridge between theoretical pricing and real spend. A negotiated rate that never gets used is trivia. A rate attached to high utilization services is a lever.

The third is a taxonomy file. This file discloses the plan or issuer’s internal provider taxonomy used in claims adjudication to decide whether a provider is appropriate for a given service. This is the logic that determines whether a claim gets paid or denied based on specialty. The rule then requires plans to use this same logic to exclude unlikely provider service combinations from the in network rate file.

This is a big deal. Plans already have this logic. It has just never been public. By forcing disclosure, the rule reduces garbage data and also exposes an internal decision layer that has historically been opaque. That creates risk for plans, but it also creates opportunity for vendors who can help manage, version, audit, and defend this logic.

The change log requirement becomes applicable on the first day of the calendar year quarter following the date on which the first in network rate file is required to be posted, and updated and posted quarterly whether or not there are changes. The utilization file is required beginning on the first day of the calendar year quarter following the applicability date and updated annually after the initial posting. The taxonomy file is required beginning on the first day of the calendar year quarter following the applicability date and updated and posted quarterly if changes to the internal provider taxonomy impact the information required in the in network rate file.

Utilization Data as the Missing Multiplier

If you talk to sophisticated employers or benefits consultants, the complaint about price transparency is always the same. Rates are interesting, but what matters is what people actually use. The utilization file directly addresses that.

By pairing rates with a standardized view of reimbursed services over a meaningful time window, the rule enables spend weighted analysis without stitching together external claims datasets. It does not replace full claims data, but it fills a gap that has forced many analytics vendors to rely on proprietary or licensed data sources.

The utilization file must document for a twelve month period ending six months prior to publication all items and services for which a claim was submitted and reimbursed. It must include each in network provider identified by NPI, TIN, and place of service code who was reimbursed in whole or in part. This is not sample data. This is census data for what actually happened.

For investors, the key point is that utilization context unlocks products that were previously too expensive or fragile to build. Network comparisons become more accurate. Contract optimization becomes less hypothetical. Benefit design modeling becomes more grounded. Even site of service analysis becomes easier when you know both the price and where care actually happened.

The proposal estimates the one time cost to build utilization files at over six hundred thirty eight million dollars across the industry. The annual ongoing cost is estimated at over nine million dollars. Those numbers represent the cost of compliance, but they also represent the pain point that software can address. Any vendor who can reduce the cost of generating, validating, or maintaining utilization files is selling into a market where the baseline cost is known and published.

Taxonomy Disclosure and the End of Contracting Theater

The taxonomy requirement deserves special attention because it will create friction inside plans and issuers. Internal provider taxonomies are messy. They evolve. They encode business rules that were never designed to be public. By forcing disclosure and alignment between adjudication logic and published rates, the rule collapses a long standing gap between what contracts say and what actually gets paid.

The rule requires plans and issuers to publish a taxonomy file that includes their internal provider taxonomy matching items and services represented by billing codes with provider specialties represented by specialty codes derived from the Health Care Provider Taxonomy code set established by NUCC. This taxonomy is used to determine if the plan or issuer should deny reimbursement for an item or service because it was not furnished by a provider in an appropriate specialty.

This is where a new class of tooling becomes inevitable. Taxonomy management is not something most organizations treat as a product. It lives in spreadsheets, legacy systems, and institutional memory. Once it becomes a compliance artifact that must be published, updated, and defended, it turns into software.

Expect to see products that treat taxonomy like code. Version control. Impact analysis. Testing against claims history. Audit trails. These are not sexy features, but they are the kind of features compliance buyers pay for when regulators start asking questions.

The proposal also requires plans and issuers to exclude from in network rate files any provider rate combination for items or services where the provider is unlikely to be reimbursed given that provider’s area of specialty according to the plan’s or issuer’s internal provider taxonomy. The one time cost estimate for this exclusion logic is over forty two million dollars. That is the cost of implementing the filtering. The ongoing maintenance is embedded in the taxonomy file updates.

Out of Network Data Finally Becomes Usable

Out of network allowed amount files have always been the weakest part of Transparency in Coverage. Too many codes never crossed the threshold to be reported. The windows were too short. The data was too noisy.

The proposal tackles this head on. It aggregates allowed amounts and billed charges at the health insurance market level rather than the plan level. It lowers the claim threshold from twenty to eleven. It expands the reporting period from ninety days to six months and the lookback window from six months to nine.

Each of these changes increases data density. Together they dramatically increase the likelihood that a given service appears in the file. That makes the data more useful for benchmarking, negotiation, and modeling out of network exposure.

This is not just academic. Out of network spend is still a meaningful driver of employer cost and member dissatisfaction. More stable benchmarks make it easier to design benefits, negotiate contracts, and evaluate vendor performance.

The market level aggregation is particularly clever. For self insured group health plans, health insurance market means all self insured group health plans maintained by the plan sponsor. For fully insured plans, it means the individual market, the large group market, or the small group market as defined in existing regulations. This creates natural aggregation pools that are big enough to hit the eleven claim threshold more consistently but small enough to preserve price signal by market segment.

The rule also permits self insured group health plans under certain circumstances to allow another party such as a service provider to aggregate allowed amount files for more than one self insured group health plan including those offered by different plan sponsors. This is the explicit permission structure that turns TPAs and ASOs into disclosure platforms.

Quarterly Cadence and the Death of Panic ETL

Another underappreciated change is the move from monthly to quarterly updates for in network and allowed amount files. Monthly cadence sounded good in theory. In practice it created constant ingestion pressure and made it hard for downstream users to finish analysis before the next update landed.

Quarterly cadence aligns better with how contracting and budgeting actually work. It reduces compute costs. It reduces storage and egress costs. It also reduces the likelihood of errors introduced by rushed updates.

For software companies, this changes product design. You can build around quarter over quarter change instead of constantly chasing the latest file. That makes analytics more stable and products easier to explain to buyers.

The proposal explicitly notes that plans, issuers, and researchers have indicated that since provider networks and rates do not change significantly from month to month, switching to a quarterly reporting cadence would not lead to a significant reduction in meaningful data. This reduced reporting cadence may also provide more time to analyze the data, as some file users have informed the Departments that they have difficulty keeping up with the pace of downloading and ingesting the file data monthly.

The benefits section of the proposal estimates annual cost savings of over two hundred fifty seven million dollars from reduced data cleaning, storage, discovery, and network egress costs driven by the combination of file size reduction and quarterly cadence. That number is split between plans, issuers, third party developers, and other file users. Those savings become margin expansion for compliance vendors and efficiency gains for analytics platforms.

Findability as a Regulatory Weapon

One of the most practical parts of the rule is also one of the most impactful. Plans and issuers will be required to publish a simple text file in the root of their website that points to the location of the machine readable files and names a contact person. They will also be required to include a standardized footer link labeled Price Transparency or Transparency in Coverage.

This does two things. It makes it trivial for crawlers to find the files without custom scraping logic. And it assigns responsibility. When there is a named contact, errors get reported and fixed faster.

The text file must be in a dot txt format located in the root folder of the plan or issuer’s website with information on the specific location of the machine readable files as well as contact information including a name and email address for those responsible for the files. The footer link must route directly to the publicly available web page that hosts the machine readable files.

From an investor perspective, this commoditizes basic ingestion. If your moat is that you can find the files, your moat is gone. If your value is what you do once you have them, this rule helps you.

The text file and footer requirements apply to all machine readable files including the prescription drug file. The text file must be posted beginning on the first day of the calendar year quarter following the applicability date and updated and posted as soon as practicable but no later than seven calendar days following a change in any of the required information.

The Phone Requirement and the Operational Reckoning

The participant facing side of the rule is where operations meet regulation. Plans and issuers will be required to provide cost sharing estimates and related disclosures by phone, using the same customer assistance number that appears on ID cards. The information must be accurate and provided at the time of the request.

This is not a small ask. Many existing tools are estimation engines with disclaimers. Phone delivery implies real time quoting and accountability. It also satisfies the No Surprises Act price comparison requirement, including for grandfathered plans that were otherwise exempt.

The rule proposes to allow plans and issuers to limit the number of providers with respect to which cost sharing information for covered items and services is provided to no fewer than twenty providers per day and to require plans and issuers to disclose the applicable provider per day limit to the participant, beneficiary, or enrollee when the request for information is made. This mirrors the existing limitation for paper requests.

This requirement will drive spending on call center tooling, agent assist software, workflow automation, and quality assurance. It creates an opportunity for vendors who can reduce handle time and error rates while creating compliance logs.

The proposal estimates the one time training cost for customer service representatives and supervisors at over thirteen million dollars. That is just training. The ongoing annual cost of providing phone based disclosure is estimated at over fifty two million dollars. Those are operational costs that software can compress.

The applicability date for the phone requirement is for plan years beginning on or after January 1, 2027. That gives the industry roughly two years from when the rule is finalized to build out the infrastructure. For vendors selling into this space, the clock is already ticking.

Costs, Burden, and What the Government Is Accidentally Telling Investors

The proposal includes detailed cost and benefit estimates. One time costs are dominated by building utilization files and change logs. Ongoing annual costs are driven by participant disclosures, utilization file maintenance, and responding to inquiries.

The total one time cost across the industry is estimated at over nine hundred thirteen million dollars. The total annual ongoing cost is estimated at over sixty eight million dollars. These are not rough guesses. These are line item estimates based on labor hours, systems development, and operational assumptions.

This is essentially a TAM estimate for vendors who can reduce these burdens. When the government says this will cost the industry hundreds of millions of dollars to implement, it is implicitly saying there is money to be saved by doing it better.

The benefits estimates focus on reduced data cleaning, storage, and discovery costs. That tells you where CMS thinks inefficiency lives today. Products that attack those inefficiencies directly are aligned with regulatory intent.

The cost estimates also reveal assumptions about how plans and issuers will comply. For example, the utilization file build cost assumes plans and issuers will need to develop new data pipelines to extract and format claims history. Any vendor who can sell a pre built pipeline or a managed service is competing against that baseline cost.

The Market Level Aggregation Gambit

The rule creates explicit permission for third party aggregation at a scale that did not previously exist. Self insured group health plans can allow another party such as a service provider with which they have an agreement to aggregate allowed amount files for more than one self insured group health plan including those offered by different plan sponsors.

This is not just a technical detail. This is the Departments saying that TPAs, ASOs, and other service providers can act as disclosure platforms. They can aggregate data across multiple employers, publish it once, and point all the individual plans to that aggregated file.

The same logic applies to in network rate files. Plans and issuers can allow another party to make available in a single in network rate file the information required for more than one plan, insurance policy, or contract including those offered by different plan sponsors across different health insurance markets.

This creates a natural consolidation point. Whoever operates the largest aggregation platform has the cleanest data, the most leverage with plans, and the best position to upsell adjacent services. This is where private equity can build roll up plays and where venture can fund platforms that aim to become the definitive source of truth.

The Percentage of Billed Charges Cleanup

The rule tightens the representation of negotiated rates. In network rates must be reflected as a dollar amount except for contractual arrangements under which plans and issuers agree to pay an in network provider a percentage of billed charges and are not able to assign a dollar amount to an item or service prior to a bill being generated.

This is a narrowing of when percentage of billed charges can be used. Previously, percentage of billed charges was treated as an acceptable rate representation. Now it is explicitly an exception that only applies when a dollar amount cannot be assigned in advance.

This matters because percentage of billed charges is inherently less transparent. The final payment amount depends on what the provider bills, which is not disclosed in the transparency file. By limiting when this representation can be used, the rule pushes the industry toward more concrete pricing.

The one time cost estimate for implementing this requirement is over seven million dollars. That cost is driven by the need to convert existing percentage of billed charges contracts into dollar amounts where possible and to document why conversion is not possible where it is not.

What This Means for Founders

The biggest mistake founders can make in this space is to chase consumer behavior change. The smarter move is to sell to organizations that already feel the pain and have budgets.

There is room for pricing data observability platforms that validate files, track changes, and flag anomalies. There is room for utilization weighted network analytics that inform contracting and benefit design. There is room for taxonomy governance tools that turn a compliance headache into a managed process. There is room for call center automation that turns a phone mandate into a cost reduction story.

The common thread is that these are enterprise products tied to operations, not consumer engagement.

The buyers are plans, issuers, TPAs, ASOs, benefits consultants, large employers, and state regulators. These are entities with procurement processes, multi year contracts, and willingness to pay for risk reduction. They are not consumers trying to save fifty dollars on an MRI.

The wedge is compliance. The expansion is optimization. The endgame is becoming embedded infrastructure.

Where Venture Capital Fits Cleanly

Venture capital fits best where there is horizontal software leverage and the potential for platform expansion. Data infrastructure, observability, and analytics layers that can be reused across many customers are good candidates.

Products that ingest, validate, and normalize transparency files at scale can sell to plans, issuers, consultants, and regulators. Products that automate change detection and impact analysis can sell to anyone who needs to monitor pricing or compliance. Products that layer utilization data on top of negotiated rates can sell to anyone trying to model total cost of care.

The key is distribution. Products that wedge into benefits consultants, TPAs, or large employers can scale. Products that require selling plan by plan with heavy customization will struggle unless they price accordingly.

The venture opportunity is also in tooling for the service providers who are aggregating data. If TPAs and ASOs become disclosure platforms, they need software to manage ingestion, aggregation, versioning, and distribution. That software can be sold as a recurring subscription with expansion revenue tied to the number of plans or volume of data.

The timing matters. The rule has a twelve month implementation period from when it is finalized. Assuming finalization in late 2025, the first network level files will be due in late 2026 or early 2027. The first utilization files will be due shortly after. Vendors who can get to market before the compliance deadline will capture the initial wave of spend.

Where Private Equity Has the Edge

Private equity shines where services and software intersect. Managed compliance, transparency operations, and TPA adjacent platforms are ripe for roll up. The rule explicitly allows third parties to publish files on behalf of plans, including aggregating across multiple self insured plans. That legitimizes service providers as disclosure operators.

Once a provider owns that role, upselling software becomes easier. Margins improve with automation. Contracts get sticky.

The roll up thesis is straightforward. Acquire regional TPAs or benefits administrators who are already managing transparency files for clients. Standardize the technology stack. Centralize data operations. Cross sell adjacent services like network analytics, contract benchmarking, and taxonomy management.

The rule also creates opportunity in the taxonomy and change log space. Plans and issuers will need help building, maintaining, and defending their internal taxonomies. That is a services business that can be productized over time. Start with consulting on taxonomy design. Sell managed taxonomy as a service. Build software to automate taxonomy updates and impact analysis. Roll it into the next acquisition.

Private equity can also target the call center software and services market. Plans and issuers will need to stand up or expand phone based disclosure capabilities. That creates demand for agent assist tools, IVR systems, quality assurance platforms, and outsourced call center services. Acquire a call center operator focused on health plans. Layer in software to reduce handle time and improve accuracy. Expand to serve other compliance disclosure requirements.

The Service Provider Play and Why It Matters

The rule creates explicit permission for service providers to act as disclosure platforms. Self insured group health plans can allow another party to aggregate and publish files on their behalf. That party can be a TPA, an ASO, a benefits consultant, or any other entity with whom the plan has an agreement.

This is significant because it shifts the unit economics of compliance. Instead of every plan building its own infrastructure, they can outsource to a platform that amortizes the cost across many clients. The platform gets recurring revenue. The plans get lower operational burden. The regulators get better data quality because platforms have more resources to invest in validation and error correction.

The platform play is especially attractive in the self insured market. There are millions of self insured plans sponsored by employers of all sizes. Most of them use TPAs to handle claims administration. Those TPAs already have access to the claims data needed to build utilization files and the contracts needed to populate in network rate files. They just need the software and processes to turn that data into compliant disclosures.

Whoever builds the best platform for TPAs wins. That platform needs to ingest claims history, map it to the required file schemas, generate change logs, apply taxonomy exclusions, and serve the files with the required metadata. It needs to handle versioning, error correction, and inquiry response. It needs to scale to hundreds or thousands of plans without requiring custom work for each one.

The Compliance Software Stack Nobody Sees Coming

The rule will create demand for an entire stack of compliance software that does not yet exist or exists only in fragmented form. At the bottom of the stack is file generation. Plans and issuers need tools to extract data from their systems, apply the required transformations, and output compliant files. This is ETL for regulatory disclosure.

Above that is validation. Files need to be checked for schema compliance, logical consistency, and completeness. Errors need to be flagged and corrected before publication. This is where data quality platforms fit.

Above that is monitoring. Files need to be tracked over time. Changes need to be detected and explained. Anomalies need to be investigated. This is where observability platforms fit.

Above that is analytics. The data in the files needs to be turned into insights. Network performance needs to be benchmarked. Utilization trends need to be tracked. Contract opportunities need to be identified. This is where BI and analytics platforms fit.

And at the top of the stack is governance. Taxonomies need to be managed. Change logs need to be audited. Inquiries need to be responded to. This is where workflow and compliance management platforms fit.

Each layer creates a business. Each layer can be sold separately or bundled. Each layer has different buyers and different competitive dynamics. But they all share a common foundation, which is that the rule creates demand that did not exist before.

Deep Analysis of Investment Opportunities by Vertical

The taxonomy management vertical is particularly interesting because it sits at the intersection of compliance and operations. Plans and issuers have always had internal taxonomies, but they have never had to publish them or defend them. Now they do. That creates demand for tools that make taxonomies auditable, versionable, and testable.

A taxonomy management platform needs to import existing taxonomies from whatever format they currently live in, map them to the NUCC code set, identify gaps and inconsistencies, simulate the impact of changes, and generate compliant taxonomy files. It also needs to track changes over time and provide an audit trail.

The market for this is every plan and issuer. The urgency is the compliance deadline. The expansion is helping plans optimize their taxonomies to reduce denials, improve provider satisfaction, and defend against audits.

The network analytics vertical is interesting because utilization data makes network analysis more accurate. Right now, most network analytics are based on contracted rates without weighting for actual use. The utilization file changes that. Analytics platforms that can join negotiated rates with utilization data can produce spend weighted benchmarks that are much more useful for contract negotiation and benefit design.

The market for this is employers, benefits consultants, and brokers. The pitch is better data leads to better decisions leads to lower costs. The pricing model is likely subscription with expansion based on number of employees or total spend under management.

The change detection and monitoring vertical is interesting because it turns a compliance requirement into an operational capability. Plans and issuers will publish change log files every quarter. Someone needs to ingest those files, compare them to prior versions, flag meaningful changes, and alert stakeholders. That someone could be internal staff, or it could be a monitoring platform.

The market for this is broad. Employers want to know when their network’s rates change. Consultants want to know when their clients’ competitors change rates. Regulators want to know when outlier changes happen. Providers want to know when their contracted rates get published correctly. A monitoring platform that serves all these constituencies at once has serious revenue potential.

