Riding the Wave: Nine Business Models for Healthcare’s Next Chapter
DISCLAIMER: The views and opinions expressed in this essay are my own and do not reflect the views of my employer or any other entities.
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Table of Contents
Abstract
Introduction: Why This Time Actually Matters
The Prevention Infrastructure Play
Consumer Engagement as a Service
The MA Enablement Stack
Pharmacy Transparency and Alternative Models
CMMI Model Operators
AI Implementation Partners
Dual-Eligible Integration Platforms
ICHRA Infrastructure Bets
Reconciliation Arbitrage Opportunities
Conclusion: Picking Your Wave
Abstract
Town Hall Ventures has identified nine major healthcare shifts likely to reshape the industry between 2025 and 2027, ranging from the MAHA movement to pharmacy reform to Medicare Advantage expansion. For angel investors and entrepreneurs, these shifts represent discrete opportunities to build businesses addressing specific market needs created by policy changes, technology adoption, and evolving payment models. This essay examines each trend through an investment lens, proposing concrete business models and company archetypes that could capture value during this transition period. Key opportunities include: prevention infrastructure enabling outcomes-based contracts, AI implementation services for provider organizations, dual-eligible care coordination platforms, and MA enablement software. The analysis focuses on identifying wedge products, go-to-market strategies, and defensible moats for early-stage companies targeting these opportunities. While policy uncertainty remains high, the convergence of regulatory change and generative AI capabilities creates conditions favoring nimble startups over incumbent players in several categories.
Introduction: Why This Time Actually Matters
Every few years someone publishes a breathless piece about how healthcare is finally going to change, and every few years most of those predictions land somewhere between “wildly optimistic” and “completely wrong.” So let’s be clear upfront about why 2025 might actually be different, and more importantly, why it matters for people writing checks into early-stage healthcare companies.
The Trump administration is coming in with an explicit focus on chronic disease reduction under the MAHA banner, which is not just rhetoric but appears backed by actual polling data they’ve commissioned and real policy conversations happening with transition teams. At the same time, generative AI has hit an inflection point where it can actually do useful things in healthcare workflows rather than just being a science project. The combination of policy tailwinds and technology readiness creates conditions we haven’t seen since the ACA implementation period.
What makes this interesting for angels isn’t the macro story but the specific gaps these shifts create. When Medicare Advantage plans suddenly get 200 to 400 basis points of margin relief while also facing pressure to demonstrate actual health outcomes, they need tools they don’t currently have. When CMMI launches new models focused on consumer incentives and prevention, someone has to build the infrastructure to make those models operational. When AI adoption gets pushed by federal policy but healthcare organizations have no internal capability to implement it safely, services businesses emerge to fill that gap.
The thesis here is straightforward. Nine major trends are creating specific market needs that incumbent players cannot easily address with their current capabilities. This creates wedges for startups to get initial traction, prove value in constrained contexts, and potentially scale into meaningful businesses. Some of these will be software companies, some will be service-enabled software, some will be pure services businesses that happen to use technology. The unifying thread is that they’re all solving problems that either didn’t exist before or that are suddenly urgent enough that buyers will actually pay for solutions.
The Prevention Infrastructure Play
Let’s start with what Town Hall calls the health outcomes theme. The new administration wants to shift from healthcare delivery to actual health improvement, which sounds nice but creates a massive operational problem. How do you actually measure whether someone is getting healthier? How do you structure contracts that pay for reduced disease burden rather than services rendered? How do you engage patients in behavior change at scale?
The business model here is building the infrastructure layer that enables outcomes-based contracting focused on prevention. Think of it as picks and shovels for the prevention economy. The wedge product is probably something narrow like hypertension management or diabetes prevention, where clinical pathways are well understood and outcomes can be measured relatively quickly. The initial customer is likely a Medicare Advantage plan or a large self-insured employer willing to run a pilot program.
What you’re building is a platform that does three things. First, it identifies high-risk individuals using claims data and potentially consumer data sources. Second, it delivers evidence-based interventions through some combination of digital tools, coaching, and clinical oversight. Third, it measures outcomes in a way that satisfies payer requirements and demonstrates ROI. The key innovation isn’t necessarily the clinical program, which might be pretty standard. The key innovation is making it operationally feasible to run outcomes-based contracts at scale.
