Veeraj Shah

OpenEvidence went from a $1B to $3.5B valuation in 5 months. Last week, Doximity bought their competitor Pathways for $63M. OpenEvidence is positioning itself as the single source of truth for medical decisions, basically ChatGPT meets UpToDate. They've locked down partnerships with 13 major scientific journals (including JAMA) and claim 40% of US doctors already use their platform. Doximity—once "LinkedIn for doctors," now much more—just acquired Pathways for $63M ($26M cash, $37M equity). Pathways is essentially an OpenEvidence clone, and according to Kevin from Health Tech Nerds, they're generating maybe $1-2M ARR. That's a 30x revenue multiple. Doximity already has "Doximity GPT" for summarizing charts and reports. So Pathways fits their strategy. But what's amusing is that, as Brendan Keeler reported, OpenEvidence sued both Pathways AND Doximity for allegedly "stealing" their system prompt. So what happened is that Doximity realized they can't beat OpenEvidence, so they bought the company that also can't beat OpenEvidence (see visual), betting that their distribution moat can overcome OpenEvidence's first-mover advantage in medical decision support. Now we have two distribution powerhouses on a collision course. OpenEvidence owns 40% of physicians through product/marketing superiority. Doximity owns massive reach through platform dominance. Both want to capture the critical moment when doctors need answers. In Belgium we say "a cat in trouble makes weird jumps," but maybe this cat knows exactly where it's landing. PS: The above was co-written with Blake Madden from Hospitalogy, go subscribe as it’s my fav newsletter ❤️

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SCOOP: OpenAI wants to start competing in healthcare with its own products — and the AI giant just made 2 new hires to lead the push. Nate Gross, MD, the cofounder and former chief strategy officer of Doximity, joined OpenAI in June to lead its healthcare go-to-market strategy. Ashley Alexander, former co-head of product at Instagram, starts today in her new role as OpenAI's VP of health products. OpenAI has historically stuck to powering other healthcare companies' products and partnering on medical AI research. Now, however, it wants to sell its own health tech. It's part of a larger strategic pivot for OpenAI: the ~$500B company wants to own not just foundation models, but also the infrastructure and AI applications built on top of them, across verticals from education to healthcare. My latest in Business Insider: https://lnkd.in/e7Uw8Gy6

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Healthcare has a data problem. And AI is arriving at exactly the right time. Despite being one of the world’s largest data-generating industries, so much of that potential remains unrealized: Researchers devote years to advancing science, yet much of the data remains underutilized. Health system operators are under constant pressure to deliver better care at lower cost, but without accessible insights. Drug developers invest heavily into new therapies, but patient data and claims remain scattered. And the list goes on. I see AI as the catalyst: connecting silos, unlocking insights, and enabling real-time decisions, and more – all in service of better science, better care, and better therapies. Do I see AI replacing human effort in healthcare and research? No, the potential of AI isn’t about substitution. It’s about amplification. It’s about partnership. Do I see AI as a force for good? Yes, I believe AI can be one of the most transformative forces for good in medicine and science - not just by making systems more efficient, but by helping people live healthier lives. We have plenty of ground to cover, but the momentum is building. These are the questions I’ve wrestled with as a surgeon, professor, patient, and now founder at Medeloop. They remain at the heart of the work we’re doing.