The call center automation vertical is interesting because the phone requirement creates an immediate operational problem with a known cost. Plans and issuers estimate they will spend over thirteen million dollars on training and over fifty two million annually on ongoing phone operations. Any vendor who can reduce handle time, improve accuracy, or automate responses is competing against that baseline.

The opportunity is agent assist software that sits alongside the existing cost sharing estimation tools and helps representatives navigate phone requests in real time. The software needs to pull the same data that powers the online tool, format it for verbal delivery, handle the twenty provider per day limit, log the interaction for compliance, and do it fast enough that call times stay reasonable.

The more ambitious play is full automation through conversational AI. If the cost sharing estimation logic is already codified for the online tool, it can be wrapped in a voice interface. The challenge is handling edge cases, maintaining accuracy requirements, and building trust with callers who expect a human. But the economics are compelling if it works.

The utilization file generation vertical is interesting because the one time cost is over six hundred million dollars and the annual maintenance cost is over nine million. That is the cost of building custom pipelines to extract twelve months of claims history, filter to reimbursed claims only, group by item or service code, attach provider identifiers, and format for disclosure.

Most plans and issuers do not have this capability today. Claims data lives in adjudication systems that were not built for this kind of batch extraction. The data needs to be joined across multiple tables, deduplicated, validated, and mapped to the required schema. That is classic data engineering work that can be productized.

A utilization file platform needs to connect to common claims platforms, handle the extraction and transformation logic, apply the six month lag requirement, generate the files on the required annual cadence, and version them appropriately. The buyer is anyone responsible for transparency compliance at a plan or issuer. The pricing is either per plan, per file, or per claim volume.

The file hosting and distribution vertical is less obvious but still real. Plans and issuers need to serve potentially massive files from publicly accessible URLs with high availability and low latency. The files need to be discoverable via the text file and footer link requirements. The hosting environment needs to handle traffic spikes when researchers or vendors scrape all the files at once.

Most plans and issuers are not set up for this. They are used to serving member portals and broker sites, not public data repositories. Standing up the infrastructure is a one time cost, but maintaining it is ongoing. A managed hosting service that handles file storage, distribution, metadata management, and access logging could sell into plans and issuers who do not want to build this themselves.

The aggregation platform vertical is where the biggest outcome potential lives. The rule explicitly allows third parties to aggregate files across multiple plans. Whoever builds the dominant aggregation platform becomes the de facto source of truth for transparency data. They can monetize through subscriptions to plans for the compliance service, subscriptions to data users for access and analytics, and transaction fees for any marketplace features they layer on top.

The aggregation platform needs to ingest files from multiple sources, normalize them to a common schema, deduplicate rates across plans that use the same networks, apply quality checks, version everything, and expose it through APIs and bulk download. It also needs to handle the change log aggregation problem, where changes from multiple underlying plans need to be rolled up into a coherent view.

The moat is data quality and coverage. The platform that has the cleanest data and the most complete coverage becomes the default choice. Network effects kick in as more plans join because data users prefer platforms with broader coverage, and more data users join because the platform has better data.

How to Underwrite This Wave Without Getting Burned

Underwriting in this space requires discipline. The first rule is to look for companies that treat the public data as an input, not gospel. The transparency files will have errors. Rates will be stale. Coverage will be incomplete. Companies that assume the data is perfect will build fragile products. Companies that assume the data is messy and build validation and correction into their workflows will build resilient products.

The second rule is to look for alignment with buyer incentives. The buyers here are not consumers. They are enterprises trying to reduce cost, manage risk, or comply with regulations. Products need to map to budget line items that already exist. Compliance software competes against legal and regulatory risk. Analytics competes against consulting fees. Automation competes against internal labor costs.

The third rule is to be wary of moats based on ingestion alone. The text file and footer requirements commoditize file discovery. The network level organization reduces the complexity of joining data across plans. The change log files reduce the need for custom diff logic. Ingestion used to be a meaningful technical challenge. Now it is table stakes. The value has to be in what you do with the data once you have it.

The fourth rule is to pay attention to regulatory timing but not bet everything on final rules. This is a proposed rule with a sixty day comment period. The final rule could change. The implementation timeline could shift. The enforcement approach could evolve. Companies that are only viable if the rule lands exactly as proposed are taking regulatory risk. Companies that solve problems that exist regardless of the specific rule text are more robust.

The fifth rule is to favor companies that reduce operational cost or create negotiating leverage. Those are the budgets that renew. Employers will pay for tools that help them negotiate better network rates. Plans will pay for tools that reduce the cost of compliance. Consultants will pay for tools that make their analysis faster and more accurate. Those are recurring revenue streams tied to ongoing pain points.

The sixth rule is to watch for concentration risk. If a company’s entire revenue model depends on selling to health plans and there are only a few hundred meaningful buyers in the country, customer concentration becomes an existential risk. Products that can sell to plans, issuers, employers, consultants, and service providers have more diversified revenue streams.

The seventh rule is to be realistic about sales cycles and implementation timelines. Enterprise software sales to health plans are slow. Compliance deadlines create urgency, but procurement processes do not move faster just because the deadline is tight. Companies need enough runway to survive twelve to eighteen month sales cycles and another six to twelve months of implementation before they see meaningful recurring revenue.

The eighth rule is to understand that this wave will have multiple phases. The first phase is compliance. Companies sell software and services that help plans and issuers meet the basic requirements. The second phase is optimization. Companies sell analytics and insights that help buyers use the data to make better decisions. The third phase is transformation. Companies sell platforms that fundamentally change how healthcare pricing and contracting work. Most companies will only ever get to phase one or two. The ones that get to phase three are the ones that become generational outcomes.

Closing Thoughts on What Actually Gets Built

This proposed rule will not make patients perfect shoppers. It will not magically fix healthcare pricing. What it will do is force the industry to emit cleaner, contextualized pricing telemetry that can be used by people who already care deeply about cost.

For investors and entrepreneurs, that is enough. The opportunity is not to change human behavior. It is to sell better tools to the humans already tasked with managing billions of dollars in spend.

The rule creates explicit permission structures for third party aggregation, mandates the disclosure of internal business logic that has never been public, establishes new operational requirements that will cost hundreds of millions to implement, and does all of this on a timeline that gives vendors roughly eighteen months to get to market before the compliance deadline hits.

The government has essentially published a detailed TAM estimate, identified the specific pain points, estimated the baseline costs, and told the industry that third parties are allowed to build infrastructure to solve these problems at scale. That is not a regulatory headache. That is an investor roadmap.

The companies that win will be the ones that understand this is fundamentally about reducing operational burden and creating decision leverage for sophisticated buyers, not about empowering consumers to shop for care. They will sell into existing budget lines, integrate with existing workflows, and solve problems that renew annually.

The products that matter will be boring. File validation tools. Taxonomy management platforms. Change detection services. Utilization weighted network analytics. Call center automation. Aggregation infrastructure. These are not exciting demos. But they are what enterprises will actually buy.

The exits that work will be to buyers who care about infrastructure and data quality, not consumer engagement metrics. Strategic buyers will be larger health tech platforms that need pricing data as a feature. Financial buyers will be private equity firms that understand how to roll up fragmented service providers and cross sell software. The IPO path exists but requires getting to meaningful scale in a market with concentrated buyers and long sales cycles.

The timing is now. The rule is proposed but the direction is clear. The compliance deadline is coming. The industry is unprepared. The technology does not exist in productized form. The winners will be the companies that launch in the next twelve months, sign their first customers in the next eighteen months, and have revenue at scale before the compliance deadline hits.

This is not a consumer transparency play. This is an enterprise infrastructure play disguised as a transparency rule. The investors who see it clearly will fund the companies that build the plumbing. The ones who chase the consumer narrative will fund companies that struggle to find buyers.

The price transparency stack is real. The investment opportunity is real. The question is who builds it and who funds them.​​​​​​​​​​​​​​​​

Abstract

This essay outlines a private equity rollup strategy targeting fragmented durable medical equipment suppliers ahead of CMS’s 2026 DMEPOS Competitive Bidding Program restructuring. The regulatory shift to nationwide Remote Item Delivery with constrained contract awards (4-10 suppliers per category) creates a 24-month window to consolidate regional operators, achieve scale economies, and position for dominant market share capture across multiple product categories.

Key investment thesis elements:

- Acquisition universe: 150-200 regional DME suppliers doing $5-50M revenue with 8-15% EBITDA margins

- Rollup timeline: 18-24 months to acquire 8-12 platforms, integrate operations, and prepare competitive bids

- Total equity check: $200-300M across platform acquisitions, bolt-ons, and operational transformation

- Revenue opportunity: $500M-1B annually post-contract award across 3-4 product categories

- Exit multiple expansion: Entry at 5-7x EBITDA, exit at 12-15x on improved margins and regulatory moats

- Hold period: 4-5 years including 2-year build/bid period and 2-3 year contract optimization

Table of Contents

Why the Rollup Window Is Right Now

The Acquisition Targets and How to Find Them

Deal Structure and Integration Playbook

Building the National Platform While You Roll

The Bid Strategy and Why Scale Wins

Post-Award Value Creation Levers

Exit Scenarios and Expected Returns

Why the Rollup Window Is Right Now

Most PE healthcare services deals I see are either too early (market not mature enough, regulatory uncertainty) or too late (strategics already consolidating, multiples inflated). This one sits in a perfect window that closes in about 18 months, and I think there are maybe two or three firms positioned to actually execute on it.

Here’s the regulatory catalyst. CMS just published the final rule for the 2026 DMEPOS Competitive Bidding Program, and buried in the details is a complete restructuring of how Medicare pays for high-volume medical devices and supplies. Starting January 2028, seven product categories move to a nationwide Remote Item Delivery model where CMS awards only 4-10 national contracts per category. The current market has probably 200-300 suppliers per category across regional competitive bidding areas. You’re going from a fragmented, regionally competitive market to a national oligopoly with 95%+ supplier reduction.

The bid window opens late summer 2026, contracts get awarded late summer 2027, and the switch flips January 1, 2028. So you’ve got roughly 24 months from today to build a platform that can win multiple national contracts. That timeline is too short for a startup to build from scratch (they’d need to raise capital, build infrastructure, get accreditation, hire teams). It’s too short for most strategics to pivot their business models (legacy DME suppliers are regional, not national, and device manufacturers don’t have distribution capabilities). But it’s perfect for a PE rollup that can acquire existing operators, consolidate their EBITDA, integrate operations, and deploy serious capital to build the national infrastructure needed to compete.

The categories in scope are massive. Continuous glucose monitors and insulin pumps represent about $4B in annual Medicare spend. Urological supplies, ostomy supplies, and hydrophilic catheters are another $3-4B combined. Off-the-shelf braces (back, knee, upper extremity) are maybe $2-3B. Total addressable market across all seven categories is probably $10-12B in annual Medicare revenue, and you need to win contracts to participate at all. There’s no opt-in at fee schedule rates, there’s no secondary market. If you’re not one of the 4-10 contract winners per category, you cannot bill Medicare for these products.

Now think about the current supplier landscape. The market is super fragmented with a mix of regional DME suppliers (guys doing $10-30M revenue serving specific metro areas), specialty distributors focused on diabetes or ostomy or urology (doing $20-80M revenue), and a few larger players like Byram Healthcare or Edgepark that have some national presence but operate regionally. Most of these companies are family-owned or held by small PE funds that bought them 5-7 years ago and are looking for exits. Their multiples are compressed (5-7x EBITDA) because the market perceives them as commodity businesses with Medicare reimbursement risk.

But here’s what changes with the RID model. If you roll up 8-12 of these regional operators, you get immediate EBITDA consolidation (they’re all doing 8-15% margins, you can get to 20-25% with centralized ops). You get national infrastructure that no single target has (warehouse network, customer service, compliance capabilities). You get scale to negotiate better pricing with device manufacturers (Dexcom, Abbott, Medtronic all want large, reliable distribution partners). And most importantly, you get the operational credibility and financial capacity to submit winning bids across multiple categories.

The targets are cheap right now because they don’t see the opportunity (most operators are thinking defensively about how to survive competitive bidding, not offensively about how to dominate it). You can probably buy decent regional operators at 5-6x EBITDA, maybe 6-7x for better ones with good customer bases. If you roll up $150M in revenue at blended 12% EBITDA margins (so $18M EBITDA) and pay 6x, that’s $108M in purchase price plus maybe $20-30M in integration costs and working capital. Call it $130-140M in total capital to build a platform doing $150M revenue.

Then you spend another 12-18 months and $60-80M building the national infrastructure (technology platform, logistics network, compliance systems, clinical support). You submit bids in late 2026, win 3-4 category contracts in 2027, and by 2029 you’re doing $500-800M in revenue at 22-25% EBITDA margins (call it $110-200M EBITDA). At a 12-15x exit multiple (justified by the regulatory moats, recurring revenue, and market dominance), you’re looking at a $1.3-3B exit on $200-300M of total invested capital. That’s a 4-6x MOIC in 4-5 years, which is exactly what healthcare services PE funds are targeting.

The window closes once the bid process starts because you can’t acquire your way into contracts after they’re awarded. You need to have the platform built and ready to bid by summer 2026. So the actionable timeline is: close fund or allocate capital by Q1 2026, execute first 3-4 acquisitions by Q3 2026, complete remaining acquisitions and integration by Q1 2027, submit bids late summer 2026, receive contract awards fall 2027, optimize and scale through 2028-2029, exit 2030-2031.

The Acquisition Targets and How to Find Them

The ideal target profile is a regional DME supplier doing $15-40M in revenue with strong concentrations in the RID categories (diabetes, urology, ostomy, bracing). You want companies that are profitable (10-15% EBITDA), have established physician relationships, maintain good compliance and accreditation, and are owned by founders ready to exit or by PE funds at end of hold period.

There are probably 150-200 companies in the US that fit this profile. Most are not marketed publicly because DME is an unsexy, low-margin business that doesn’t attract broad buyer interest. The deal flow comes from three sources: healthcare-focused M&A intermediaries (firms like VERTESS, Blair, Provident), direct outreach to known operators (you can identify them through Medicare provider databases and industry associations like AAHomecare), and off-market opportunities through industry relationships.

Let me sketch out what the target list looks like by category:

For diabetes supplies, you’re looking at regional distributors that serve endocrinology practices and have relationships with Dexcom, Abbott, Medtronic. Companies like US Med (although they’re probably too big at $200M+ revenue and would be expensive), Liberty Medical (owned by a large PE fund), or smaller regional players doing $20-50M that focus on specific states or metro areas. The key criteria are high CGM penetration (not just test strips and basic supplies), good reorder rates (indicating customer satisfaction), and strong payer relationships beyond Medicare.

For urology and ostomy, you’re targeting specialty distributors like 180 Medical, Parthenon (although again, probably too large), or regional suppliers that came out of hospital-based home health agencies. These companies typically have clinical staff (nurses, wound care specialists) that provide patient education, which is valuable for the RID model where you need to deliver clinical support remotely. The margins in these categories are decent (12-18% EBITDA) because the products are recurring and the customer switching costs are high.

For bracing, you want companies that serve orthopedic practices and have relationships with brace manufacturers. This category is more transactional (one-time purchases vs recurring supplies) but the margins are higher (20-25% gross margins). The operational requirements are simpler (no monthly refills, less customer service intensity), so these make good bolt-on acquisitions to add product breadth.

The acquisition strategy is to build a core platform through 2-3 anchor acquisitions, then bolt on 5-8 smaller targets for geographic coverage and category expansion. Your anchor acquisitions should be in the $25-50M revenue range, ideally with some multi-category presence. You’re paying 6-7x EBITDA, so call it $15-35M per anchor deal. Then your bolt-ons are $10-20M revenue companies at 5-6x EBITDA, so $5-12M per deal.

Let’s model this out. You do three anchor acquisitions at an average $30M revenue and $3.6M EBITDA (12% margin) at 6.5x, so $23M per deal, $70M total. You do seven bolt-on deals at an average $15M revenue and $2M EBITDA (13% margin) at 5.5x, so $11M per deal, $77M total. Total acquisition spend is $147M for companies doing $195M in aggregate revenue and $26M in aggregate EBITDA. Add in $25M for integration, working capital, and deal costs, and you’re at $172M in total capital for the rollup phase.

The deal structure is pretty standard PE buyout. You’re doing management rollovers for the anchor deals (keep 10-20% of equity for existing management to stay on and run the integrated business), you’re paying mostly cash at close with some earnouts tied to integration milestones or EBITDA performance. For founder-owned businesses, you might do seller notes to bridge valuation gaps or defer some consideration. For PE-owned businesses, it’s cleaner: just negotiate price, do QoE diligence, and close quickly.

The diligence focus is different than typical healthcare services deals. You care less about payer contracts (Medicare rates are standardized) and more about operational metrics: customer retention rates, reorder frequency, documentation quality (because CMS audits medical necessity), inventory management, and compliance track record. You want to see clean accreditation status, no recent Medicare audits or sanctions, good relationships with manufacturers, and strong NPS scores from beneficiaries.

You also want geographic diversity to demonstrate national capability in your bids. If all your targets are in the Southeast, you’re going to struggle to convince CMS you can serve Alaska or Montana. So you’re deliberately looking for targets in different regions: a few in the Southeast, a few in the Midwest, one or two in the West, maybe one in the Northeast. That gives you the credibility to say you have national infrastructure.

Deal Structure and Integration Playbook

The hardest part of any rollup is integration, and most PE firms screw this up by either going too fast (cut costs aggressively, lose customers and employees) or too slow (keep redundant systems and teams, never capture synergies). The right approach for this deal is a phased integration that prioritizes speed on technology and compliance while being patient on customer migration.

Phase one is day one integration of corporate functions. You immediately consolidate finance, HR, legal, and executive leadership. You pick the best CFO from your anchor acquisitions (or hire externally), same with Chief Compliance Officer and General Counsel. You shut down redundant back-office systems and move everyone to a single ERP (probably NetSuite or Sage Intacct for a business this size). This saves you 3-5% of revenue in G&A costs within 90 days.

Phase two is technology platform migration over 6-9 months. This is the critical path for the RID strategy because you need a single, national order management system that can integrate with EHRs, handle Medicare billing, track inventory across warehouses, and provide customer service tools. None of your acquired companies will have this (they’re all running on legacy DME software like Brightree or Fastrack, which are functional but not designed for national scale).

You have two options here. Option one is build custom on modern cloud infrastructure, which takes 12-18 months and costs $5-8M but gives you exactly what you need. Option two is buy a platform from a healthcare IT vendor and customize it, which is faster (6-9 months) and cheaper ($2-4M) but less tailored. I’d probably go with option two for speed, then plan to rebuild custom post-contract award once you have revenue and can justify the investment.