The moat here comes from data and operational excellence. As you run more programs, you get better at predicting who will respond to which interventions. You build relationships with payers and prove you can actually move outcomes metrics that matter for their quality scores. You develop the operational muscle to manage complex shared savings arrangements without taking on too much risk too fast.
The obvious comp is something like Omada or Livongo in earlier eras, but the opportunity now is potentially larger because the policy environment is explicitly pushing toward this model rather than just tolerating it. The risk is that you’re building infrastructure for a payment model that might not fully materialize, or that takes longer to scale than your runway allows.
From an angel investment perspective, the diligence questions are about clinical evidence, payer relationships, and unit economics. Can they actually move the outcome metric they claim to target? Do they have a path to a first contract, and is that contract structured in a way that lets them learn without betting the company? What does customer acquisition cost look like in a world where you’re selling to health plans rather than consumers?
Consumer Engagement as a Service
The second trend Town Hall identifies is around consumer incentives and prevention, which overlaps with the first theme but has a distinct business model opportunity. The specific gap here is that almost no one in healthcare is actually good at consumer engagement. Health plans are terrible at it. Providers are terrible at it. Digital health companies are often terrible at it. But generative AI plus modern consumer product thinking plus behavioral economics creates the conditions to actually be good at it.
The business here is building consumer engagement infrastructure that other healthcare organizations can white-label or integrate into their programs. You’re essentially creating a consumer engagement layer that sits on top of existing clinical programs and makes them actually work. The wedge could be something like medication adherence for chronic conditions, where the value is obvious and measurable.
What makes this viable now is that generative AI allows for personalized engagement at scale in a way that was economically impossible before. You can have what feels like a one-on-one conversation with every patient, tailored to their specific context, without hiring an army of health coaches. The technology finally matches the ambition of truly personalized health engagement.
The initial GTM is probably selling to Medicare Advantage plans or Medicaid MCOs who are under pressure to improve Stars ratings or health outcomes but lack the consumer engagement capabilities to do it themselves. You’re selling them a solution to a problem they know they have but can’t solve internally.
The challenge is that consumer engagement is hard to isolate as a value driver. If outcomes improve, is it because of your engagement platform or because of the underlying clinical program? This makes it harder to prove ROI cleanly, which means you need to either be very good at attribution or you need to be cheap enough that buyers will use you based on directional evidence rather than rigorous proof.
The moat comes from brand and data. If patients actually like using your product and it becomes the way they think about managing their health, that’s valuable. If you accumulate enough data about what engagement strategies work for which patient populations, that’s defensible IP even if the core technology is ultimately replicable.
The MA Enablement Stack
Medicare Advantage is about to get interesting again. After years of margin pressure, Town Hall expects 200 to 400 basis points of margin relief from a combination of risk adjustment changes, rate increases, and modified quality measures. At the same time, MA plans are going to face pressure to demonstrate they’re actually improving health outcomes rather than just cherry-picking healthy seniors.
This creates opportunity for software and services that help MA plans operate more efficiently or perform better on the metrics that matter. The specific gaps are around risk adjustment accuracy, quality measure performance, and care coordination for high-need populations.
One business model is building better risk adjustment software. Despite how much money flows through HCC coding, the tools are still pretty clunky and the process is still pretty manual. There’s room for AI-powered ambient documentation and coding assistance that captures more accurate risk scores without requiring physicians to do extra work. The wedge is probably selling to small to mid-size MA plans who don’t have sophisticated in-house capabilities.
Another model is quality measure optimization software. Stars ratings determine a huge amount of MA economics, but many plans still manage quality performance through spreadsheets and manual processes. Building a system that identifies gaps in care, prioritizes interventions, and closes gaps efficiently could be a meaningful business. The key is making it operationally easy for plans to improve their scores rather than just giving them better reporting.
A third model is care coordination infrastructure for specific high-cost populations. As MA plans expand, they’re taking on more complex patients but often lack the clinical programs to manage them well. Building specialized care management platforms for conditions like heart failure or COPD, sold as a service to MA plans, could capture value.
