Assessing the Financial Viability of Healthcare Innovations

I've uncovered why so many promising health tech solutions never make it past the pilot stage. The problem isn't value demonstration—it's a fundamental misunderstanding of how healthcare budgets actually work. Here's what most founders get wrong: They calculate TAM by multiplying market size by assumed spend per customer, imagining there's some magical "innovation fund" waiting to be tapped. In reality, healthcare budgets are rigid structures where every dollar is already allocated, constrained by regulations, labor contracts, and mandatory expenses. Take a typical 500-bed hospital with $1.2B in revenue. While that sounds like massive potential, labor consumes $720M, supplies another $300M, and facilities $60M. The IT budget of $72M? Most goes to EHR licensing, cybersecurity, and maintaining legacy systems. The actual discretionary innovation budget might be just $10-15M—and it's fiercely contested by dozens of internal projects. The math gets even tighter for other players. A physician group generating $60M might have only $100K truly discretionary. A TPA with $12M revenue could have less than $500K available for innovation. Even large national insurers, despite billions in premium revenue, face medical loss ratio requirements that severely limit their administrative spending flexibility. Looking ahead to 2030, the situation will likely worsen. Labor costs are projected to hit 65-70% of hospital budgets, specialty drug spending will consume 70% of pharmacy budgets, and regulatory compliance costs continue rising. In my worst-case scenario modeling, discretionary budgets could shrink to less than 0.5% of revenue. The entrepreneurs who succeed will be those who map their solutions to non-discretionary budget lines—cybersecurity, compliance, labor cost reduction, or direct replacements for existing spend. They'll price within existing constraints rather than requiring new budget allocation, and they'll time their sales cycles to customer budget calendars. This isn't about building worse products or lowering prices arbitrarily. It's about budget fluency—understanding that in healthcare, the money flows through very specific channels, and your innovation needs to align with those channels to reach scale. ____________ Disclaimer: These thoughts and opinions are my own and do not reflect those of my employer or any other entities. ____________ The full analysis, including detailed mock budgets and scenario modeling through 2030, is linked in the comments below.

🏥 Why Most Healthcare AI Projects Fail to Deliver ROI (And How to Fix It) AI is being implemented across healthcare systems, yet few initiatives move beyond pilot projects. Even fewer deliver measurable value. The problem is not the algorithm. It is that the ROI narrative is unclear or poorly defined. Using the McKinsey Five Frames of Transformation, and one more I believe is essential, here is a reframing of how leaders should evaluate AI investment in healthcare. 🎯 1. Aspire – What is our bold ambition? Most AI efforts begin with small goals like reducing paperwork or saving a few minutes. But true aspiration speaks to leadership priorities: ✅ Expand clinical capacity ✅ Improve outcomes tied to reimbursement ✅ Create meaningful, sustainable value at scale If your AI vision does not resonate with the CFO or COO, the bar may be set too low. 🔍 2. Assess – Where are we starting from? Too many AI projects assume readiness without evaluating the context. Do we have the right data quality? Are workflows integrated? Is there a path to reimbursement? From my experience with diabetic foot screening innovations, technical feasibility was not the main barrier — operational alignment was. 🏗️ 3. Architect – How will we design for scale? AI must be embedded within the healthcare system, not layered on top of it. Effective architecture requires: Data pipelines Clinician co-design Payment mechanisms Risk and governance structures Without the right foundation, even the best models will fail to deliver impact. 🚀 4. Act – How will we execute? A successful pilot is not success unless it is implemented and scaled. Execution requires: End-user training Workflow integration Real-time feedback Clear KPIs What matters is not just the model's accuracy, but how it performs in real-world care delivery. 📈 5. Advance – How will we sustain and expand results? AI success must lead to repeatable, expanding gains. Use dashboards, outcome audits, and standardised playbooks to: Track value over time Learn and adapt Replicate success across teams and regions The goal is not a prototype. The goal is a repeatable platform for transformation. 📣 6. Advocate – How will we communicate the value? AI projects fail when leaders cannot explain the value clearly. Do not say: “The model had 94 percent accuracy.” Say: “This reduced unnecessary hospital visits by 18 percent and saved $500,000 this quarter.” Frame your results in terms that resonate with boards, payers, and clinicians. ✅Follow me for frameworks, reflections, and lessons that move ideas into implementation. 🔄Share this with colleagues who may benefit from more clarity on healthcare AI ROI. #HealthcareAI #ValueBasedCare #DigitalHealth #HealthEconomics #McKinsey5Frames #HealthTechStrategy #AIImplementation #DrGaryAng