Phase three is warehouse and logistics consolidation over 12 months. Your acquired companies probably have 10-15 small warehouses scattered across the country, each doing their own inventory management and fulfillment. You need to consolidate to 3-4 regional distribution centers (East, Central, West, maybe Southeast) that can serve the entire country. You negotiate with 3PL partners (like Geodis or AmeriCold or a healthcare-specific logistics provider) to take over warehousing and fulfillment. This reduces inventory carrying costs (you’re pooling inventory across locations instead of stocking everything everywhere) and improves delivery times (better routing and fewer handoffs).

Phase four is customer service centralization over 6-9 months. You build a single national call center (probably in a lower-cost location like Texas, Tennessee, or remote/distributed) and migrate all customer service reps to standardized processes and tools. You invest in training so reps can handle all product categories, not just the specific category their legacy company served. You implement Zendesk or Salesforce Service Cloud with good telephony integration and knowledge management. This improves customer experience (faster answer times, better first-call resolution) and reduces headcount (you need fewer reps when you’re pooling volume).

Phase five is clinical support and quality assurance over 6-12 months. You hire a Chief Clinical Officer (probably a nurse or respiratory therapist with DME experience) to build standardized clinical protocols, staff a centralized clinical team, and implement quality monitoring. This is important for the RID model where you need to demonstrate clinical outcomes and patient satisfaction to CMS. You’re tracking metrics like device adherence (are patients actually using their CGMs?), complication rates (pressure ulcers from poor fitting braces, UTIs from improper catheter use), and patient-reported outcomes.

The integration timeline is aggressive but achievable. You’re running all five phases in parallel, which means you need a strong integration management office (hire a VP of Integration with consulting or PE portfolio company experience, give them 2-3 project managers) and you need to over-communicate with employees about what’s changing and why. The total integration cost is probably $20-25M (technology platform, severance for redundant roles, facility closures, change management), which you’ve already budgeted in the $172M total capital number.

The synergies are real and meaningful. You’re taking companies doing aggregate 13-14% EBITDA margins and getting them to 18-20% post-integration through:

Reduced COGS from better manufacturer pricing (you’re now buying $150M+ in products vs $15-30M per legacy company). This probably saves you 3-5% on COGS, which at 60% COGS as a percent of revenue is worth 2-3% EBITDA margin improvement.

Lower fulfillment costs from consolidated logistics (fewer warehouses, better routing, volume discounts with shippers). This saves maybe 1-2% of revenue.

Reduced G&A from centralized corporate functions (you need one finance team, one HR team, one legal team, not eight). This saves 2-3% of revenue.

Improved revenue retention from better customer experience (national platform, better technology, faster response times). This doesn’t show up as margin improvement but does show up as revenue growth (you’re retaining 95%+ of customers vs 85-90% for legacy operators).

All in, you’re getting from 13-14% EBITDA margins to 18-20% within 18-24 months post-acquisition. On $195M in baseline revenue, that’s going from $26M EBITDA to $36-39M EBITDA, so $10-13M in annual synergies. At a 12x exit multiple, that synergy capture alone is worth $120-156M in enterprise value creation.

Building the National Platform While You Roll

The parallel workstream to the rollup is building the greenfield capabilities you need to compete nationally. Even with 8-12 acquisitions, you’re not going to have everything required for the RID model. You need to invest $60-80M in new capabilities across technology, logistics, compliance, and clinical support.

On technology, you’re building or buying an order management platform that can handle the entire patient journey. Physician e-prescribes a CGM through Epic, the script flows into your system via HL7 or FHIR integration, your system automatically checks patient eligibility with Medicare, retrieves prior medical records to verify medical necessity, generates the required documentation, submits to Medicare for approval, and notifies the patient of approval status. Then the patient chooses delivery method (ship to home, pick up at pharmacy partner, pick up at retail location), the order routes to the appropriate warehouse, the product ships with tracking, and the patient receives automated onboarding (video tutorials, text-based support, connection to live clinical support if needed). Post-delivery, the system tracks device usage (via manufacturer APIs for CGMs and insulin pumps), monitors for issues, and automatically triggers reorder workflows every 90 days.

This is not trivial to build, but it’s also not rocket science. You’re talking about 8-12 engineers over 12-18 months, mostly backend and integration work, with some frontend for patient and internal user interfaces. Total cost is $3-5M for the build plus $500K-1M annually for maintenance and enhancements. You can accelerate by buying a base platform from a DME software vendor (like Brightree or Kareo or a newer player) and customizing it, which might cut the timeline to 6-9 months and the cost to $2-3M.

On logistics, you’re building a national distribution network that can deliver to all 50 states within 1-2 business days. Your 3-4 regional distribution centers give you geographic coverage, but you also need last-mile delivery partnerships (FedEx, UPS, regional couriers) and pickup location agreements (with pharmacy chains like Walgreens or CVS, or with urgent care networks, or with retail partners who have local presence). The pickup location piece is interesting because it differentiates you from pure mail-order competitors and appeals to beneficiaries who want in-person handoff for expensive devices.

The capital requirement for logistics is mostly working capital (inventory) plus some facility setup. You need $10-15M in inventory across your product categories (CGMs, insulin pumps, catheters, ostomy supplies, braces). You need maybe $2-3M for warehouse equipment and setup (racking, RF scanning, inventory management systems). And you need $1-2M for delivery partnerships and pickup location agreements (mostly onboarding costs and integration, the actual delivery costs are variable and get billed to individual orders).

On compliance, you’re building a best-in-class program that exceeds CMS requirements and positions you well for bids. You hire a Chief Compliance Officer (probably someone from a large DME supplier or a consulting firm like PwC or Deloitte that does healthcare regulatory work), a few compliance analysts, and you invest in technology for documentation management, audit trails, and quality monitoring. You implement automated processes for verifying medical necessity (using AI/NLP to extract relevant info from physician notes), tracking product quality (monitoring manufacturer recalls, patient complaints, device failures), and managing audits (CMS does regular audits of contract suppliers, you need to be able to respond quickly with complete documentation).

The compliance investment is probably $2-3M in upfront costs (hiring, technology, process design) and $1-2M annually for ongoing operations. This sounds expensive but it’s essential for two reasons. One, you need clean compliance to win bids (CMS reviews your compliance track record as part of bid evaluation). Two, you need robust compliance to avoid penalties post-award (contract suppliers can be terminated for compliance failures, which would be catastrophic).

On clinical support, you’re building a team of diabetes educators, respiratory therapists, wound care nurses, and other specialists who can provide patient education and clinical guidance. The RID model requires you to deliver products remotely, which means you can’t rely on in-person education and fitting like traditional DME suppliers do. You need to train patients via video, phone, or text on how to use their devices correctly, how to troubleshoot issues, when to call for help, and how to manage their underlying conditions.

The clinical team size depends on your projected patient volume, but for 50-80K patients across all categories you probably need 15-20 clinical staff. These are mostly contractors or part-time employees (you hire per-diem nurses and educators who work flexible hours to handle patient onboarding and support calls). The cost is $2-3M annually, but it drives huge value in terms of patient outcomes (better device adherence, fewer complications) and customer satisfaction (NPS in the 70-80 range vs 30-40 for competitors).

All in, the greenfield investment is $60-80M over 18-24 months. Add that to the $172M for acquisitions and integration, and you’re at $232-252M in total capital. That’s a meaningful check, but it’s totally achievable for a mid-size healthcare PE fund ($1-2B in AUM) as a large platform investment, or for a larger fund ($3-5B+ AUM) as a normal platform deal.

The Bid Strategy and Why Scale Wins

The actual bid submission is where all the work comes together. CMS evaluates bids on two dimensions: price (your proposed payment amount) and capability (your operational plan and track record). The price dimension is straightforward - lower bids are better, subject to the bona fide bid review process where CMS rejects unrealistically low bids. The capability dimension is more subjective - CMS looks at your accreditation status, compliance history, financial stability, operational infrastructure, and beneficiary satisfaction.

Your bid strategy is to price aggressively but not recklessly, and to differentiate heavily on capability. On price, you want to bid in the 25th-35th percentile of the expected bid distribution. You have better cost structure than most competitors (economies of scale from the rollup, better manufacturer pricing, efficient logistics) so you can afford to bid lower while maintaining 45-50% gross margins. You’re not trying to be the absolute lowest bidder (that triggers bona fide review scrutiny), you’re trying to be low enough to win while high enough to be credible.

CMS calculates the Single Payment Amount at the 75th percentile of winning bids, so if you bid at the 30th percentile and the 75th percentile comes in 25-30% higher, you’re getting paid 25-30% above your cost structure. That’s huge margin expansion compared to your baseline business.

On capability, you’re showcasing the national infrastructure you built through the rollup. You demonstrate that you have warehouses in multiple regions, delivery partnerships covering all 50 states, clinical staff to support patient education, technology platforms for seamless ordering and tracking, and a track record of high beneficiary satisfaction. You highlight your compliance program, your accreditation status, your financial stability (backed by a PE fund with hundreds of millions in AUM), and your management team’s experience.

The capability narrative is essentially: “We’re the only bidder with true national scale, modern technology, and the financial backing to deliver exceptional service to Medicare beneficiaries everywhere.” Most of your competitors are regional operators bidding up to national (they lack the infrastructure and credibility), or device manufacturers bidding down to distribution (they lack the logistics and customer service capabilities), or legacy DME suppliers with outdated technology and fragmented operations. You’re positioned as the modern, scaled, well-capitalized alternative.

You submit bids across 4-5 categories. The obvious ones are CGM/insulin pumps (biggest market, highest value), urological supplies (good margins, recurring revenue), and ostomy supplies (similar to urology). Then you decide whether to bid on the brace categories (back, knee, upper extremity). The braces are lower revenue per patient but also lower operational complexity, so they’re good margin enhancers if you win.

The bid amounts vary by category based on your cost structure and market dynamics. For CGMs, the bid limit is around $273/month (using 2025 fee schedules adjusted for 2026 inflation), and you probably bid $210-230 depending on your COGS and competitive positioning. For urological and ostomy supplies, you bid at 20-30% below current fee schedules. For braces, you bid at 15-25% below fee schedules. In every case, you’re pricing to win while maintaining strong margins.

The contracts get awarded late summer/fall 2027, and realistically you should win 3-4 categories with this strategy. Winning all 4-5 would be great but probably optimistic (CMS wants supplier diversity, so they might not award multiple categories to the same bidder). Winning 2 would be disappointing but still valuable. The base case is 3 categories, which gets you into CGM/insulin pumps plus two of the supply/brace categories.

Post-Award Value Creation Levers

January 1, 2028 is when the real work begins. You’ve spent 24 months rolling up companies and building infrastructure, now you need to execute on patient acquisition, operational excellence, and margin expansion.

The patient acquisition strategy is all about the transition period. For the first six months of 2028, existing Medicare beneficiaries using non-contract suppliers need to switch to contract suppliers. CMS requires contract suppliers to do beneficiary education, and physicians are notified about the transition. Your job is to make switching as easy as possible and to capture as many patients as you can.

You run a multi-channel outreach campaign. One, you work directly with physicians and endocrinology practices to communicate the transition and encourage them to e-prescribe to your platform. You hire 10-15 sales reps who call on high-volume prescribers and make the case for why you’re the best contract supplier (better technology, faster delivery, superior clinical support). Two, you do direct-to-beneficiary outreach via mail, phone, and potentially digital channels (email, text) for beneficiaries where you have contact info from your legacy acquired companies. Three, you optimize for organic discovery through Medicare’s supplier directory and 1-800-MEDICARE, where beneficiaries can search for contract suppliers by location and product.

The target is to capture 15-20% market share across your contract categories by end of 2028. Total market is probably 300-400K beneficiaries across CGM/insulin pumps, urology, and ostomy (assuming those are your three categories). At 15-20% share, you’re serving 45-80K beneficiaries. Average revenue per beneficiary varies by category but blends to maybe $2,200-2,500 annually (CGMs are $3,000+, supplies are $1,500-2,000). So you’re doing $100-200M in annualized revenue by end of year one.

The operational excellence focus is on customer experience and clinical outcomes. You track NPS religiously and target 70+ (vs industry average of 30-40). You measure device adherence for CGMs (are patients wearing sensors consistently?) and aim for 85%+ (vs industry average of 60-70%). You monitor complications and adverse events and drive them below industry benchmarks. You track reorder rates and retention (95%+ of patients should stay with you once they’re onboarded).

The margin expansion comes from scaling fixed costs across growing revenue. Your technology platform, corporate G&A, and compliance functions are largely fixed, so as you grow from $150M to $300M to $500M revenue, those costs as a percent of revenue decline from 8-10% to 5-6%. Your logistics costs are semi-variable (some fixed costs for warehouses and systems, variable costs for shipping and handling), so you get some margin benefit from density. Your clinical support is variable but you get efficiency gains from better tooling and standardized protocols.

The overall margin trajectory is from 18-20% EBITDA at baseline (post-rollup integration) to 22-25% EBITDA at scale (year 2-3 of contract). On $500M revenue, that’s $110-125M in EBITDA. You’re also growing revenue because the Medicare beneficiary population grows (10K new Medicare enrollees per day, plus increasing CGM adoption rates as technology improves and coverage expands). So by year three you might be doing $600-700M revenue at 23-25% EBITDA, which is $138-175M EBITDA.

The other value creation lever is category expansion and contract renewal. When the next competitive bidding round comes around (bids in 2029 for 2031 contracts), you’re the incumbent with proven operational excellence and strong beneficiary satisfaction. You should be able to retain your existing categories and potentially add 1-2 more. You also have the option to expand into non-RID categories (other DME products, non-Medicare populations, ancillary services like diabetes education or telehealth) using the infrastructure you built.

Exit Scenarios and Expected Returns

The exit timing is probably 2030-2031, which is 4-5 years from initial investment. You want to get through the first contract period (2028-2030) to demonstrate the business model works, show margin expansion from scale, and build a track record for contract renewal. By 2030-2031, you’re a $600-800M revenue business doing $140-200M EBITDA with regulatory moats, recurring revenue, and a clear path to continued growth.

There are three potential exit paths:

Path one is sale to a strategic acquirer. The obvious buyers are device manufacturers (Dexcom, Abbott, Medtronic, Insulet, Tandem) who want vertical integration and direct relationships with patients. Dexcom in particular has been acquiring distribution assets (they bought Sequel Med Tech for this reason), and owning a national DME platform would give them control over the patient experience and data flows. The other strategic buyers are pharmacy benefit managers (CVS, Cigna, Optum) who want to expand into DME as part of broader healthcare services, or retail health platforms (Walgreens, Amazon) who see DME as an adjacency to pharmacy and primary care.

Strategic buyers typically pay 12-15x EBITDA for assets with regulatory moats and recurring revenue. On $150M EBITDA (conservative case), that’s a $1.8-2.25B exit. On $180M EBITDA (base case), that’s $2.16-2.7B. These buyers care about strategic fit (does this asset help them achieve their broader goals?), defensibility (can competitors replicate this?), and growth trajectory (is the business still growing or is it mature?). You have strong answers on all three dimensions.

Path two is sale to another PE fund as a platform for further consolidation. A larger healthcare PE fund or a generalist PE fund looking for healthcare exposure might buy you as a platform to continue rolling up DME suppliers and expanding into adjacent categories. They’d be buying a proven operating model, strong management team, and attractive growth profile. The multiple here is probably 11-13x EBITDA (slightly lower than strategic because there’s less synergy potential and more execution risk). On $150-180M EBITDA, that’s a $1.65-2.34B exit.

Path three is continuation fund or dividend recap, where you refinance the business, return capital to LPs, and hold for another 3-5 years. This makes sense if the contract renewal is looking good and you see a path to $1B+ revenue and $250M+ EBITDA over the next contract period. You could refi at 5-6x leverage on $150-180M EBITDA, which gives you $750M-1B in debt proceeds to return to investors while keeping equity upside. This is less common in healthcare services (more common in infrastructure or software) but could work if the business is truly annuity-like.

The return math on a strategic exit is pretty compelling. You invest $250M total capital ($172M rollup, $78M greenfield build). You exit for $2-2.5B at 12-15x on $150-180M EBITDA. The gross MOIC is 8-10x, but you have management equity (10-20% rollover from acquisitions), deal fees, and ongoing monitoring fees that reduce net MOIC to 6-8x. On a 4-5 year hold period, that’s a 40-50% IRR, which is exactly what top-quartile healthcare PE funds are returning.

The downside scenario is you don’t win enough contracts (maybe only 1-2 categories instead of 3-4), or you win contracts but struggle with execution (poor customer experience, compliance issues, margin pressure). In that case, you’re still building a valuable business (you have the rollup EBITDA and some contract revenue), but the exit multiple compresses to 8-10x and the valuation is maybe $1.2-1.5B. That’s still a 5-6x MOIC and a 30-35% IRR, which is acceptable but not exceptional.

The risk mitigation is all about execution discipline. You need to be rigorous on acquisition due diligence (don’t overpay, don’t buy broken companies, ensure cultural fit). You need to be aggressive on integration (move fast on cost synergies, don’t let acquired companies drift). You need to be thoughtful on the technology and infrastructure build (don’t over-engineer, focus on what’s needed to win bids and serve customers). And you need to be excellent on the bid strategy (price to win but maintain margins, differentiate on capability).

The other risk is regulatory. CMS could change the program structure, alter payment methodologies, or terminate contracts if they perceive access issues. But the final rule is pretty clear and stable, and CMS has strong incentives to make the RID model work (it saves money, improves quality, and reduces beneficiary out-of-pocket costs). The regulatory risk is lower than most Medicare businesses because you’re working within an established program with clear rules.

For a healthcare PE fund with $1-3B in AUM, this is probably the best risk-adjusted opportunity in the market right now. You’re getting immediate EBITDA from the rollup, regulatory tailwinds from the CMS restructuring, margin expansion from scale, and multiple expansion from moat creation. The execution is hard but totally within the capability of a good PE operating team. And the timing is perfect - you have 18 months to build before you need to bid, which is enough time to do it right but not so much time that competitors catch on and multiples inflate.

I’d be making calls to healthcare M&A intermediaries today to start sourcing targets, lining up debt financing with healthcare lenders (firms like Ares, Owl Rock, Blue Torch who understand the model), and recruiting operating partners who know the DME space. The fund that moves fast on this is going to build a generational asset. The funds that wait or miss it are going to be looking at the 2031 contract round and wishing they’d started in 2025.