The challenge with all of these is that you’re selling into a relatively concentrated buyer universe with long sales cycles. MA plans are not exactly known for moving quickly or taking risks on unproven vendors. Your initial traction will likely come from smaller plans or regional players, which means you need to have a clear path to either expanding within those customers or eventually cracking into the national plans.
The moat is probably switching costs and integration depth. If you become embedded in their core operations and they start relying on your system for critical workflows, that’s defensible. If you’re just a nice-to-have analytics dashboard, you’re vulnerable.
Pharmacy Transparency and Alternative Models
Pharmacy costs are under intense scrutiny, and Town Hall expects meaningful changes around international reference pricing, 340B reform, and PBM transparency. This creates opportunities for companies that help healthcare organizations navigate a changing pharmacy landscape or build alternative models for drug access and reimbursement.
One opportunity is building decision support tools for payers and providers around formulary management and drug cost optimization. As pricing becomes more transparent and potentially benchmarked to international standards, there’s value in systems that help organizations make better decisions about which drugs to cover and how to structure their pharmacy benefits. This is not a sexy business but it’s a real problem that will get more acute as pricing models change.
Another model is building infrastructure for value-based pharma contracts. If outcomes-based contracting becomes more common in pharma, someone needs to build the data pipes and measurement systems to make those contracts operational. This is a narrow wedge but could expand as pharma companies look for ways to demonstrate value in a more cost-constrained environment.
A more contrarian bet is building alternative distribution models for specialty drugs that bypass traditional PBM economics. If PBM margins get squeezed by transparency requirements, there’s potential for new intermediaries that connect manufacturers directly to providers or patients with clearer pricing and better service. This is hard because you’re fighting established relationships and rebate structures, but disruption often happens when incumbents are under regulatory pressure.
The diligence questions are about regulatory risk and buyer sophistication. Pharmacy policy is complex and changes can have weird second-order effects. You need to be confident the team understands the regulatory environment and can adapt as it shifts. You also need to understand whether buyers are sophisticated enough to actually value transparency, or whether they’re so locked into existing PBM relationships that they won’t switch even if better options emerge.
CMMI Model Operators
CMMI is about to get a refresh, with the likely elimination of underperforming models and the launch of new initiatives focused on prevention, chronic care management, and price transparency. This creates opportunity for companies that can operate CMMI models effectively, essentially becoming the infrastructure layer that makes new payment models work in practice.
The business model is being a specialized operator that participates in CMMI models on behalf of providers who don’t want to take on the complexity themselves. You’re taking on some risk, managing the operational details, and splitting the upside with provider partners. Think of it as a platform for value-based care participation, but specifically focused on CMMI programs.
The wedge is probably picking one specific model that you think will scale and building the operational capabilities to execute it really well. If CMMI launches a new chronic care management model with consumer incentives, you become the company that providers partner with to participate in that model. You handle patient identification, intervention delivery, outcomes tracking, and financial reconciliation.
What makes this viable is that most providers have neither the capital nor the expertise to participate effectively in new payment models. They know fee-for-service, and maybe they have some experience with Medicare Shared Savings Program, but they’re not set up to run complex population health programs. By being the specialist operator, you can aggregate enough volume across multiple provider partners to make the economics work.
The risk is that you’re betting heavily on specific CMMI models actually launching and achieving scale. CMMI has a history of models that get announced, run for a few years with limited participation, and then quietly wind down. You need to be right about which models will actually matter and attract meaningful participation.
The moat is operational excellence and network effects. If you can actually deliver savings and improve outcomes consistently, provider partners will want to work with you. As you add more partners, you get more data and better ability to predict what works. Eventually you might build enough of a network that being part of your platform becomes the default way to participate in CMMI models.
AI Implementation Partners
Healthcare organizations know they need to adopt AI but most have no idea how to do it safely and effectively. This creates a massive services opportunity for companies that can help health systems, payers, and digital health companies implement AI in high-value workflows without creating compliance or quality issues.
The business model is part consulting, part software, part managed services. You come in and assess which workflows are good candidates for AI augmentation, help build or integrate the appropriate tools, ensure they meet security and compliance requirements, and potentially stick around to manage ongoing performance and improvement.