The number one problem I see in early-stage health tech companies is too much innovation. Hear me out. These founders aren't lacking ambition; they're drowning in it. They've been schooled by the startup world that big ideas get funded, disruption is the goal, and if you're not reimagining entire systems you're thinking too small. So they design brilliant clinical solutions and revolutionary payment models and entirely new care delivery frameworks, all at once. Then they're baffled when nobody buys. Here's what's happening: They've built something that only works if healthcare's entire economic infrastructure changes first. And they're asking customers - legacy institutions with entrenched incentives and bureaucratic inertia - to take a leap of faith on both the product and the business model. That's not innovation, it's a death wish. Because the thing about healthcare economics is they're glacially slow to change. Codes take years to establish. VBC pilots move at the speed of committee approval. Reimbursement models don't shift because a startup has a compelling pitch deck; they shift because of policy, evidence, and institutional consensus. All of which take more time than venture capital will give you. And when founders push back on this, I get it. I respect it. They're setting out to change broken systems - that's the whole point. But policy reform and business viability operate on different timelines. If your business model requires the former before it can achieve the latter, you're going to run out of runway before you get there. VCs want traction yesterday. They want repeatable revenue, clear unit economics, proof that customers will pay for what you're selling. They're not going to float you through years of evidence generation and lobbying while you burn cash waiting for CMS to rewrite reimbursement policy. So the question isn't whether your vision is bold enough, it's whether you can survive long enough to implement it. The smartest founders I work with understand this and are playing a different game. Their long-term vision is revolutionary, but their short term strategies are ruthlessly pragmatic. Work within existing CPT codes. Fit into value-based contracts that already exist. Generate revenue from operational efficiencies institutions will pay to solve right now. Build evidence and trust and leverage using today's economics, not tomorrow's. Then, once they have traction, revenue, and institutional credibility, they start pushing on the bigger levers. But they do it from a position of strength, not in the midst of a burn rate crisis. Too much innovation kills companies, not because the ideas are bad, but because the sequencing is wrong. You can't change the game if you don't survive long enough to play it. Build the foundation first, then burn it down and build something better. And for the love of all that is holy, make sure you can pay your bills while you're doing it.

The toughest and most important lesson I’ve learned in healthcare is this: great medicine does not automatically make a great business. Today, when I look at any healthcare product or initiative, I do not start with clinical questions. Instead, I start with two grounding questions. 1) Who is the economic buyer? Not the end user. Not the internal champion who loves the idea. The economic buyer is the person who owns the budget and is accountable for the financial outcome. They are the one who signs the contract and answers for the spend when results are reviewed. If they do not feel the financial impact, they are not the buyer. 2) How do they personally realize the upside? This is the most important lever that defines whether the most innovation narratives break down or scale. It’s not enough to say “this saves money” or “this improves efficiency.” You have to show exactly how the benefit shows up for that buyer. For example: - “More efficient staff” → Does headcount actually go down? Are overtime hours reduced? Or do workflows just change? - “Cost savings” → Where do those savings appear in the P&L, and in what time frame? - “Better outcomes” → Who gets paid for that improvement, and through what contract, bonus, or risk arrangement? When the path from impact → dollars → decision-maker is clear, conversations move faster and decisions get easier. Because ROI isn’t theoretical, it’s behavioral. Someone has to change how they work, what they approve, or how they’re measured for the upside to materialize. If the buyer: - Doesn’t have a clear incentive. - Doesn’t directly experience the gain. - Or has to take on risk without near-term reward. Then even the best clinical innovation will stall. But when you’re crystal clear on: - Who you’re selling to. - Why do they want to buy it? - And what has to change for them to win. Innovation doesn’t just sound compelling, it becomes scalable. When you evaluate healthcare ideas, do you start with clinical impact or economic incentives?

🚨Clinical Wins Alone Not Enough in HealthTech Great Outcomes Fail without Financial Models👇 📄 In a JAMA Network Open study, evaluated all-virtual at-home acute care model. Researchers compared 876 Safer-At-Home patients to 1,590 matched controls across 10 diagnoses from September 2022 to August 2023. 🎯 Analyzing net hospital and payer costs cut hospital length of stay by 4 days with no rise in mortality or readmissions. 😯 One would imagine these outcomes would be a slam dunk for adoption. > Yet, without reimbursement, those clinical gains can’t scale. 💡 Key insights > Net hospital savings totaled $5.6 M > $4 M in lost inpatient revenue threatened viability > Medicaid/uninsured cases saved $8,380–$10,934 each  > Medicare/commercial cases lost $4,143–$25,999 each  > The program would incur an $11.2 M net loss without reimbursement 💵 Modeling shows that reimbursing 50–60% of inpatient variable costs would balance hospital and payer benefits ⚡ My takeaways > Clinical efficacy alone is insufficient > Economic alignment drives adoption > Embed early-stage ROI into program design to secure payer support > HealthTech must link clinical outcomes to clear revenue pathways > The Clinical Enterprise Value Stack unites impact with financial viability 🤙 DM to make sure your healthtech is driving true Clinical<>Enterprise Value. Citation Spellberg B et al., Health Economic Analysis of an All-Virtual, At-Home Acute Care Model, JAMA Network Open, 8(6), 2025, doi:10.1001/jamanetworkopen.2025.17114 (CC-BY License) #llm #ai #artificialintelligence #medicalai #healthcareai #genai #startups #entrepreneurships