TABLE OF CONTENTS

Abstract

Introduction: The Activist as Diagnostic Tool

The Valeant Disaster: A Masterclass in What Not to Do

The Herbalife War and Healthcare Adjacent Plays

What Ackman Actually Understands About Healthcare Markets

The Pharma Pricing Thesis and Why It Matters for Startups

Insurance Market Dynamics Through an Activist Lens

The COVID Testing Play and Public Health Infrastructure

What Health Tech Investors Can Learn from Activist Failures

Conclusion: The Limits of Financial Engineering in Healthcare

ABSTRACT

Bill Ackman’s healthcare investments offer a unique lens into both the opportunities and limitations of applying traditional financial activism to healthcare markets. From the catastrophic Valeant partnership to more successful plays in insurance and COVID testing, Ackman’s track record reveals fundamental truths about healthcare market dynamics that matter for early-stage investors. His advocacy for drug pricing reform and insurance market transparency stems directly from painful lessons about how healthcare companies create and destroy value differently than other sectors. This essay examines Ackman’s major healthcare positions, his public statements on reform, and what his experiences teach us about building defensible healthcare businesses. For health tech angels, the key insight is that Ackman’s failures came from treating healthcare like any other industry while his successes came from understanding its unique structural constraints. The businesses that survive activist scrutiny and regulatory pressure are the ones solving real inefficiencies rather than exploiting information asymmetries or regulatory capture.

Introduction: The Activist as Diagnostic Tool

Here’s something most people don’t think about when they’re watching billionaire hedge fund managers fight each other on CNBC: activist investors are essentially doing free market research for the rest of us. When someone like Bill Ackman takes a massive position in a company and then spends months or years publicly articulating everything wrong with that company’s business model, strategy, or market position, they’re building a detailed thesis based on access to information and analytical resources that most early-stage investors can’t match. And when they’re catastrophically wrong about something, that’s even more valuable information.

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• Abstract

• The Dawn of Regulatory Clarity

• Governance as the New Competitive Advantage

• The Privacy Paradox: Transparency in an Opaque World

• Data Security: Building Digital Fortresses

• Quality Monitoring: The Art of Algorithmic Oversight

• Safety Reporting: Creating a Culture of Shared Learning

• Bias and Risk Assessment: The Technical Challenge of Fairness

• Education and Training: Building AI-Literate Healthcare Organizations

• Strategic Implications for Health Tech Entrepreneurs

• The Path Forward: From Compliance to Excellence

Abstract

The Joint Commission and Coalition for Health AI have released groundbreaking guidance on the Responsible Use of AI in Healthcare, marking a pivotal moment for health tech entrepreneurs. This framework introduces seven core elements that will fundamentally reshape how healthcare organizations implement, monitor, and govern AI systems. For entrepreneurs, this guidance represents both an opportunity and a challenge: while it provides much-needed regulatory clarity, it also establishes new baseline expectations that will influence product development, go-to-market strategies, and customer success initiatives. The guidance emphasizes governance structures, patient privacy, data security, ongoing quality monitoring, voluntary safety reporting, bias assessment, and comprehensive education programs. Understanding these requirements is crucial for health tech companies seeking to build sustainable, scalable solutions that healthcare organizations will confidently adopt and regulators will support.

Disclaimer: The thoughts and opinions expressed in this essay are my own and do not reflect the views or positions of my employer.

The Dawn of Regulatory Clarity

The healthcare AI landscape has been operating in a regulatory twilight zone for years, with innovators pushing boundaries while healthcare organizations hesitate at the threshold of adoption, uncertain about compliance requirements and liability exposure. The Joint Commission's collaboration with the Coalition for Health AI to produce comprehensive guidance on the Responsible Use of AI in Healthcare represents the most significant regulatory development in this space since the FDA began approving AI-enabled medical devices. For health tech entrepreneurs, this moment is comparable to the introduction of HIPAA compliance requirements in the late 1990s, when what initially seemed like bureaucratic burden ultimately became a foundation for trust and systematic growth across the industry.

The timing of this guidance is particularly noteworthy. Healthcare organizations surveyed by the Joint Commission expressed clear demand for standardized approaches to AI implementation, while the rapid proliferation of AI tools has created a patchwork of internal policies and procedures that vary dramatically across institutions. The guidance emerges from extensive stakeholder engagement, including meetings with representatives across the healthcare industry, surveys of accredited hospitals and health systems, and review of existing frameworks from organizations like the National Academy of Medicine and NIST. This collaborative approach suggests that the recommendations reflect real-world operational needs rather than theoretical regulatory idealism.

What makes this guidance especially significant for entrepreneurs is its focus on implementation and operation rather than development. Unlike FDA device approval processes that primarily concern product safety and efficacy, the Joint Commission framework addresses how healthcare organizations should responsibly deploy and manage AI tools throughout their lifecycle. This operational focus creates new opportunities for companies that can help healthcare organizations achieve compliance while maximizing the value of their AI investments.

The economic implications are substantial. Healthcare organizations that align with this guidance position themselves favorably for Joint Commission accreditation reviews, while those that ignore it may face increased scrutiny. More importantly, the guidance establishes a common language and set of expectations that will influence procurement decisions, vendor evaluations, and contract negotiations. For entrepreneurs, understanding these requirements early provides a competitive advantage in product development and market positioning.

Governance as the New Competitive Advantage

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This essay examines Joe Kwon's policy paper "AI Agents: Governing Autonomy in the Digital Age" from the Center for AI Policy and analyzes its critical implications for health tech entrepreneurs and investors. The paper proposes a comprehensive regulatory framework for autonomous AI agents that includes an "Autonomy Passport" system, continuous monitoring requirements, human oversight mandates, and workforce impact research. For health tech stakeholders, these proposed regulations present both significant compliance challenges and strategic opportunities. While the regulatory framework may increase development costs and time-to-market, it also creates competitive moats for well-capitalized companies and standardizes safety practices across the industry. Health tech entrepreneurs must prepare for substantial regulatory compliance investments, while investors need to adjust their due diligence processes and portfolio strategies to account for the emerging regulatory landscape. The paper's emphasis on human oversight in critical domains aligns closely with existing healthcare regulations but may limit the efficiency gains that make AI agents attractive to healthcare organizations.

Table of Contents

1. Introduction and Context Setting

2. Key Findings from Kwon's Policy Paper

3. Current State of AI Agents in Healthcare

4. Regulatory Impact Analysis for Health Tech Companies

5. Strategic Implications for Entrepreneurs

6. Investment Considerations and Portfolio Strategy

7. Compliance Framework Development

8. Competitive Landscape Transformation

9. Future Scenarios and Risk Assessment

10. Recommendations and Action Items

Governing Autonomous AI Agents: Critical Implications for Health Tech Entrepreneurs and Investors

In May 2025, Joe Kwon of the Center for AI Policy published a comprehensive policy paper titled "AI Agents: Governing Autonomy in the Digital Age" that proposes the most detailed regulatory framework yet conceived for autonomous AI systems. For health tech entrepreneurs and investors, this 23-page document represents a potential inflection point that could fundamentally alter the development, deployment, and commercialization of AI-powered healthcare solutions. The paper's recommendations, if adopted by Congress, would establish mandatory pre-deployment licensing, continuous monitoring requirements, human oversight mandates, and comprehensive workforce impact assessments for AI agents operating above basic assistant levels.

The timing of this policy proposal coincides with explosive growth in healthcare AI agent adoption. Industry data from 2025 shows that AI agents are already managing 50-60% of front-office administrative tasks in many healthcare practices, reducing revenue cycle management costs by up to 70%, and automating complex workflows from appointment scheduling to clinical documentation. Major healthcare technology companies like ServiceNow report USD 325 million in annualized value from autonomous agent deployments, while organizations like Mayo Clinic are pioneering "agentic automation architectures" that fundamentally reimagine clinical and operational workflows. This rapid adoption trajectory makes Kwon's regulatory proposals particularly consequential for stakeholders who must navigate the intersection of technological capability and regulatory compliance.

The paper's central thesis argues that AI agents pose three distinct categories of risk: catastrophic misuse through cyberattacks or dual-use applications, gradual human disempowerment as decision-making migrates to opaque algorithms, and unprecedented workforce displacement affecting up to 300 million jobs globally according to Goldman Sachs projections. Healthcare, with its complex regulatory environment, high-stakes decision making, and substantial economic footprint, represents a critical testbed for these regulatory approaches. The proposed framework would require health tech companies to fundamentally rethink their product development cycles, compliance strategies, and go-to-market approaches while potentially creating new competitive advantages for organizations capable of navigating increased regulatory complexity.

Understanding the paper's key findings requires examining the sophisticated five-level autonomy classification system that Kwon proposes as the foundation for risk-proportional regulation. Level 1 "shift-length assistants" can work autonomously for roughly eight hours with human oversight, while Level 5 "frontier super-capable systems" operate indefinitely across any domain without human guidance. Most current healthcare AI applications fall into Levels 1-2, but the rapid pace of capability advancement suggests that Level 3-4 systems capable of multi-day autonomous operation may emerge within the next few years. This progression timeline has direct implications for health tech companies, as the regulatory requirements become substantially more stringent at Level 2 and above, requiring formal registration, safety audits, and continuous monitoring.

The paper's "Autonomy Passport" proposal represents the most significant regulatory innovation, establishing a mandatory federal registration system for AI agents operating at Level 2 or higher. This system would require companies to file detailed dossiers documenting their agents' mission envelopes, tool access permissions, autonomy classifications, security validation results, and emergency contact information before deployment. The US AI Safety Institute would set technical standards and maintain a public registry of approved agents, while accredited private firms would conduct the required safety audits. For health tech entrepreneurs, this represents a fundamental shift from the current largely self-regulated environment to a formal pre-market approval process similar to FDA device clearance but potentially more complex and time-consuming.

The continuous monitoring and enforcement mechanisms proposed in the paper would create ongoing compliance obligations throughout an AI agent's operational lifecycle. High-capability agents would need to operate within digital sandboxes that enforce pre-approved action lists, attach tamper-evident signatures to every outbound action, and remain subject to emergency recall authority by the Cybersecurity and Infrastructure Security Agency. These requirements mirror some aspects of existing healthcare IT security frameworks but extend far beyond current practice in terms of real-time monitoring and federal oversight authority. Healthcare organizations that have invested heavily in AI agent deployments would need to retrofit their systems to comply with these new monitoring requirements, potentially creating significant technical and financial challenges.

The human oversight mandate for critical systems represents perhaps the most directly relevant aspect of Kwon's framework for healthcare applications. The paper proposes that when AI agents make recommendations in domains that normally require professional licensing or regulatory approval, qualified humans must review and approve those recommendations before execution. In healthcare contexts, this would mean that licensed healthcare professionals must approve AI agent recommendations for prescription changes, treatment modifications, diagnostic interpretations, and other clinical decisions. While this aligns with existing medical practice standards and liability frameworks, it may limit the efficiency gains that make AI agents attractive to healthcare organizations facing staffing shortages and cost pressures.

The workforce impact research mandate represents the final pillar of Kwon's regulatory framework, directing federal agencies to produce annual reports tracking job displacement and wage trends related to AI agent adoption. For healthcare, this is particularly relevant given projections that up to 40% of jobs in advanced economies show high exposure to AI-driven automation, with college-educated roles facing even higher displacement rates. Healthcare organizations are already reporting significant productivity gains from AI agent deployments, with some companies achieving 15% developer productivity improvements and double-digit reductions in call handling times. The proposed research mandate would create systematic tracking of these workforce impacts, potentially informing future regulatory interventions or support programs.

Current healthcare AI agent deployments demonstrate both the transformative potential and regulatory complexity that Kwon's framework aims to address. Revenue cycle management represents one of the most mature application areas, with companies like CodaMetrix using natural language processing to automate medical coding across 200 hospitals and 50,000 providers. These systems continuously learn from clinical data while incorporating payer rules and compliance requirements, achieving accuracy levels that reduce manual intervention while maintaining regulatory compliance. Under Kwon's proposed framework, these systems would likely require Level 2 classification and full Autonomy Passport registration, given their ability to make consequential financial decisions autonomously.

Clinical documentation represents another rapidly evolving application area where AI agents are fundamentally changing healthcare workflows. Companies like Augmedix deploy ambient documentation tools that capture natural clinician-patient conversations and convert them into structured medical notes, serving nearly half a million clinicians. These systems operate continuously during patient encounters, making real-time decisions about what information to capture and how to structure clinical documentation. The proposed regulatory framework would likely require human oversight for clinical documentation agents that generate billable diagnosis codes or treatment recommendations, potentially limiting their autonomous capabilities while ensuring clinical accountability.

Diagnostic and imaging applications showcase the potential for AI agents to operate at higher autonomy levels while maintaining safety and accuracy. Companies like Qure.ai have deployed AI systems across 4,500 sites in over 100 countries to automate interpretation of X-rays, CT scans, and ultrasounds, particularly in areas with limited radiology expertise. These systems can identify and prioritize conditions like tuberculosis, lung cancer, and stroke, often detecting anomalies that human radiologists might miss. However, under Kwon's framework, such systems would require human radiologist approval before final diagnostic decisions, maintaining the current standard of care while potentially limiting efficiency gains.

The regulatory impact analysis for health tech companies reveals several critical considerations that will shape strategic planning and investment decisions. Compliance costs represent the most immediate concern, as companies would need to invest in safety auditing, continuous monitoring systems, human oversight integration, and emergency response capabilities. These costs are not one-time expenses but ongoing operational requirements that scale with system complexity and deployment breadth. For early-stage health tech startups, these compliance requirements could represent existential challenges, as the cost of Autonomy Passport registration and continuous monitoring may exceed available funding runway.

Time-to-market implications are equally significant, as the proposed pre-deployment review process could add months or years to product development cycles. Healthcare AI companies currently benefit from relatively rapid iteration and deployment capabilities, particularly for administrative and operational applications that don't require formal FDA approval. The Autonomy Passport system would introduce a formal gate that must be cleared before any Level 2 or higher AI agent can be deployed, potentially slowing innovation cycles and reducing the first-mover advantages that characterize the current competitive landscape.

Market access considerations add another layer of complexity, as the proposed framework would require major cloud providers and app stores to block any AI agent that doesn't appear on the federal green list. This creates a binary approval system where companies either achieve full market access through successful registration or face complete market exclusion. For health tech companies that rely on cloud-based deployment models, this represents a fundamental shift in go-to-market strategy and risk management. Companies would need to build stronger relationships with accredited auditing firms and develop more robust quality assurance processes to ensure successful registration.

The competitive landscape implications of Kwon's regulatory framework are particularly nuanced for health tech companies. Large, well-capitalized organizations with existing regulatory compliance capabilities may benefit from increased barriers to entry that limit competition from smaller, more agile startups. Companies like Epic, Cerner, and other established healthcare IT vendors have extensive experience navigating complex regulatory environments and may be better positioned to absorb the costs and complexity of Autonomy Passport compliance. This could accelerate market consolidation and reduce the likelihood of disruptive innovation from startup companies.

However, the framework also creates opportunities for companies that can develop specialized compliance capabilities or serve as regulatory technology providers. The requirement for accredited private auditing firms creates an entirely new service category that doesn't currently exist in the AI industry. Health tech companies with strong regulatory expertise could potentially pivot to serve this emerging market, while specialized consulting firms could emerge to help smaller companies navigate the compliance requirements. The standardization aspects of the framework could also benefit companies by creating clearer requirements and reducing regulatory uncertainty.

Strategic implications for health tech entrepreneurs require fundamental rethinking of business model assumptions and development priorities. Product development strategies must now account for regulatory compliance from the earliest stages, rather than treating compliance as a post-development consideration. This means integrating safety-by-design principles, building comprehensive audit trails, and designing human oversight capabilities as core system features rather than add-on components. Entrepreneurs must also consider whether their target applications justify the increased development and compliance costs, potentially shifting focus toward higher-value use cases that can support the additional regulatory burden.

Funding strategy considerations become more complex under the proposed regulatory framework, as investors will need to factor compliance costs and regulatory risks into their investment decisions. Early-stage companies may need larger seed and Series A rounds to fund regulatory compliance activities, while later-stage companies may face longer development cycles that delay revenue generation and extend time-to-exit scenarios. Entrepreneurs must be prepared to articulate clear regulatory strategies to potential investors and demonstrate deep understanding of the compliance requirements that affect their specific applications.

Partnership and acquisition strategies may become more attractive under increased regulatory complexity, as smaller companies may find it more efficient to partner with or be acquired by larger organizations with established regulatory capabilities. This could lead to earlier exit opportunities for entrepreneurs but potentially at lower valuations if regulatory uncertainty reduces buyer enthusiasm. Strategic partnerships with established healthcare IT companies or device manufacturers may become essential for smaller companies that lack the resources to navigate complex regulatory requirements independently.

Investment considerations for health tech venture capital and private equity firms must evolve to address the new regulatory landscape that Kwon's framework would create. Due diligence processes must now include detailed assessment of regulatory compliance strategies, evaluation of management team regulatory expertise, and analysis of competitive positioning in a more heavily regulated market environment. Investors need to develop capabilities to evaluate the technical feasibility and cost implications of Autonomy Passport compliance, human oversight integration requirements, and continuous monitoring system development.

Portfolio construction strategies may shift toward companies with stronger regulatory moats and away from pure-play technology companies that lack healthcare domain expertise. The increased barriers to entry created by regulatory compliance requirements may make incumbent healthcare IT companies more attractive investment targets, while making early-stage AI startups riskier investments unless they demonstrate exceptional regulatory preparation. Investors may also need to increase reserve requirements for follow-on funding to support compliance activities that were not previously necessary.

Risk assessment frameworks must incorporate regulatory change risk as a primary factor in investment decisions. The Kwon framework represents just one possible regulatory outcome, and investors must consider scenarios ranging from no additional regulation to even more restrictive requirements. Companies with greater regulatory flexibility and stronger compliance capabilities will be better positioned to adapt to various regulatory scenarios, making them more attractive investment targets. Geographic diversification considerations also become relevant, as regulatory requirements may vary significantly across different jurisdictions and create opportunities for regulatory arbitrage.

Compliance framework development represents a critical operational priority for health tech companies preparing for potential regulatory changes. Legal and regulatory affairs capabilities must be substantially enhanced, with companies needing either in-house expertise or reliable external counsel with deep understanding of AI regulation, healthcare compliance, and emerging technology governance. The interdisciplinary nature of the proposed requirements means that companies need legal expertise spanning technology law, healthcare regulation, employment law, and federal administrative procedure.

Technical infrastructure development for compliance requires significant engineering investment in monitoring systems, audit trail capabilities, human oversight integration, and emergency response mechanisms. These systems must be designed for regulatory compliance from the ground up, rather than retrofitted onto existing architectures. Companies must also develop capabilities to work with third-party auditing firms and maintain detailed documentation of system capabilities, safety testing results, and operational procedures. The technical complexity of implementing tamper-evident signatures, sandbox containment, and real-time monitoring represents substantial development effort that must be factored into product roadmaps.

Quality assurance and risk management processes must be elevated to meet the standards expected by federal regulators and third-party auditors. This includes implementing formal software development lifecycle processes, conducting comprehensive security testing, maintaining detailed change control documentation, and developing incident response procedures. Companies must also establish governance structures that can demonstrate appropriate oversight of AI system development and deployment decisions, potentially requiring board-level committees with relevant expertise.