The wedge is probably focusing on one specific workflow category where value is obvious and implementation is relatively contained. Prior authorization is an obvious candidate because it’s universally hated, highly manual, and a clear cost center. Clinical documentation is another good target because physicians spend enormous amounts of time on it and AI can materially reduce that burden.
What makes this a real business rather than just consulting is that you’re building reusable IP around implementation patterns, compliance frameworks, and performance monitoring. The first few implementations are mostly services, but over time you’re extracting repeatable processes into software and playbooks that let you scale.
The obvious objection is that this is not a venture-backable business model because it looks too much like consulting. The counter is that during technology transitions, there’s often a window where services businesses that help with adoption can scale quickly and eventually evolve into software companies. Epic didn’t start as a pure software business either.
The other opportunity in this category is building infrastructure specifically designed for healthcare AI deployment. Town Hall mentions Qualified Health as an example of creating a secure environment for AI in healthcare. The business model is being the platform that healthcare organizations use to deploy AI applications safely, handling all the security, compliance, and governance issues that make healthcare AI harder than AI in other industries.
Dual-Eligible Integration Platforms
Dual-eligible populations represent over 12 million people who are simultaneously covered by Medicare and Medicaid, often with complex needs and fragmented care. Town Hall expects policy push toward integrated care models for duals, which creates opportunity for companies that can help plans and providers actually coordinate care for this population effectively.
The business model is building a care coordination platform specifically designed for dual-eligibles. You’re solving the operational problem of managing patients who have multiple payers, complex social needs, and often behavioral health issues. The platform needs to integrate data from both Medicare and Medicaid sources, coordinate between different provider types, and manage services that span medical and non-medical interventions.
The wedge is probably partnering with Medicaid MCOs or D-SNP plans who are trying to improve their integrated care capabilities. You’re selling them a solution to a problem that’s about to become more urgent as policy pushes toward integration and potentially limits Lookalike MA plans that avoid full integration.
What makes this hard is that dual-eligible populations are genuinely complex to serve, with high needs and often unstable housing or social situations. You can’t just build a standard care coordination platform and expect it to work. You need to understand the specific workflows, the interplay between medical and social services, and the nuances of how Medicare and Medicaid coverage interact.
The opportunity is that there are not many companies that do this well, and the ones that do exist like Cityblock tend to be full-stack provider models rather than pure platforms. There’s potentially room for software and services that enable other organizations to serve duals effectively without having to build all the capabilities in-house.
The moat comes from domain expertise and data integration. If you can actually make sense of fragmented data sources and turn them into actionable care coordination workflows, that’s valuable and hard to replicate. If you build relationships with state Medicaid programs and understand their specific requirements, that’s also defensible.
ICHRA Infrastructure Bets
ICHRAs allow employers to give employees money to buy individual coverage rather than offering group plans. Town Hall expects slow but steady growth in ICHRA adoption, particularly among small and mid-size employers. This creates infrastructure opportunities, though the market timing is uncertain given potential changes to ACA subsidies.
The business model is building the technology and services layer that makes ICHRAs actually work. Employers need tools to administer the reimbursement arrangements, employees need decision support to choose appropriate individual coverage, and someone needs to handle the compliance and regulatory requirements.
This is not a new opportunity. Private exchanges and benefits administration platforms have been trying to crack this for years with limited success. What might be different now is if regulatory changes make ICHRAs more attractive or if ACA subsidy changes push more people toward individual coverage.
The honest assessment is that this is a lower-conviction opportunity compared to others on this list. ICHRA adoption has been slower than advocates hoped, and it’s not clear that the underlying dynamics have changed enough to create a venture-scale opportunity. If you’re going to bet here, you probably want to build something that’s relevant whether or not ICHRAs take off, rather than betting purely on regulatory-driven adoption.
The more interesting version of this opportunity is building tools that help individuals navigate health insurance generally, whether through ICHRAs, Marketplace coverage, or traditional employer plans. If you can crack consumer-friendly insurance decision support and enrollment, that’s valuable across multiple contexts and not dependent on a single regulatory bet.