The competitive landscape transformation that would result from Kwon's regulatory framework creates both risks and opportunities across different market segments. Enterprise healthcare IT companies may benefit from increased barriers to entry and the opportunity to leverage existing regulatory relationships and compliance capabilities. However, they also face substantial costs to retrofit existing AI systems for compliance and may need to slow innovation cycles to accommodate regulatory review processes. The requirement for human oversight in critical applications may limit the automation benefits that justify AI investments, potentially reducing market demand for certain categories of AI solutions.

Startup and emerging company impacts are more complex and depend heavily on specific business models and target applications. Companies focused on non-critical administrative applications may benefit from lower regulatory barriers, while those targeting clinical decision support or autonomous diagnostic applications face more stringent requirements. The requirement for pre-deployment registration could eliminate the rapid iteration and continuous deployment models that many AI startups rely on for competitive advantage. However, successful navigation of the regulatory framework could also create significant competitive moats for companies that achieve compliance.

International competitiveness considerations become relevant as US companies may face compliance costs and development delays that don't affect international competitors. However, the proposed framework's alignment with emerging international standards could also position US companies advantageously in global markets that adopt similar regulatory approaches. The paper specifically mentions coordination opportunities with the UK AI Safety Institute, the G7 Hiroshima Process, and emerging OECD frameworks, suggesting that regulatory harmonization may reduce the competitive disadvantage of early compliance.

Future scenario analysis reveals several possible trajectories that health tech stakeholders must consider in strategic planning. The base case scenario assumes partial adoption of Kwon's recommendations, with Congress implementing some form of AI agent registration and oversight but potentially with modified requirements or longer implementation timelines. This would create a transitional period during which companies can adapt their systems and processes while maintaining competitive dynamics similar to current conditions.

The accelerated regulation scenario envisions rapid adoption of the full framework, potentially in response to a significant AI-related incident that creates political pressure for immediate regulatory action. This scenario would create severe challenges for companies that haven't prepared for compliance requirements, potentially triggering market consolidation and significant competitive reshuffling. Companies with advanced regulatory preparation would gain substantial competitive advantages, while unprepared companies could face existential challenges.

The expanded regulation scenario considers the possibility that initial AI agent regulations could be followed by additional requirements covering broader aspects of healthcare AI deployment. This could include integration with existing FDA device regulation, expansion of human oversight requirements, or additional reporting obligations related to clinical outcomes and patient safety. Companies must consider how initial regulatory compliance capabilities could be expanded to address future requirements and whether their regulatory strategies are sufficiently flexible to adapt to evolving requirements.

Risk assessment and mitigation strategies must address both direct regulatory compliance risks and indirect competitive and market risks. Regulatory non-compliance risks include the possibility of market exclusion, civil penalties, criminal liability for executives, and reputational damage that affects customer relationships and investor confidence. Companies must develop comprehensive compliance monitoring systems and legal risk assessment capabilities to identify and address potential violations before they result in enforcement actions.

Market and competitive risks include the possibility that regulatory requirements could reduce market demand for AI solutions, increase customer acquisition costs, or create competitive advantages for non-AI alternatives. Companies must maintain flexibility in their product development strategies and consider pivot options that could reduce regulatory exposure while maintaining market position. Financial risks include the possibility that compliance costs could exceed revenue generation capabilities, particularly for early-stage companies with limited resources.

Technology and operational risks focus on the possibility that required compliance systems could interfere with product functionality, reduce system performance, or create new security vulnerabilities. The integration of human oversight requirements with autonomous AI systems presents particular technical challenges that must be carefully managed to maintain both regulatory compliance and operational effectiveness. Companies must also consider the risks associated with third-party dependencies, including auditing firms, cloud providers, and monitoring system vendors.

Recommendations and action items for health tech entrepreneurs center on proactive preparation for potential regulatory requirements while maintaining operational flexibility. Immediate actions should include comprehensive assessment of current AI system autonomy levels using the five-level framework proposed by Kwon, evaluation of which systems would require registration and oversight under the proposed rules, and development of preliminary compliance strategies for high-risk applications. Entrepreneurs should also begin building relationships with potential auditing partners and regulatory advisors while monitoring Congressional activity related to AI regulation.

Medium-term strategic actions should focus on integration of compliance capabilities into product development processes, including implementation of audit trail systems, human oversight interfaces, and monitoring capabilities that could satisfy regulatory requirements. Companies should also consider geographic expansion strategies that could provide regulatory diversification and develop contingency plans for different regulatory scenarios. Investment in regulatory expertise, either through hiring or external partnerships, becomes essential for companies targeting applications that would face significant oversight requirements.

Long-term strategic positioning requires consideration of how regulatory compliance capabilities could become competitive advantages and revenue generators. Companies that develop strong compliance capabilities may be able to serve as regulatory technology providers for other organizations or provide consulting services to help smaller companies navigate complex requirements. The standardization aspects of the proposed framework could also create opportunities for companies that develop best-practice approaches that can be scaled across the industry.

Investment recommendations for venture capital and private equity firms emphasize the need for enhanced due diligence capabilities and risk assessment frameworks that account for regulatory change scenarios. Investors should develop expertise in AI regulation and healthcare compliance or partner with specialized advisors who can provide detailed assessment of regulatory risks and opportunities. Portfolio companies should be encouraged to begin compliance preparation activities immediately, even in the absence of final regulatory requirements, to maintain strategic flexibility and competitive positioning.

The health tech industry stands at a critical juncture where technological capability advancement intersects with emerging regulatory frameworks that could fundamentally reshape competitive dynamics and business model viability. Joe Kwon's comprehensive policy framework represents the most detailed roadmap yet proposed for AI agent governance, with implications that extend far beyond simple compliance requirements. For entrepreneurs and investors in the health tech space, success in this evolving landscape will require not just technological innovation but also regulatory sophistication, strategic flexibility, and deep understanding of the complex interplay between automation capabilities and human oversight requirements.

The companies that thrive in this new regulatory environment will be those that view compliance not as a burden but as a source of competitive advantage, building regulatory capabilities that enable them to deploy AI agents safely and effectively while creating barriers to entry for less prepared competitors. The investment firms that succeed will be those that can accurately assess regulatory risks and opportunities, support portfolio companies through complex compliance requirements, and identify emerging opportunities created by the new regulatory landscape. As the health tech industry continues its rapid evolution toward AI-powered automation, the intersection of innovation and regulation will increasingly determine which companies and investors achieve sustainable success in this transformative market.

Abstract

The 2026 CMS Home Health Prospective Payment System proposed rule represents a watershed moment in American healthcare delivery, introducing fundamental changes that will reshape business models, incumbent strategies, and stakeholder relationships across the home health ecosystem. With aggregate Medicare payments to home health agencies decreasing by 6.4% ($1.135 billion) and the implementation of retrospective overpayment recoupment mechanisms, the rule creates both unprecedented challenges and transformative opportunities for health technology entrepreneurs, established providers, and payers.

This analysis examines the emergence of new business models driven by regulatory constraints, technological advancement, and evolving patient expectations. Traditional home health agencies face existential pressures from payment reductions, enhanced fraud prevention measures, and increased quality reporting requirements, while innovative technology companies position themselves to capture value through efficiency solutions, care coordination platforms, and outcome-based services. The rule's emphasis on patient-driven groupings, value-based purchasing models, and digital quality measurements creates fertile ground for entrepreneurial ventures focused on data analytics, artificial intelligence, and integrated care delivery platforms.

The analysis reveals that successful market participants will need to navigate a complex landscape of regulatory compliance, technological innovation, and stakeholder alignment. New entrants have opportunities to disrupt traditional care models through technology-enabled solutions, while incumbents must rapidly adapt their operational frameworks to survive in an increasingly constrained payment environment. The implications extend beyond home health to encompass broader healthcare delivery transformation, suggesting that the 2026 rule may serve as a catalyst for industry-wide innovation and consolidation.

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Table of Contents

1. Introduction: The Regulatory Catalyst

2. Payment Model Transformation and Financial Pressure

3. Emerging Business Models in the New Paradigm

4. Impact on Incumbent Home Health Agencies

5. Payer Strategy Evolution and Market Dynamics

6. Technology Disruption and Innovation Opportunities

7. Strategic Implications for Health Tech Entrepreneurs

8. Future Market Structure and Competitive Landscape

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Introduction: The Regulatory Catalyst

The healthcare industry stands at a critical inflection point as the Centers for Medicare & Medicaid Services prepares to implement sweeping changes to the Home Health Prospective Payment System for calendar year 2026. The proposed rule introduces a permanent prospective adjustment of -4.059% to account for the impact of implementing the Patient-Driven Groupings Model (PDGM), alongside a -5.0% temporary adjustment to recoup retrospective overpayments. These changes represent more than mere payment adjustments; they constitute a fundamental restructuring of how home health services are valued, delivered, and measured in the American healthcare system.

The magnitude of these changes cannot be overstated. CMS estimates that Medicare payments to home health agencies in CY 2026 would decrease in the aggregate by 6.4%, or $1.135 billion, compared to CY 2025. This reduction occurs against a backdrop of increasing healthcare costs, rising patient acuity, and growing demand for home-based care services driven by demographic shifts and consumer preferences. The resulting pressure creates a compelling case for innovation and business model transformation across the entire home health ecosystem.

For health technology entrepreneurs, this regulatory environment presents both unprecedented challenges and extraordinary opportunities. The traditional fee-for-service model that has sustained home health agencies for decades is being systematically dismantled in favor of value-based care arrangements, outcome-driven payments, and technology-enabled efficiency measures. Companies that can successfully navigate this transition while providing genuine value to patients, providers, and payers will find themselves positioned at the forefront of a transforming industry.

The proposed rule's emphasis on fraud prevention, quality measurement, and care coordination creates natural market opportunities for technology solutions. The rule proposes several new provider enrollment provisions intended to prevent fraud, waste, and abuse, including retroactive revocations and expanded bases for revocation or deactivation. These regulatory requirements create demand for compliance technology, monitoring systems, and integrated platforms that can help providers navigate an increasingly complex regulatory landscape while maintaining operational efficiency.

Understanding the implications of these changes requires examining not just the immediate financial impacts but also the broader strategic shifts they represent. The home health industry is moving from a volume-based model focused on service delivery to a value-based model emphasizing outcomes, efficiency, and patient satisfaction. This transition mirrors broader healthcare industry trends but is accelerated by the specific regulatory pressures facing home health providers.

Payment Model Transformation and Financial Pressure

The financial architecture of home health care is undergoing a fundamental transformation that will reshape business models across the industry. The Patient-Driven Groupings Model, implemented in 2020, was designed to better align payments with patient care needs, but its implementation has revealed significant gaps between projected and actual behavior changes. CMS has applied permanent adjustments to the 30-day payment rate in CYs 2023-2025, with adjustments of -3.925%, -2.890%, and -1.975% respectively, representing only half of the permanent adjustments calculated at the time.

The 2026 proposed rule represents an acceleration of this trend, with CMS implementing both permanent and temporary adjustments that collectively create substantial financial pressure on home health agencies. The permanent adjustment of -4.059% reflects ongoing recalibration of the payment model, while the temporary adjustment of -5.0% addresses retrospective overpayments that have accumulated since the PDGM implementation. The calculated temporary adjustment amount for CYs 2020 through 2024 is approximately $5.3 billion, with the proposed 5.0% reduction collecting approximately $786 million, or about 14.8% of the total.

These payment reductions create a cascade of strategic challenges for existing home health agencies. Traditional operational models built around maximizing visit volume and service intensity are no longer financially viable under the new payment structure. Agencies must fundamentally reconsider their approach to patient care, resource allocation, and service delivery to maintain financial sustainability. The shift requires not just operational efficiency improvements but complete business model transformation focused on value creation rather than volume maximization.

The financial pressure extends beyond immediate payment reductions to encompass the broader cost structure of home health operations. Agencies must invest in technology systems, quality measurement capabilities, and compliance infrastructure while simultaneously reducing their revenue base. This creates a particularly challenging environment for smaller, independent agencies that lack the capital resources to make necessary investments in operational transformation.

For health technology entrepreneurs, this financial pressure creates substantial market opportunities. Agencies desperate to maintain margins while complying with new requirements represent a ready market for efficiency solutions, automation platforms, and integrated care management systems. The key is developing solutions that deliver immediate, measurable value while positioning agencies for long-term success under the evolving payment model.

The recalibration of case-mix weights and low utilization payment adjustment thresholds further complicates the financial landscape. CMS is proposing to recalibrate the case-mix weights including updating the functional impairment levels, comorbidity adjustment subgroups, and LUPA thresholds using CY 2024 data to more accurately pay for the types of patients home health agencies are serving. This ongoing adjustment process means that agencies cannot simply adapt to current payment levels but must build flexibility into their business models to accommodate continuous regulatory evolution.

The financial transformation also affects the competitive dynamics within the home health industry. Larger, well-capitalized agencies with sophisticated technology infrastructures are better positioned to absorb payment reductions while investing in operational improvements. This creates natural consolidation pressure as smaller agencies struggle to maintain viability under the new payment model. The resulting market concentration may create opportunities for technology companies to serve as the infrastructure backbone for consolidated service delivery networks.

Emerging Business Models in the New Paradigm

The regulatory and financial pressures created by the 2026 proposed rule are catalyzing the emergence of entirely new business models in the home health sector. These models are characterized by their emphasis on technology integration, outcome-based care delivery, and multi-stakeholder value creation. The traditional linear model of home health service delivery—where agencies provide services directly to patients under physician orders—is giving way to more complex, networked approaches that leverage technology platforms to coordinate care across multiple providers and settings.

Platform-based business models represent one of the most significant emerging trends. These platforms serve as intermediaries between patients, providers, and payers, using technology to optimize care coordination, resource allocation, and outcome measurement. The platform model addresses several key challenges created by the new payment structure: the need for efficient care coordination, the requirement for comprehensive quality measurement, and the pressure to demonstrate value-based outcomes. Companies building these platforms can capture value through transaction fees, subscription models, or outcome-based payments tied to improved patient outcomes or reduced total cost of care.

The shift toward value-based care arrangements is creating opportunities for risk-sharing business models that were previously uncommon in home health. These models involve technology companies or care management organizations taking on financial risk for patient outcomes in exchange for upside potential when care is delivered efficiently and effectively. The approach requires sophisticated data analytics capabilities, predictive modeling, and integrated care management systems but offers the potential for substantial returns when executed successfully.

Specialized technology services represent another emerging category of business models. The increased emphasis on fraud prevention, quality measurement, and regulatory compliance creates market demand for specialized solutions that help agencies navigate complex requirements while maintaining operational efficiency. The rule proposes enhanced provider enrollment provisions, including retroactive revocations and expanded bases for revocation or deactivation. These requirements create opportunities for companies offering compliance monitoring, audit support, and risk management services specifically tailored to the home health environment.

The integration of digital health technologies is enabling new hybrid care models that combine traditional home health services with remote monitoring, telemedicine, and artificial intelligence-powered care management. These models can deliver improved patient outcomes while reducing the cost and complexity of traditional in-person care delivery. The regulatory environment supports these innovations through expanded face-to-face encounter policies and increased emphasis on digital quality measurements.

Care pathway optimization represents another emerging business model category. Companies focusing on specific disease conditions or patient populations can develop comprehensive care management solutions that integrate home health services with other healthcare delivery modalities. The PDGM's emphasis on patient-driven groupings creates natural market segments for specialized care management approaches tailored to specific clinical conditions or patient characteristics.

The emergence of these new business models is facilitated by the regulatory environment's increased emphasis on transparency, quality measurement, and outcome reporting. The rule proposes implementing a revised Home Health Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) survey beginning with the April 2026 sample month. This focus on patient experience measurement creates opportunities for companies developing patient engagement platforms, experience optimization tools, and outcome tracking systems.

Data monetization models are also emerging as agencies and technology companies recognize the value of the comprehensive patient data generated through home health services. These models involve aggregating and analyzing patient data to generate insights that can be sold to pharmaceutical companies, medical device manufacturers, or other healthcare stakeholders. The approach requires careful attention to privacy and regulatory compliance but offers the potential for significant additional revenue streams.

Impact on Incumbent Home Health Agencies

The 2026 proposed rule creates existential challenges for incumbent home health agencies, forcing them to fundamentally reconsider their operational strategies, financial models, and competitive positioning. Traditional agencies built around volume-based service delivery face immediate financial pressure that cannot be addressed through incremental operational improvements alone. The magnitude of the payment reductions, combined with increased regulatory requirements, demands comprehensive business model transformation that many agencies are ill-equipped to execute.

Smaller, independent agencies face particularly acute challenges under the new regulatory environment. These agencies typically lack the capital resources, technology infrastructure, and operational sophistication necessary to successfully navigate the transition to value-based care models. The combination of reduced payments and increased compliance requirements creates a scissors effect that threatens the viability of many smaller providers. This dynamic is likely to accelerate industry consolidation as smaller agencies either merge with larger organizations or exit the market entirely.

The operational transformation required by the new payment model extends beyond simple cost reduction to encompass fundamental changes in care delivery approaches. Agencies must invest in technology systems that support care coordination, outcome measurement, and regulatory compliance while simultaneously reducing their operational costs. This requires significant upfront capital investment at precisely the time when agencies are facing revenue reductions, creating a challenging financial dynamic that many agencies cannot sustain.

Quality measurement and reporting requirements create additional operational burdens for incumbent agencies. The rule proposes updates to the Home Health Quality Reporting Program, including removal of certain measures and addition of new assessment items. These changes require agencies to retrain staff, update documentation systems, and implement new quality monitoring processes. The cumulative effect of these requirements is to increase the operational complexity of home health service delivery while reducing the financial resources available to support that complexity.

The enhanced fraud prevention measures proposed in the rule create additional compliance burdens that disproportionately affect smaller agencies. The rule proposes several new provider enrollment provisions intended to prevent fraud, waste, and abuse, including retroactive revocations and expanded bases for revocation or deactivation. These requirements demand sophisticated compliance monitoring systems and legal expertise that many smaller agencies cannot afford to maintain internally.

Larger, well-capitalized agencies have greater ability to adapt to the new regulatory environment but still face significant strategic challenges. These agencies must balance the need for operational efficiency with the requirement to maintain service quality and patient satisfaction. The shift to value-based care models requires these agencies to develop new capabilities in data analytics, care coordination, and outcome measurement that were not previously central to their operations.

The competitive dynamics within the home health industry are also being reshaped by the new payment model. Agencies that successfully implement technology-enabled efficiency improvements and value-based care models will gain competitive advantages that allow them to capture market share from less sophisticated competitors. This creates pressure for continuous innovation and operational improvement that many agencies struggle to maintain.