Reconciliation Arbitrage Opportunities
Congressional reconciliation is going to put every healthcare spending category under scrutiny, with pressure to reduce costs while extending tax cuts. Town Hall expects proposals around Medicaid work requirements, site-neutral payments, 340B transparency, and various other cost-containment measures. For entrepreneurs and investors, this creates opportunities to benefit from specific policy changes or to help healthcare organizations adapt to new requirements.
The challenge with reconciliation opportunities is that they’re inherently speculative. You’re betting on specific policy changes that may or may not happen, and even if they do happen, they may be implemented differently than expected. This makes it hard to build a company purely around reconciliation arbitrage.
That said, there are a few potentially interesting bets. If site-neutral payments become reality, there’s opportunity in helping outpatient providers compete more effectively with hospital outpatient departments by offering comparable quality at lower cost. If 340B gets reformed, there’s potential in building better systems for covered entities to demonstrate community benefit and comply with new requirements.
The more general opportunity is building businesses that help healthcare organizations become more efficient regardless of specific policy changes. If the overall direction is cost containment and greater accountability for outcomes, companies that help reduce waste and improve operational efficiency should find willing buyers even if specific reconciliation provisions don’t materialize.
From an angel investment perspective, reconciliation bets are probably too speculative as standalone investments but might be attractive as optionality on top of businesses that make sense even without policy tailwinds. If you’re investing in a company that helps hospitals optimize their cost structure, the potential for site-neutral payment requirements is a nice bonus but shouldn’t be the primary thesis.
Conclusion: Picking Your Wave
The meta lesson from these nine trends is that meaningful business opportunities in healthcare usually emerge when there’s both a policy tailwind and a technology enabler converging to create urgency around a previously unsolved problem. The MAHA focus on outcomes creates urgency around prevention infrastructure. MA margin relief plus outcomes pressure creates urgency around quality performance tools. AI policy push plus technology maturity creates urgency around safe implementation services.
For angel investors, the framework for evaluating opportunities should probably include these questions. First, is the problem newly urgent or just newly interesting? Healthcare is full of problems that have existed forever but that buyers have learned to live with. You want problems that are becoming urgent enough that organizations will actually allocate budget and management attention to solve them.
Second, does the startup have a clear wedge into the market that doesn’t require buyers to make huge bets or change their entire operating model? The most successful healthcare startups usually start narrow and expand from there rather than trying to transform everything at once.
Third, is there a path to a defensible moat that’s not just about being first? Network effects, data advantages, and switching costs all count. Being first to market in healthcare often matters less than you’d think because adoption is slow and fast followers can catch up if the moat is weak.
Fourth, does the team actually understand the operational realities of healthcare or are they optimizing for a theoretical market that doesn’t match how healthcare actually works? The best healthcare entrepreneurs usually have some combination of domain expertise and technology capability rather than being pure technologists trying to fix healthcare from first principles.
The nine opportunities outlined here are not exhaustive and they’re certainly not guaranteed. Policy could shift in unexpected ways, technology could disappoint, or incumbent players could prove more adaptable than expected. But for investors looking to deploy capital into early-stage healthcare companies over the next few years, these trends provide a useful framework for thinking about where genuine market gaps are likely to emerge and where startups might have windows of opportunity to build defensible businesses.
The broader pattern is that healthcare transitions create opportunities for new entrants because they introduce complexity that incumbents struggle to navigate and problems that existing solutions weren’t designed to solve. The organizations that built their businesses around fee-for-service Medicare and commercial insurance are not naturally equipped to succeed in a world focused on outcomes, prevention, and consumer engagement. That gap is where startups live, at least until incumbents catch up through acquisition or internal development.
For entrepreneurs, the actionable advice is to pick one specific wedge where you have insight into both the problem and the customer, build something that solves that narrow problem really well, and then use that foothold to expand into adjacent opportunities. Trying to solve everything at once or betting entirely on macro trends without a specific go-to-market plan is a recipe for running out of money before finding traction.
The next few years should be fertile ground for healthcare company creation, but success will require navigating complexity rather than just riding policy tailwinds. The companies that win will be the ones that combine genuine healthcare domain expertise with strong execution and a clear understanding of how to create value for specific customers in specific contexts. That’s always been true in healthcare, but it’s especially true during transition periods when the old playbooks don’t quite work and the new playbooks haven’t been written yet.