For incumbent agencies, the path forward requires strategic partnerships with technology companies, significant investment in operational transformation, and fundamental changes in organizational culture and capabilities. Agencies that can successfully navigate this transition will emerge stronger and more competitive, while those that fail to adapt face declining market position and potential business failure.

Payer Strategy Evolution and Market Dynamics

The transformation of the home health payment landscape is fundamentally altering payer strategies and market dynamics across the healthcare ecosystem. Medicare's implementation of the Patient-Driven Groupings Model and associated payment adjustments represents more than a simple reimbursement change; it signals a broader shift toward value-based care arrangements that will influence how all payers approach home health services. This evolution creates both challenges and opportunities for payers seeking to optimize their total cost of care while maintaining or improving patient outcomes.

Medicare's leadership in implementing these changes creates a template that commercial payers and Medicare Advantage plans are likely to follow. The emphasis on patient-driven groupings, outcome-based payments, and quality measurement establishes a framework that other payers can adapt to their specific populations and risk profiles. This standardization effect reduces the complexity for providers who must navigate multiple payer requirements while creating opportunities for technology companies to develop solutions that work across multiple payer types.

The financial pressure created by the payment reductions is forcing payers to reconsider their approach to home health services within the broader context of total cost of care management. Rather than viewing home health as a standalone service category, payers are increasingly integrating it into comprehensive care management strategies that span multiple settings and provider types. This integration creates opportunities for technology companies that can facilitate care coordination across different healthcare delivery modalities.

Medicare Advantage plans face particularly complex strategic considerations under the new payment model. These plans must balance the need to control home health costs with the requirement to maintain member satisfaction and health outcomes. The reduction in Medicare payments for home health services may create opportunities for Medicare Advantage plans to offer enhanced home health benefits as a competitive differentiator, potentially creating new market segments for innovative service delivery models.

The emphasis on quality measurement and patient satisfaction creates alignment between payer objectives and regulatory requirements. The rule proposes implementing a revised Home Health Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) survey beginning with the April 2026 sample month. This focus on patient experience measurement supports payer efforts to improve member satisfaction while ensuring that cost reductions do not compromise service quality.

Commercial payers are likely to accelerate their adoption of value-based care models for home health services in response to Medicare's leadership. The regulatory framework created by the CMS rule provides a proven structure for implementing outcome-based payments and quality measurements that commercial payers can adapt to their specific needs. This creates opportunities for technology companies that can support value-based care arrangements across multiple payer types.

The integration of home health services into broader population health management strategies is creating new market opportunities for companies that can demonstrate their ability to improve outcomes while reducing total cost of care. Payers are increasingly interested in solutions that can prevent unnecessary hospitalizations, reduce readmissions, and improve medication adherence through effective home health interventions.

Risk adjustment and predictive analytics are becoming critical capabilities for payers operating in the new home health environment. The Patient-Driven Groupings Model's emphasis on patient characteristics and care needs requires sophisticated data analytics capabilities to accurately predict costs and outcomes. This creates opportunities for companies developing predictive modeling solutions, risk stratification tools, and population health management platforms.

The regulatory changes also create opportunities for new payer-provider partnership models that share risk and reward for home health outcomes. These arrangements can provide payers with greater cost predictability while offering providers the opportunity to capture additional value through improved efficiency and outcomes. The success of these models depends on sophisticated data sharing and analytics capabilities that create additional market opportunities for technology companies.

Technology Disruption and Innovation Opportunities

The regulatory transformation of the home health industry is creating unprecedented opportunities for technology disruption and innovation. The combination of payment pressure, quality measurement requirements, and fraud prevention measures creates a perfect storm of market demand for technological solutions that can address multiple challenges simultaneously. Companies that can successfully develop and deploy comprehensive technology platforms stand to capture significant value while fundamentally reshaping how home health services are delivered and managed.

Artificial intelligence and machine learning represent perhaps the most significant technological opportunity in the evolving home health landscape. The vast amounts of patient data generated through home health services, combined with the need for predictive analytics and outcome optimization, create natural applications for AI-powered solutions. These technologies can support care pathway optimization, risk stratification, fraud detection, and quality measurement while reducing the administrative burden on clinical staff.

The Patient-Driven Groupings Model's emphasis on patient characteristics and care needs creates opportunities for AI-powered care management platforms that can automatically optimize care plans based on individual patient profiles. These systems can analyze historical data, clinical guidelines, and patient preferences to recommend optimal care interventions while predicting likely outcomes and resource requirements. The ability to demonstrate improved outcomes through AI-enabled care management creates compelling value propositions for both providers and payers.

Remote monitoring and digital health technologies are becoming increasingly important components of home health service delivery. The regulatory environment supports these innovations through expanded face-to-face encounter policies and increased emphasis on digital quality measurements. The rule proposes changes to the face-to-face encounter policy to broaden the language to align with the CARES Act regarding which practitioners can perform the face-to-face encounter. This regulatory flexibility enables technology companies to develop innovative remote monitoring solutions that can reduce the need for in-person visits while maintaining or improving care quality.

Interoperability and data integration represent critical technological challenges that create significant market opportunities. The fragmented nature of healthcare data systems makes it difficult for home health agencies to access comprehensive patient information necessary for optimal care delivery. Companies that can develop effective data integration platforms, FHIR-compliant APIs, and interoperability solutions will provide substantial value to providers struggling to coordinate care across multiple systems and settings.

The emphasis on quality measurement and regulatory compliance creates opportunities for comprehensive compliance management platforms that can automate reporting requirements, monitor quality metrics, and identify potential compliance issues before they become problems. The rule proposes updates to the Home Health Quality Reporting Program, including removal of certain measures and addition of new assessment items. These changing requirements create ongoing demand for flexible, adaptable compliance management solutions.

Fraud prevention and detection technologies represent another significant opportunity area. The rule proposes several new provider enrollment provisions intended to prevent fraud, waste, and abuse, including retroactive revocations and expanded bases for revocation or deactivation. These requirements create market demand for sophisticated fraud detection systems that can identify suspicious patterns, verify service delivery, and ensure compliance with regulatory requirements.

Digital therapeutics and care management applications are emerging as important tools for improving patient outcomes while reducing the cost of traditional home health services. These applications can provide patient education, medication management, symptom tracking, and care coordination support that extends the reach of clinical staff while improving patient engagement and adherence.

The integration of Internet of Things (IoT) devices and sensors into home health care delivery creates opportunities for continuous monitoring and intervention that were previously impossible. These technologies can track patient vital signs, medication adherence, activity levels, and environmental conditions while providing real-time alerts to clinical staff when intervention is needed.

Strategic Implications for Health Tech Entrepreneurs

The regulatory transformation of the home health industry creates a unique strategic environment for health technology entrepreneurs characterized by urgent market need, significant regulatory complexity, and substantial financial opportunity. Success in this environment requires careful navigation of multiple stakeholder interests while developing solutions that address immediate operational challenges and position companies for long-term growth as the industry continues to evolve.

The most immediate opportunity lies in developing solutions that help existing home health agencies adapt to the new payment model while maintaining operational efficiency. These solutions must deliver measurable value in terms of cost reduction, quality improvement, or compliance enhancement that can be clearly demonstrated to potential customers facing severe financial pressure. The urgency of the situation creates a favorable environment for companies that can rapidly deploy effective solutions.

Market entry strategies must carefully consider the regulatory environment and the specific pain points created by the 2026 proposed rule. Companies developing compliance management solutions, quality measurement platforms, or fraud prevention systems can leverage the regulatory requirements to create compelling value propositions. The key is developing solutions that address multiple regulatory requirements simultaneously while integrating seamlessly with existing operational workflows.

The financial pressure on home health agencies creates both opportunities and challenges for technology entrepreneurs. While agencies have urgent need for efficiency solutions, their reduced financial resources may limit their ability to invest in new technology systems. This dynamic favors companies that can demonstrate rapid return on investment or offer flexible pricing models that align with agencies' financial constraints.

Partnership strategies become particularly important in this environment. Technology companies should consider partnerships with larger home health agencies, managed care organizations, or health systems that can provide market access and credibility while offering the scale necessary to develop and deploy comprehensive solutions. These partnerships can also provide access to the patient data and operational insights necessary to develop effective solutions.

The emphasis on value-based care creates opportunities for technology companies to develop risk-sharing business models that align their incentives with those of their customers. Companies that can demonstrate their ability to improve outcomes while reducing costs may be able to participate in the financial upside of successful implementations. This approach requires sophisticated data analytics capabilities and clear outcome measurement but offers the potential for significant returns.

International expansion opportunities may emerge as other countries observe the U.S. experience with home health payment reform. Companies that successfully navigate the U.S. regulatory environment while developing scalable technology solutions may find opportunities to expand their offerings to other markets facing similar challenges with healthcare cost control and quality improvement.

The regulatory environment also creates opportunities for companies to influence policy development through participation in industry organizations, comment periods, and stakeholder engagement processes. Companies that can demonstrate expertise in both technology and healthcare policy may be able to shape future regulatory developments in ways that support their business objectives.

Talent acquisition and retention strategies must account for the specialized expertise required to succeed in the regulated healthcare environment. Companies need teams that combine deep healthcare industry knowledge with sophisticated technology capabilities and regulatory expertise. The competition for this talent is intense, requiring competitive compensation packages and compelling value propositions for potential employees.

Investment and funding strategies should consider the regulatory timeline and the need for rapid deployment of solutions. The implementation of the 2026 proposed rule creates a defined timeline for market opportunity that may influence investor interest and valuation approaches. Companies that can demonstrate rapid market traction and clear regulatory compliance may be particularly attractive to investors seeking exposure to healthcare technology transformation.

Future Market Structure and Competitive Landscape

The transformation of the home health industry through the 2026 proposed rule will create a fundamentally different competitive landscape characterized by increased consolidation, technology-enabled differentiation, and new forms of competition from non-traditional market participants. The financial pressure and regulatory requirements created by the rule will accelerate industry evolution that might otherwise have taken decades to unfold.

Industry consolidation represents one of the most significant structural changes likely to emerge from the regulatory transformation. Smaller, independent home health agencies lacking the capital resources and operational sophistication to adapt to the new payment model will face pressure to merge with larger organizations or exit the market entirely. This consolidation will create opportunities for technology companies to serve as the infrastructure backbone for consolidated service delivery networks while reducing the overall number of potential customers in the market.

The surviving home health agencies will likely be larger, more technologically sophisticated organizations with greater emphasis on data analytics, care coordination, and outcome measurement. These organizations will represent more attractive customers for technology companies but will also have more sophisticated procurement processes and higher expectations for solution performance and integration capabilities.

New market entrants from adjacent industries may emerge as significant competitors in the transformed home health landscape. Health systems, managed care organizations, and technology companies with healthcare expertise may see opportunities to enter the home health market through acquisition, partnership, or direct service development. These new entrants may bring different competitive dynamics and business models that challenge traditional home health service delivery approaches.

The emphasis on value-based care and outcome measurement will create new forms of competition based on demonstrated results rather than simply service delivery capacity. Home health agencies that can demonstrate superior outcomes, patient satisfaction, or cost efficiency will gain competitive advantages that allow them to capture market share and negotiate better contracts with payers. This outcome-based competition will favor organizations with sophisticated data analytics capabilities and continuous improvement processes.

Technology companies will play an increasingly important role in the competitive landscape as home health agencies become more dependent on technology solutions for operational efficiency and regulatory compliance. The companies that can develop comprehensive, integrated platforms addressing multiple operational challenges will be positioned to capture significant value while potentially influencing the competitive dynamics within the home health industry.

The regulatory environment will continue to evolve, creating ongoing opportunities for companies that can adapt quickly to changing requirements. CMS is seeking information on digital quality measurement transition for home health agencies and the adoption of health information technology, including Fast Healthcare Interoperability Resources (FHIR). This ongoing regulatory evolution creates opportunities for companies that can anticipate and prepare for future requirements while helping existing market participants adapt to changing conditions.

Geographic market dynamics may also shift as the new payment model affects the economics of serving different patient populations and geographic regions. Rural and underserved areas may face particular challenges under the new payment model, creating opportunities for technology-enabled solutions that can extend the reach of home health services while maintaining economic viability.

The competitive landscape will likely feature increased emphasis on specialization and niche market focus. Rather than attempting to serve all patient populations, successful home health agencies may focus on specific clinical conditions, patient demographics, or geographic regions where they can develop competitive advantages through specialized expertise and optimized care delivery models.

International competition may also emerge as global healthcare companies recognize the opportunities created by the U.S. home health market transformation. Companies with experience in value-based care delivery, regulatory compliance, and technology-enabled healthcare may enter the U.S. market through acquisition, partnership, or direct investment.

The future competitive landscape will ultimately be shaped by the ability of market participants to successfully navigate the complex interplay between regulatory requirements, financial pressures, and technological opportunities. Companies that can develop sustainable competitive advantages through technology innovation, operational excellence, and strategic positioning will be best positioned to thrive in the transformed home health industry.

The transformation of the home health industry through the 2026 CMS proposed rule represents a fundamental shift that will create winners and losers across the healthcare ecosystem. For health technology entrepreneurs, the regulatory changes create unprecedented opportunities to develop innovative solutions that address urgent market needs while building sustainable businesses in a rapidly evolving industry. Success will require careful attention to regulatory requirements, deep understanding of stakeholder needs, and the ability to rapidly deploy effective solutions that deliver measurable value. The companies that can successfully navigate this transformation will be positioned to capture significant market share while contributing to the broader evolution of healthcare delivery toward more efficient, outcome-focused models of care.

Table of Contents

1. Introduction: The Evolution of Medical Data Exchange

2. HIPAA's Framework and Its Boundaries

3. Consent and Authorization Mechanisms Beyond TPO

4. Patient-Directed Data Sharing and Personal Health Records

5. Research and Clinical Trial Applications

6. Public Health and Population Health Management

7. Commercial Partnerships and Data Monetization

8. Emerging Technologies and Future Frameworks

9. Regulatory Landscape and Compliance Considerations

10. Implementation Challenges and Best Practices

11. Future Outlook and Strategic Implications

12. Conclusion: Navigating the New Paradigm

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This moment bears striking resemblance to the evolution from closed corporate intranets to the open internet, from private blockchain networks to public distributed ledgers. Each transition forced organizations to confront their relationship with openness, their tolerance for risk, and their vision of competitive advantage in an increasingly connected world. Today's healthcare enterprises face identical questions as they develop AI governance frameworks that will determine not just their operational efficiency, but their very survival in an AI-driven future.

The stakes could not be higher. Healthcare organizations possess some of the most sensitive data on earth, protected health information that carries both regulatory obligations and profound moral imperatives. Simultaneously, they operate in an environment where AI's potential to save lives, reduce costs, and improve outcomes grows more compelling each day. The tension between these realities has created a complex landscape of compliance frameworks, security protocols, and governance structures that will fundamentally reshape how healthcare organizations operate in the coming decade.

The Current State of Healthcare AI Governance

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Prologue: The Paradox of Healing

In the vast landscape of American healthcare institutions, there exists a peculiar entity that embodies both the nation's guilt and its half-hearted attempts at redemption. The Indian Health Service (IHS) stands as a monument to what historians might call "obligatory atonement" – a system born not from generosity but from treaty obligations, not from medical vision but from political necessity. To understand the IHS is to understand the complex interplay between colonization, cultural genocide, legal maneuvering, and the struggle for basic human dignity that has characterized the relationship between the United States government and Indigenous peoples for centuries.

Like many institutions that bridge ancient promises and modern realities, the IHS exists in a perpetual state of contradiction. It is simultaneously crucial and chronically insufficient, legally mandated yet politically neglected, staffed by dedicated professionals yet hamstrung by systemic constraints. Its history is not merely an institutional timeline but a reflection of America's evolving but consistently troubled relationship with its original inhabitants.

Yuval Noah Harari might observe that the story of the Indian Health Service is a fascinating case study in how modern bureaucracies attempt to address historical injustices without fundamentally challenging the power structures that created those injustices in the first place. It represents what might be called "bureaucratic absolution" – the notion that systems of paperwork, clinical procedures, and administrative hierarchies can somehow remedy centuries of displacement, cultural destruction, and broken promises.

This essay will trace the arc of Indigenous healthcare in America from pre-colonial healing traditions through the creation and evolution of the IHS to the uncertain future that awaits this unique institution. In doing so, it will examine not just the technical details of healthcare delivery but the deeper questions of obligation, identity, and what it means to heal not just bodies but historical wounds.

Part I: Before the Promise - Indigenous Health Systems Prior to Colonization

Before examining the Indian Health Service as an institution, we must first understand what it replaced. The narrative commonly presented – that European settlers brought medicine to a primitive continent – is not merely inaccurate but represents what anthropologists might call an "imperial cognitive bias," the tendency to assume technological superiority equates to universal advancement.

Indigenous nations across North America had developed sophisticated health systems long before European contact. These were not uniform but varied widely according to environment, culture, and available resources – much like healthcare systems vary across modern nations today. What unified them was their integration of physical, spiritual, and community well-being in ways that modern Western medicine has only recently begun to recognize as valuable.

The Navajo (Diné) healing traditions incorporated elaborate ceremonies performed by specialized healers known as "hataałii" who underwent years of training comparable to modern medical education. Cherokee healers maintained extensive knowledge of hundreds of medicinal plants, many of which would later be appropriated by European pharmacology without acknowledgment. The Iroquois Confederacy developed complex public health approaches that included advanced urban sanitation systems at a time when European cities were drowning in their own waste.

These systems were not perfect – no healthcare system is – but they were adapted to the specific needs and contexts of their communities. They addressed both acute conditions and chronic management of disease. They incorporated preventive approaches alongside interventional ones. Perhaps most importantly, they recognized the connection between community well-being and individual health in ways that Western medicine would not acknowledge until the late 20th century.

Colonial narratives dismissing these systems as "primitive superstition" served a political purpose: to justify the imposition of European approaches and to categorize Indigenous peoples as intellectually inferior. This narrative framing helped rationalize the systematic destruction of Native knowledge systems by positioning colonizers as benevolent providers of superior medical care rather than as disruptors of functional existing systems.

The subsequent collision between these health systems represented more than mere clinical disagreement – it was a fundamental clash of epistemologies. Western medicine's mind-body dualism, derived from Cartesian philosophy, stood in stark contrast to Indigenous holistic approaches that saw no meaningful separation between physical symptoms and spiritual or community well-being.

As colonization progressed, these Indigenous health systems didn't simply disappear – they were actively suppressed through multiple mechanisms. Religious conversion efforts specifically targeted traditional healers as "witch doctors" or practitioners of "devil worship." Federal policies criminalized healing ceremonies. Boarding schools indoctrinated Indigenous children away from their traditional knowledge systems. The result was not merely the replacement of one medical system with another, but the creation of a healthcare void – one that would remain unfilled for generations.

By the late 19th century, this void had contributed to catastrophic health outcomes in Indigenous communities. The combination of introduced diseases, forced relocations, ecological disruption of traditional food systems, and the suppression of Indigenous medicine created a perfect storm of health crises. It was in this context – one of devastating health inequity created by colonial policies – that the earliest federal efforts at Indigenous healthcare emerged.

The fundamental irony, which would persist through the entire history of the IHS, was already apparent: the government that had systematically destroyed Indigenous healthcare systems now positioned itself as the solution to the very problems it had created. This pattern of creating crises through policy, then implementing insufficient remedies as acts of apparent benevolence, would characterize Indigenous healthcare for the next century and beyond.

Part II: The Seeds of a System - Early Federal Involvement in Indigenous Health (1800s-1920s)

The first formal U.S. government involvement in Indigenous healthcare did not emerge from humanitarian concern but from practical necessity. As American settlers pushed westward, infectious diseases threatened both Native populations and newcomers alike. The early federal approach to this challenge reveals much about the underlying attitudes that would later shape the Indian Health Service.

In 1832, Congress passed the first legislation specifically addressing Native American health – but its primary concern was vaccination against smallpox. This was less about protecting Indigenous lives and more about preventing epidemic spread to white settlements. The legislation authorized the Indian Office to hire physicians and purchase vaccine material, marking the first federal budget allocation for Indigenous healthcare.

This pattern – addressing Indigenous health primarily when it intersected with non-Indigenous interests – would repeat throughout the 19th century. Medical services were provided sporadically, usually as afterthoughts in treaties that primarily focused on land cessions. The 1855 Treaty with the Blackfeet, for example, promised one physician among its provisions, alongside promises of agricultural equipment and schools – all presented as "gifts" despite being compensation for millions of acres of surrendered land.

By the 1870s, a rudimentary system had emerged within the Bureau of Indian Affairs (BIA), with agency physicians assigned to reservations. These early efforts faced challenges that would remain familiar throughout IHS history: chronic underfunding, shortage of willing medical personnel, vast geographic territories to cover, and cultural barriers to care.

The records of these agency physicians reveal as much about colonial attitudes as they do about medical practice. Many documented their frustration with Indigenous patients' continued reliance on traditional healers. Few made efforts to understand the cultural contexts of the communities they served. Most viewed their role through the lens of the broader "civilization" program – the explicit government policy of forcing Indigenous peoples to abandon their cultural practices in favor of Euro-American norms.

The resulting healthcare was both medically and culturally inadequate. Indigenous communities, already devastated by forced relocations and ecological disruption, faced new health challenges including tuberculosis, which reached epidemic proportions on many reservations. The incidence of TB among some Indigenous populations was up to 10 times higher than in the general U.S. population by the early 20th century. Rather than addressing the overcrowded, unsuitable housing and nutritional deficiencies driving these epidemics, medical officials often blamed "Indian habits" and "primitive living conditions."

In 1910, a pivotal moment arrived when the first comprehensive government study of Indigenous health conditions was conducted. The resulting "Meriam Report," published in 1928, documented appalling statistics: infant mortality rates among Indigenous populations up to 300% higher than the general population; tuberculosis rates 600% higher; average life expectancy approximately 44 years compared to 60 years for white Americans.

What made the Meriam Report significant was not just its documentation of these disparities but its explicit recognition of their causes. The report directly linked these health outcomes to federal policies of land allotment, cultural suppression, and inadequate services. It represented perhaps the first official acknowledgment that the health crisis in Indigenous communities was not a result of inherent racial susceptibility or cultural inferiority but of specific government actions and inactions.

The report recommended a dramatic increase in funding, specialized training for medical personnel working in Indigenous communities, and incorporation of Indigenous perspectives in healthcare planning. These recommendations, revolutionary for their time, would influence the next phase of Indigenous healthcare development, though their implementation would remain frustratingly incomplete.

By the 1920s, what existed was not yet a coherent healthcare system but a patchwork of underfunded services, varying dramatically by location and often contingent on the particular attitudes of local BIA officials. The seeds of a system had been planted, but they had germinated in soil contaminated by paternalism, cultural supremacy, and chronic resource starvation.

Part III: The Birth of an Institution - From BIA Medical Division to the Indian Health Service (1920s-1955)

The period between the Meriam Report and the formal establishment of the Indian Health Service represents a critical transitional phase in Indigenous healthcare. During these decades, fundamental questions were debated about federal obligations, appropriate models of service delivery, and the very purpose of Indigenous healthcare programs.

The 1920s saw the first serious efforts to professionalize the BIA's medical services. The Division of Indian Health was established within the Bureau in 1924, coincidentally the same year that the Indian Citizenship Act granted U.S. citizenship to all Native Americans born in the United States. This pairing reflects the contradictory impulses of the era – extending nominal rights while maintaining paternalistic control over essential services.

Under the leadership of Dr. Mary Frett Riggs, the first medical director, the Division began standardizing healthcare delivery across reservations. Nursing positions were established, hospital construction accelerated, and the first efforts at preventive healthcare programs emerged. Yet these improvements remained severely constrained by budgetary limitations – the Division received approximately $4.60 per capita annually for Indigenous healthcare compared to over $20 per capita spent on the general population.

The Great Depression initially threatened even these modest gains, as federal budgets contracted across all departments. Paradoxically, however, the New Deal era ultimately strengthened Indigenous healthcare infrastructure through programs like the Civilian Conservation Corps-Indian Division, which built water and sanitation systems on reservations, and the Indian Emergency Conservation Work program, which constructed healthcare facilities.

More significant than these material improvements was the philosophical shift represented by the Indian Reorganization Act of 1934, often called the "Indian New Deal." This legislation formally ended the disastrous allotment policy that had fractured tribal lands and attempted to rebuild tribal governance structures. In healthcare, this translated to the first meaningful attempts to incorporate Indigenous perspectives into program planning.

Under BIA Commissioner John Collier, who served from 1933 to 1945, there were pioneering efforts to integrate traditional healing practices with Western medicine in some BIA facilities. The Navajo-Cornell Field Health Project, begun in 1955, represented perhaps the first serious attempt by federal officials to bridge the gap between medical systems through cross-cultural training for healthcare providers and formal cooperation with traditional healers.

World War II temporarily halted this progress as resources were diverted to the war effort. Yet the war also had unexpected effects on Indigenous healthcare. Over 44,000 Native Americans served in the military, experiencing for the first time equal access to medical services through military healthcare. This exposure created new expectations for quality and contributed to the growing activism around healthcare rights after the war. Additionally, returning Indigenous veterans who had served as medics brought valuable skills back to their communities.

The post-war period saw two contradictory policy directions emerge simultaneously. The first was "termination" – the federal policy of ending the special relationship between tribes and the federal government, effectively abandoning treaty obligations including healthcare provisions. Several tribes had their federal recognition revoked under this policy between 1953 and 1968.

Simultaneously, however, there was growing recognition that Indigenous healthcare was too badly neglected to continue under the BIA's management. Public health professionals increasingly advocated for transferring these responsibilities to agencies with specific health expertise. A series of shocking exposés in national publications documented third-world conditions in reservation healthcare facilities, creating political pressure for reform.

In this context, the Transfer Act of 1954 (officially the Indian Health Facilities Act) moved responsibility for Indigenous healthcare from the BIA to the Public Health Service, a division of what was then the Department of Health, Education and Welfare. This transfer, implemented in July 1955, created the modern Indian Health Service as a discrete entity with its own administrative structure and budget.

The creation of the IHS represented both an acknowledgment of the federal government's continuing responsibility for Indigenous healthcare and a recognition that this responsibility had been shamefully neglected. The transfer to health professionals rather than administrators was intended to improve care quality and bring Indigenous healthcare in line with mainstream American medical standards.

Yet the new agency inherited all the problems of its predecessor: chronic underfunding, facilities in disrepair, difficulties recruiting and retaining qualified staff, and enormous geographical challenges. It also inherited a paternalistic approach that continued to view Indigenous communities as passive recipients of services rather than partners in healthcare design and delivery.

As the newly formed IHS took shape, it carried these contradictions forward – a professional health agency born from recognized federal obligation yet constrained by limited resources and cultural assumptions that would continue to hamper its effectiveness for decades to come.

Part IV: Expansion and Transformation - The IHS from 1955 to 1975

The first two decades of the Indian Health Service's existence were characterized by significant expansion of services alongside persistent structural limitations. This period coincided with America's broader commitment to public health infrastructure and medical advancement – the golden age of American healthcare that saw the implementation of Medicare and Medicaid, major hospital construction nationwide, and remarkable innovations in medical treatment.

The IHS benefited from this national prioritization of healthcare. Its budget increased from $24.5 million in 1955 to over $200 million by 1973. New hospitals and clinics were constructed, replacing facilities that dated from the early 1900s. The agency expanded its workforce from approximately 2,500 employees in 1955 to over 8,000 by 1975. Perhaps most significantly, it developed specialized approaches to rural healthcare delivery that acknowledged the unique geographical challenges of serving Indigenous communities.

The health outcomes during this period reflected these investments. Between 1955 and 1975:

• Infant mortality among Indigenous populations decreased by 82%

• The maternal death rate declined by 89%

• Deaths from tuberculosis dropped by 94%

• Gastrointestinal disease mortality fell by 93%

These statistics, frequently cited in IHS annual reports, demonstrated real progress. Yet they obscured a more complex reality. While acute infectious diseases were being effectively addressed, chronic conditions related to poverty, displacement, and cultural trauma remained prevalent. Furthermore, these aggregate improvements masked significant regional disparities in care access and quality.

The structural limitations of the IHS became increasingly apparent during this period. Despite budget increases, per capita spending remained far below that of other federal healthcare programs. By 1974, the IHS spent approximately $286 per person annually compared to $547 per Medicare beneficiary. Facilities remained concentrated on reservations despite the growing urban Indigenous population – by 1970, nearly 45% of American Indians lived in cities, but the IHS had almost no urban presence.

The agency's approach to healthcare delivery also remained fundamentally paternalistic. Services were designed and implemented with minimal input from Indigenous communities. Medical professionals, predominantly non-Native, often viewed cultural differences as obstacles to care rather than foundations for more effective health approaches. Mental health services were particularly neglectful of cultural contexts, applying Western psychiatric models to communities experiencing the intergenerational effects of historical trauma.

By the late 1960s, these limitations had become focal points for Indigenous activism. The American Indian Movement and other Native rights organizations began explicitly demanding healthcare reform as part of their broader agenda. Their critiques centered not just on funding inadequacies but on the structural disempowerment of Indigenous communities within the healthcare system that supposedly served them.

This activism coincided with broader social movements demanding civic inclusion and institutional accountability. Together, these pressures led to the most transformative legislation in the history of Indigenous healthcare: the Indian Self-Determination and Education Assistance Act of 1975.

This landmark law fundamentally reimagined the relationship between Indigenous nations and federal services. It created mechanisms for tribes to contract directly with the federal government to manage their own healthcare programs, using IHS funds but according to tribally-determined priorities. For the first time since colonization, Indigenous nations had a legal pathway to regain control over how healthcare was delivered to their communities.

The significance of this shift cannot be overstated. It represented not merely an administrative reorganization but a philosophical reversal – from healthcare as something done to Indigenous communities to healthcare as something done by them. In historical terms, it marked the beginning of the end of the purely paternalistic model that had characterized federal Indigenous healthcare since its inception.

Yet the Self-Determination Act contained its own contradictions. It created pathways for tribal management but maintained federal funding controls. It recognized tribal authority but within parameters established by federal regulations. Perhaps most significantly, it acknowledged the right to self-determined healthcare without providing the resources necessary to address centuries of health inequities.

As the IHS entered its third decade, it was becoming something unprecedented in American healthcare – a hybrid system blending federal authority with increasing tribal control, struggling to reconcile treaty obligations with budget constraints, and attempting to bridge Western medical approaches with resurgent Indigenous healing traditions. This evolution would accelerate in the coming decades, producing both innovations and persistent challenges.

Part V: The Self-Determination Era - Tribal Management and Persistent Challenges (1975-2000)

The passage of the Indian Self-Determination Act in 1975 initiated a profound transformation in Indigenous healthcare delivery. The legislation's core mechanism – allowing tribes to contract with the federal government to administer their own health programs – seemed simple in concept but represented a revolutionary redistribution of power. For the first time since colonization, Indigenous nations had a legally protected pathway to determine how healthcare would be provided to their communities.

The implementation of this new paradigm proved both promising and challenging. Initially, tribes approached self-determination cautiously. By 1980, only 30 tribes had established contracts to manage portions of their healthcare services. This hesitation reflected both the administrative complexity of healthcare management and the lingering effects of generations of dependency imposed by federal policy.

As the 1980s progressed, however, tribal contracting accelerated dramatically. By 1990, over 200 tribes were managing some portion of their healthcare services. This growth reflected both increasing tribal administrative capacity and spreading recognition of the benefits of local control. The Cherokee Nation's healthcare system emerged as an early success story, demonstrating how tribal management could increase both clinical quality and cultural appropriateness of services.

The self-determination era revealed an interesting sociological pattern: when people gain control over institutions that serve them, they tend to invest more deeply in those institutions' success. Communities that had historically viewed the IHS with suspicion began developing genuine ownership over their healthcare systems once tribal management began. Patient satisfaction increased, as did utilization of preventive services. Healthcare facilities became not just service providers but sources of community pride and economic development.

The 1988 Indian Health Care Improvement Act amendments accelerated this transformation by creating the Self-Governance Demonstration Project, which expanded tribal authority beyond simple contracting to comprehensive program management. This approach, later made permanent through the 1994 Indian Self-Determination Act amendments, allowed participating tribes to receive block funding rather than line-item allocations, giving them unprecedented flexibility in program design.

By 2000, approximately 50% of the IHS budget was being administered directly by tribes. Some, like the Alaska Native Tribal Health Consortium, had taken over entire regional healthcare systems. Others managed specific programs while leaving hospital operations to the IHS. This diversity of approaches reflected the principle at the heart of self-determination: that Indigenous nations should be free to choose the healthcare models that best suited their specific needs, capacities, and cultural contexts.

The health outcomes of this era showed both progress and persistent challenges. Life expectancy for American Indians and Alaska Natives increased from 63.6 years in 1973 to 71.1 years by 1999 – a remarkable improvement, though still below the general U.S. population's 76.5 years. Infant mortality decreased by 58% between 1972 and 1999, narrowing but not eliminating the gap with the general population.

Perhaps the most significant innovation of this era was the integration of traditional healing practices into formal healthcare systems. Tribally managed programs pioneered approaches that respected Indigenous healing traditions not as interesting cultural artifacts but as legitimate therapeutic modalities. The Navajo Nation's integration of traditional healers (hataałii) into clinical teams, the implementation of healing lodges adjacent to IHS hospitals, and the incorporation of traditional midwifery into maternal care programs represented attempts to heal not just bodies but the historical rupture between medical systems.

Yet despite these advances, the self-determination era was characterized by persistent structural challenges that limited progress. The most significant was chronic underfunding. Throughout this period, the IHS budget received increases that barely kept pace with inflation and population growth, let alone addressed the accumulated healthcare deficits of centuries. By 1999, the IHS was spending approximately $1,350 per patient annually, compared to $3,700 per capita in other federal healthcare programs.

This resource constraint meant that tribal innovation occurred within sharply limited parameters. Healthcare facilities remained outdated, with an average age of 32 years compared to 9 years for private sector hospitals. Specialized services were limited, forcing many patients to rely on "Purchased/Referred Care" (formerly Contract Health Services) – a chronically underfunded program for buying services from non-IHS providers when needed care wasn't available within the system.

The 1980s and 1990s also saw increasing tension between trust responsibility and federal cost-cutting. The Reagan administration's attempts to significantly reduce the IHS budget were largely prevented by Congressional intervention, but the agency remained vulnerable to broader political trends favoring reduced federal spending. The resulting budgetary instability complicated long-term planning for both the IHS and tribal health programs.

Another challenge was the growing urban-rural divide in Indigenous healthcare. Despite the fact that by 1990 over 60% of American Indians lived in urban areas, less than 1% of the IHS budget was allocated to urban Indian health programs. This disparity reflected the IHS's origins as a reservation-based system but increasingly failed to serve the reality of Indigenous demographics.

Perhaps most fundamentally, the self-determination era revealed the inherent tension between tribal sovereignty and federal responsibility. As tribes took over healthcare management, complex questions emerged: Was the federal government transferring authority or abandoning responsibility? Did increased tribal control justify reduced federal funding? Could true healthcare sovereignty exist within the constraints of federal regulations and standards?

These questions had no simple answers, but they revealed the essential paradox at the heart of Indigenous healthcare in America: a system simultaneously seeking to honor historical obligations while adapting to contemporary realities, to respect tribal sovereignty while maintaining federal responsibility, to preserve cultural traditions while embracing medical advances. As the millennium turned, this paradox would only intensify.

Part VI: The Modern IHS - Crisis and Innovation (2000-2020)

As the 21st century began, the Indian Health Service found itself at a critical juncture – an agency with an increasingly clear mission but persistently inadequate resources to fulfill it. The first two decades of the new millennium would see this contradiction produce both devastating failures and remarkable innovations.

The passage of the permanent reauthorization of the Indian Health Care Improvement Act in 2010 (as part of the Affordable Care Act) represented a significant legal victory, ending decades of temporary reauthorizations. The legislation modernized the IHS's statutory authority, allowing for expanded behavioral health services, long-term care, and health professional recruitment programs. It affirmed, in legal terms, the federal government's enduring commitment to Indigenous healthcare.

Yet this legal commitment continued to stand in stark contrast to budgetary reality. By 2015, the IHS was spending approximately $3,136 per patient annually, compared to $8,517 per capita in national healthcare spending. This disparity manifested in tangible ways: outdated facilities, equipment shortages, and most critically, staffing vacancies that in some locations reached 25% of authorized positions.

These resource constraints produced highly publicized failures that shook confidence in the system. The 2015-2016 crisis in the Great Plains Area IHS facilities, where several hospitals temporarily lost Medicare certification due to dangerous quality deficiencies, revealed the consequences of decades of underfunding. Congressional investigations documented emergency rooms staffed by inexperienced providers, sterilization equipment failures, and patients receiving care in converted closets and bathroom areas.

Such crises reflected not merely administrative failures but what organizational psychologists might call "predictable system collapse" – the inevitable result of asking an organization to fulfill an expanding mission with stagnant resources. The IHS was increasingly caught in what public health scholars term the "expectation-resource gap," where public demands for healthcare quality rise while the resources to meet those demands remain flat.

Yet alongside these challenges, the period saw remarkable innovations emerging primarily from tribally managed healthcare systems. The Southcentral Foundation's "Nuka System of Care" in Alaska transformed Indigenous healthcare delivery through its customer-ownership model, integrated care teams, and focus on relationship-based care. By emphasizing prevention, cultural connection, and addressing social determinants of health, the system achieved impressive outcomes, including 36% reduction in hospital days, 42% reduction in urgent care and emergency department visits, and 58% reduction in specialty care visits.

Other tribal innovations included the Cherokee Nation's public health system, which incorporated traditional cultural knowledge into contemporary public health approaches; the Navajo Nation's community health representative program, which trained local community members as health liaisons; and the Urban Indian Health Institute's development of Indigenous-specific epidemiological approaches that accounted for the complex realities of Indigenous identity and health determinants.

These successes demonstrated that when Indigenous communities controlled their healthcare systems and had adequate resources, they could develop approaches that outperformed conventional models. Yet they remained islands of excellence in a system still characterized by scarcity and struggle.

The period also saw significant demographic and epidemiological shifts in Indigenous health challenges. While historical threats like tuberculosis and gastrointestinal diseases had largely been controlled, new epidemics emerged. By 2017, American Indians and Alaska Natives had the highest rates of diabetes (15.1%), second-highest rates of opioid overdose deaths, and significantly elevated rates of suicide, particularly among youth. These conditions reflected not just healthcare system failures but the continuing impacts of historical trauma, economic marginalization, and social disruption.

The digital transformation of healthcare presented both opportunities and challenges for the IHS. The agency successfully implemented a unified electronic health record system (the Resource and Patient Management System) across its facilities – an achievement that many larger healthcare systems failed to accomplish. Yet many IHS and tribal facilities, particularly in remote areas, lacked the telecommunications infrastructure to fully utilize telemedicine and other digital health innovations that might have helped address provider shortages.

Perhaps the most significant development of this era was the increasing recognition – both within Indigenous communities and in broader medical discourse – that Indigenous health disparities were rooted not just in clinical care deficiencies but in broader social, economic, and historical factors. This understanding led to expanded approaches that addressed what public health experts call "social determinants of health" – the conditions in which people live, work, learn, and play.

Tribal health programs increasingly developed initiatives addressing food sovereignty, housing quality, educational opportunity, and economic development alongside traditional healthcare services. The Pueblo of Jemez's incorporation of traditional agriculture programs into diabetes prevention efforts, the Coeur d'Alene Tribe's integration of cultural revitalization with substance abuse treatment, and the Oglala Sioux Tribe's community-based trauma intervention programs exemplified this holistic approach.

By 2020, the Indian Health Service had evolved into a complex, hybrid system characterized by tremendous variation in delivery models, infrastructure quality, and outcomes. Some tribal communities had developed world-class healthcare systems that served as models of innovation. Others, particularly in the most remote areas, continued to struggle with basic access to care. This variation reflected both the promise and the limitations of the self-determination approach – it allowed for local innovation but did not ensure equitable resources across communities.

As the COVID-19 pandemic struck Indigenous communities with devastating force in 2020, these contradictions were thrown into sharp relief. The pandemic exposed the consequences of decades of underfunding – inadequate facilities, staff shortages, and fragile supply chains. Yet it also demonstrated the resilience and adaptive capacity of Indigenous healthcare systems, as tribal governments implemented effective public health measures and achieved among the highest COVID-19 vaccination rates in the country once vaccines became available.

This crisis response demonstrated the unique strength of the modern IHS – its combination of federal resources with community-based implementation. It suggested a possible future direction for the agency: federal investment paired with local control, national standards with cultural adaptation, scientific medicine integrated with traditional healing approaches. Whether this potential could be realized would depend on the political, budgetary, and organizational choices of the coming decades.

Part VII: The Uncertain Future - Projections and Possibilities (2020-2050)

Predicting the future of any institution is a precarious exercise, particularly one as complex and politically contingent as the Indian Health Service. Yet certain trajectories seem probable based on current trends, while others represent possible futures dependent on policy choices and socioeconomic developments. This final section examines both the likely evolution of the IHS in the coming decades and the potential alternative paths that might emerge.

The most probable trajectory for the IHS through 2050 is continued evolution toward a predominantly tribally-managed system. The historical trend is clear: from the first self-determination contracts in the 1970s to the present, tribes have steadily assumed greater control over their healthcare services. By 2020, approximately 60% of the IHS budget was being administered by tribes. If current trends continue, this could reach 80-85% by 2050, with the federal IHS focusing primarily on standards, funding distribution, and serving communities that choose not to manage their own healthcare systems.

This evolution will likely accelerate as a new generation of Indigenous healthcare administrators – many trained in tribal colleges and universities with specific preparation for managing Indigenous health systems – assumes leadership positions. These professionals, fluent in both Western healthcare administration and traditional Indigenous values, are positioned to further transform tribal healthcare systems in ways their predecessors could only imagine.

Demographics will drive significant changes in IHS service delivery models. The Indigenous population in the United States is both growing and urbanizing. Census projections suggest the American Indian and Alaska Native population will increase from approximately 6.9 million in 2020 to over 10 million by 2050. Meanwhile, the percentage living in urban areas is likely to increase from approximately 70% to over 80%. This shift will create increasing pressure to redirect IHS resources toward urban programs and develop new service models for geographically dispersed populations.

Technology will transform Indigenous healthcare delivery, potentially helping to address the persistent geographic challenges of the IHS. Telemedicine, already growing rapidly after the COVID-19 pandemic, could become the primary care modality for many services by 2030. Remote monitoring technologies, artificial intelligence-assisted diagnostics, and digital health applications designed specifically for Indigenous populations could extend specialized care to communities that have historically lacked access.

Climate change will present new challenges for Indigenous health, particularly in Alaska, where melting permafrost is already damaging healthcare infrastructure, and in the Southwest, where water scarcity threatens both traditional foodways and basic sanitation. The IHS will increasingly need to function as a climate adaptation agency, helping communities develop resilient healthcare infrastructure and addressing the health impacts of ecological disruption.

The epidemiological profile of Indigenous communities will continue to evolve. If current trends persist, chronic conditions like diabetes, cardiovascular disease, and mental health disorders will remain the primary health challenges, though their prevalence may stabilize or decrease with improved prevention efforts. New health threats – including those related to climate change, emerging infectious diseases, and technological exposures – will need to be addressed through adaptive public health approaches.

Yet these projections assume continuation of current trends rather than transformative change. Several alternative futures are possible depending on policy choices, resource allocation, and broader societal developments.

One potential transformative path would involve full funding of the IHS at parity with other federal healthcare programs. Economic analyses suggest this would require approximately tripling the current IHS budget – a significant investment, but one that multiple studies indicate would yield substantial returns through reduced long-term healthcare costs, increased productivity, and improved quality of life. Full funding would allow for comprehensive facility modernization, competitive staff compensation, expansion of specialized services, and adequate preventive programs.

While historically such funding increases have seemed politically unrealistic, shifting public understanding of racial equity and historical responsibility could change this calculus. The growing recognition of the federal government's legal obligations to Indigenous nations – not as benefits but as payments for land and resources – provides a conceptual framework for justifying such investments.

Another transformative possibility is the integration of traditional Indigenous healing systems as equal partners with Western medicine, rather than as supplementary or alternative approaches. This would represent not merely practical integration but epistemological integration – recognizing Indigenous knowledge systems as valid scientific frameworks rather than cultural practices. Some tribal healthcare systems are already moving in this direction, developing research methodologies that validate traditional practices through both Western and Indigenous evaluative approaches.

A third possibility is the emergence of the IHS and tribal health systems as global leaders in addressing health equity for Indigenous and marginalized populations worldwide. The knowledge developed through decades of providing care in resource-constrained environments to populations with complex historical trauma could be formalized and shared with other nations grappling with similar challenges. Some tribal health organizations are already engaging in such knowledge exchange with Indigenous communities in Canada, Australia, and New Zealand.

Perhaps the most profound potential transformation would involve reconceptualizing the IHS not as a healthcare system for a disadvantaged minority but as a model for the future of American healthcare more broadly. The emphasis on community-controlled care, integration of physical and behavioral health, focus on social determinants, and incorporation of cultural values that characterizes the best tribal health systems offers a compelling alternative to the fragmented, hyperspecialized American healthcare system.

Yet alongside these possibilities for progress, significant threats to the IHS's future exist. Political movements seeking to terminate federal obligations to Indigenous nations have emerged periodically throughout American history and could resurface. Economic pressures following the massive public expenditures of the pandemic era could lead to austerity measures affecting all federal programs, including the IHS. Climate change could overwhelm healthcare systems, particularly in the most vulnerable communities.

What makes the future of the IHS particularly unpredictable is that it exists at the intersection of multiple complex systems: the American healthcare system, federal-tribal political relationships, cultural revitalization movements, technological development, and climate change impacts. Each of these systems is undergoing rapid change, creating both new possibilities and new vulnerabilities.

Perhaps the most accurate prediction is that the IHS of 2050 will reflect the same fundamental tension that has characterized Indigenous healthcare since the federal government first became involved: the tension between obligation and opportunity, between repairing historical harms and creating new possibilities, between Western medical paradigms and Indigenous healing traditions. This tension need not be seen as a weakness but rather as a creative force driving innovation and adaptation.

Epilogue: The Meaning of Healing

The story of the Indian Health Service is more than an institutional history – it is a microcosm of the larger relationship between the United States and Indigenous nations, and a case study in how societies attempt to address historical injustices through contemporary institutions. Its evolution reveals much about the possibilities and limitations of bureaucratic solutions to profound historical wounds.

Throughout this narrative, we have traced the arc of a fundamentally paradoxical institution – one born from treaty obligations yet chronically prevented from fulfilling them, committed to health equity yet perpetually underfunded, increasingly controlled by Indigenous communities yet still constrained by federal parameters. These contradictions are not incidental but central to understanding both the IHS's limitations and its resilience.

What makes the IHS unique in American healthcare is precisely this paradoxical nature. It is simultaneously a federal agency, a treaty obligation, a healthcare provider, and increasingly, a platform for Indigenous self-determination. No other American healthcare institution carries such a complex identity or serves such multifaceted purposes. This complexity explains both its struggles and its innovations.

From an anthropological perspective, the evolution of the IHS represents a case study in what might be called "institutional adaptation under constraint" – the process by which organizations evolve to serve their core purposes despite persistent resource limitations and political obstacles. The agency's history demonstrates how institutional creativity can emerge not despite constraints but because of them, as limitations force the development of innovative approaches that might otherwise never have been attempted.

The IHS's trajectory also illustrates a broader pattern in how modern bureaucratic states attempt to address historical injustices. Rather than fundamental reparation or structural reform, the tendency is toward the creation of specialized agencies tasked with managing the consequences of historical harms without addressing their root causes. These agencies become both the embodiment of the state's acknowledgment of responsibility and the limits of that acknowledgment – symbolic of both obligation and its containment.

For Indigenous communities, the IHS has been both a lifeline and a disappointment, a source of essential care and a reminder of unfulfilled promises. Its hospitals and clinics serve as physical manifestations of the complex relationship between these communities and the federal government – spaces where healing occurs daily despite historical wounds that remain unhealed.

Perhaps the most profound lesson from the IHS's history concerns the nature of healing itself. Western medicine typically conceives of healing as the restoration of normal physiological function in an individual body. Indigenous healing traditions, by contrast, often understand healing as the restoration of balance – not just within the individual but between the individual and their community, their environment, their spiritual world.

The evolution of the IHS reflects a gradual, incomplete movement toward this more expansive understanding of healing. From its origins as a provider of basic medical interventions, it has evolved toward approaches that increasingly recognize the interconnection between physical health, mental wellbeing, cultural identity, and community viability. This evolution mirrors broader shifts in medical understanding but has been particularly pronounced in Indigenous healthcare contexts.

The future of the IHS will likely continue this trajectory, developing approaches that increasingly integrate Western medical techniques with Indigenous healing traditions, that address not just immediate symptoms but underlying determinants of health, that heal not just bodies but relationships – between people, between communities, between nations, and between humans and the natural world.

In this sense, the IHS may be evolving toward something its founders never imagined – not merely a healthcare delivery system for a specific population but a laboratory for reimagining what healthcare itself might mean in a world increasingly recognizing the limitations of fragmented, specialized, disease-focused medical systems.

Whether this evolution will be supported with adequate resources remains the central question. The history of the IHS suggests that moral obligations alone have rarely been sufficient to generate political will for proper funding. Yet the growing recognition of the legal basis for these obligations, combined with increasing Indigenous political influence and broader societal reckonings with historical injustices, may create new possibilities for fulfilling the promises made generations ago.

If there is cause for optimism about the IHS's future, it lies not primarily in federal policy commitments but in the remarkable resilience and adaptive capacity of Indigenous communities themselves. Throughout five centuries of colonization, epidemic disease, forced relocation, and cultural suppression, these communities have maintained their identities, preserved core healing traditions, and continually adapted to new challenges. The increasing tribal management of healthcare systems represents not just an administrative shift but the latest chapter in this long history of adaptation and survival.

What makes this particular chapter unique is that it occurs in a context where Indigenous knowledge systems are increasingly recognized not as historical curiosities but as sophisticated frameworks with profound relevance to contemporary challenges. Traditional ecological knowledge, once dismissed as primitive, is now studied by climate scientists. Indigenous approaches to community healing are being examined by trauma specialists. Traditional food systems are being reconsidered by nutritionists. In this context, Indigenous healthcare approaches may increasingly be seen not as alternatives to mainstream medicine but as innovative complements with broader applicability.

The ultimate test of whether the United States has truly reckoned with its historical obligations to Indigenous peoples will not be legal acknowledgments or symbolic gestures but whether it provides the resources necessary for Indigenous communities to heal on their own terms – not just from immediate illnesses but from historical traumas, not just individually but collectively, not just physiologically but culturally and spiritually.

The Indian Health Service stands at the intersection of this obligation and opportunity. Its future will be shaped not just by budget allocations and administrative decisions but by how American society answers fundamental questions about responsibility, justice, and the meaning of healing itself. In this sense, the story of the IHS is not merely institutional history but a window into America's ongoing struggle to reconcile its founding ideals with its historical actions, and to imagine more just relationships with the nations that preceded it on this continent.

Perhaps the most fitting conclusion comes from a Navajo healer who, when asked about the relationship between traditional healing and modern medicine, replied: "Both are concerned with restoring hózhǫ́ – balance and harmony. One focuses on the body, the other on the whole person in their world. The wisest path uses both, walking in beauty." The future of Indigenous healthcare in America may lie precisely in this wisdom – integrating approaches to heal not just symptoms but the deeper imbalances that produce them, not just individuals but communities, not just present conditions but historical wounds.

In this perspective, the Indian Health Service is not merely a healthcare provider but a potential pathway toward a more profound healing – one that could transform not only Indigenous health outcomes but our collective understanding of what health itself means in a world increasingly recognizing the interconnection of all wellbeing.

For over five decades, HHS had operated under what insiders called the "Richardson Waiver," named after former HHS Secretary Elliot Richardson who implemented it in 1971. This self-imposed policy required the Department to follow the full notice-and-comment rulemaking procedures for virtually all of its actions, even when federal law—specifically, the Administrative Procedure Act (APA)—explicitly exempted certain categories from these requirements.

The Richardson era represented a time when expanded public participation was viewed as inherently beneficial, regardless of efficiency costs. Under this policy, even when crafting rules about grants, benefits, contracts, or internal agency management, HHS voluntarily subjected itself to a process that could take months or even years: publishing proposed rules, accepting public comments, reviewing each submission, and crafting detailed responses before finalizing any action.

But times and administrations change. Secretary Kennedy's policy statement frames the Richardson Waiver as more burden than benefit—a well-intentioned but ultimately counterproductive constraint on the Department's ability to fulfill its mission efficiently. The statement characterizes the waiver as "contrary to the clear text of the APA" and argues that it "imposes costs on the Department and the public, is contrary to the efficient operation of the Department, and impedes the Department's flexibility to adapt quickly to legal and policy mandates."

The timing is notable. As healthcare challenges continue to evolve rapidly post-pandemic, the administration clearly seeks greater nimbleness in how it addresses emergent issues. The document reflects a certain pragmatism—a belief that sometimes, procedural efficiency serves the public interest better than procedural maximalism.

Critics will undoubtedly view this change through a political lens, seeing it as part of a broader effort to reduce regulatory barriers or limit public input. Supporters will likely frame it as simply adhering to the letter of the law as written by Congress in the APA, which explicitly carved out these exemptions for practical governance reasons.

What remains unaddressed in the terse, legalistic document is how the Department will balance competing values moving forward. While the policy gives HHS components "discretion" to still use notice and comment when appropriate, it offers little guidance on when such additional procedures would be warranted versus when expediency should prevail.

As this policy takes effect, the true test will lie in how the Department wields its newly reclaimed flexibility. Will it use these streamlined procedures judiciously to address truly time-sensitive matters? Or will significant policy changes increasingly occur without the formal public feedback mechanisms that have become standard practice across the federal government?

For now, the Richardson Waiver slips into history, ending an era of administrative practice at one of the government's largest departments. In its place emerges a more textual approach to the APA—one that embraces both the requirements and the exemptions that Congress built into the administrative state. Whether this marks the beginning of a broader shift across government or remains unique to HHS under its current leadership remains to be seen.

The American healthcare system represents over a century of evolving policies, regulations, and reforms that have shaped how healthcare is delivered and financed in the United States. This analysis examines every major regulatory event and policy development in chronological order, beginning with the establishment of Industrial Sickness Funds in the early 1900s and extending through to modern healthcare reform efforts. These reforms reflect the complex interplay of societal needs, economic constraints, and political ideologies, each contributing uniquely to the current healthcare landscape.

Early Reform Efforts (1900s–1919)

The story of American healthcare reform begins in the early 1900s with the Industrial Sickness Funds, which were created to provide financial support to workers who became ill or injured. These funds offered cash payments to cover lost wages and medical expenses, representing one of the first organized efforts to address the healthcare needs of American workers.

In 1912, President Theodore Roosevelt brought healthcare reform into the national spotlight by proposing the first universal healthcare system. Although this bold initiative under the Progressive Party platform ultimately failed, it was a pivotal moment that placed healthcare at the forefront of political discourse.

Healthcare reform efforts gained traction in 1915 with the efforts of progressive reformers led by the American Association of Labor Legislation (AALL). The AALL’s campaign for state-based compulsory health insurance marked the first comprehensive effort to mandate healthcare coverage. Their proposals included medical care, sick pay, maternity benefits, and even death benefits for low-wage workers.

However, reform momentum was disrupted by America’s involvement in World War I (1917–1918). The war effort shifted national priorities, while the association of compulsory health insurance with German social policies created political challenges that stalled reform efforts.

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