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We are now starting Q4 2025 and quarter after quarter, category-shaping events show that HealthTech is maturing.... Q3 2025 was no exception. I wrote down a few thoughts. 1️⃣ 𝐑𝐚𝐝𝐢𝐨𝐥𝐨𝐠𝐲 𝐀𝐈: 𝐭𝐡𝐞 𝐛𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐟𝐨𝐫 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐀𝐈 About 80% of FDA-approved algorithms are in radiology, making it the most advanced (and most widely adopted) clinical AI category so far (even as medical chatbots like OpenEvidence are catching up). Yet, there’s no clear winner. Will success go to: ◾Aggregator platforms like Deep, which bundle multiple AI tools? ◾Specialists developing full-spectrum algorithms, like Gleamer? ◾Combined approaches doing both aggregator and algorithms like AIDOC? ◾Next-gen foundation models in radiology, such as Raidium? ◾Or incumbents (RIS and PACS vendors) using targeted M&A to stay on top? Icometrix chose the safer path, selling to GE. 2️⃣ 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈: 𝐧𝐞𝐰 𝐰𝐨𝐫𝐥𝐝 𝐯𝐬. 𝐨𝐥𝐝 𝐰𝐨𝐫𝐥𝐝 This is where the fiercest battle is happening. Software entered healthcare 20+ years ago, with Epic landing its first big deal in 2003 with Kaiser Permanente. Now AI is eating software... and incumbents are fighting. New AI-native operating systems are emerging, combining scribing, billing, clinical decision support, and workflow (like Commure in the U.S.). Incumbents like Epic and Doximity are trying to suppress challengers, while Cerner has launched a brand-new agentic AI EHR built from scratch. The battle is on. 3️⃣ 𝐁𝐢𝐠 𝐓𝐞𝐜𝐡 𝐡𝐚𝐬𝐧’𝐭 𝐥𝐞𝐟𝐭 𝐭𝐡𝐞 𝐫𝐚𝐜𝐞 Despite setbacks, Big Tech is still pushing hard. OpenAI: With OpenEvidence’s traction and U.S. healthcare making up ~20% of GDP, OpenAI sees a big opportunity. Microsoft: The most active so far, with cloud, data tools, Nuance/Dragon, and now bold clinical claims. In June 2025, Microsoft said its AI outperformed doctors on complex cases, scoring 85.5% vs. ~20% for a panel of 21 physicians. Apple: Trying again with its new hypertension feature. Tim Cook once said Apple’s biggest contribution to humanity would be in health..... we’re still waiting. 4️⃣ 𝐎𝐧 𝐭𝐡𝐞 𝐝𝐚𝐫𝐤𝐞𝐫 𝐬𝐢𝐝𝐞, 𝐦𝐚𝐧𝐲 𝐕𝐂𝐬 𝐚𝐫𝐞 𝐬𝐭𝐢𝐥𝐥 𝐰𝐨𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐡𝐨𝐰 𝐭𝐡𝐞𝐲’𝐥𝐥 𝐫𝐞𝐭𝐮𝐫𝐧 𝐭𝐡𝐞𝐢𝐫 𝐟𝐮𝐧𝐝𝐬. For SaaS point solutions in #healthcare, life is tough. They’re caught in the middle: squeezed between incumbents with outdated tech that won’t integrate their tools, and a new wave of agentic AI platforms that bundle multiple SaaS functions into a single agent. Still, some #SaaS products bring valuable technology. Expect more M&A in the months ahead. ----- Follow HGM Advisory for daily reposts of the most relevant HealthTech news #ai

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🌐 HealthTech is more than Digital Health Apps When most people hear HealthTech, they immediately think of digital health apps, wearables, or AI platforms. These are important, but they’re only one part of the full spectrum. HealthTech actually spans across: 🩺 MedTech: medical devices, diagnostics, implants, and software as a medical device (SaMD) 💊 BioTech & Pharma: drug development, biologics, advanced therapies 💻 Digital Health: software, data, connected care solutions (sometimes medical devices, sometimes not) 🌱 Consumer Health: wellness, prevention, and lifestyle tools that sit outside the hospital These domains often overlap. Digital health becomes MedTech (SaMD) once claims cross into diagnosis or treatment. Consumer health can evolve into regulated devices. BioTech increasingly relies on device-based delivery - but often translates directly into pharmaceutical products (drugs/therapeutics) We are also seeing more "BioTools" emerge. Often first considered medical devices, but when used in complex treatments, e.g. gene therapy or similar, they become a part of a different product category 🧬 👉 HealthTech is not just digital. It’s an interconnected ecosystem, and understanding where a company sits on this spectrum is crucial for founders, investors, and regulators alike. #HealthTech #MedTech #DigitalHealth #BioTech #ConsumerHealth #MedicalDevices

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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.

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Innovaccer bought Story. We will see more headlines like this in 2026. The last year of healthtech M&A has not involved Enterprise Payors, Payviders, or Big Tech: It's late-stage VC-backed companies acquiring earlier-stage startups. And the trend will likely continue. It's the anti-ZIRP playbook: build distribution first, then tuck in specialized tech, data, or care pathways. 7 recent examples: • Innovaccer (Series F) <> Story → Extending patient engagement + care management to deepen data platform utility. (Story was Series B, Raised ~$42M) • Cohere (Series D) <> ZignaAI → AI-driven utilization management meets automation to speed prior auth. (ZignaAI was Seed-stage, Raised < $10M). • Lyra (Series E) <> Bend → Mental health leader adds precision coaching + chronic care support. (Bend was Series A, Raised ~$32M). • CapitalRx (Series D) <> Amino → Scaling beyond pharmacy benefits into member navigation + data insights. (Amino was Series C, Raised ~$45M). • Datavant/Ciox <> Aetion → Linking data connectivity with real-world evidence for life sciences. (Aetion was Series C, Raised ~$212M). • H1 (Series C+) <> Ribbon → Combining provider data graphs with API-first directory infrastructure. (Ribbon was Series B, Raised ~$54M). • Commure (Series F) <> Memora → Embedding intelligent patient workflows into the operating system for healthcare delivery. (Memora was Series B, Raised ~$80M). Q4 of '25 or '26 predictions: We'll see more of this as late-stage companies tell a story about getting to profitability, strategic acqui-hires, revenue/contract expansion, and AI-proof their businesses.

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AI in healthcare: if you want adoption, start with the workflow that’s already breaking—not the model you’d love to train. Over the past year, the most credible signals we’ve seen at seed come from teams that: - pick a single, painful clinical task (not “reinvent radiology,” but e.g., “reduce time-to-lesion-tracking in MS follow-up”); - co-design with clinicians until the product saves minutes per exam every time; wire themselves into hospital data pipes early (governance + interoperability before the sales deck); - quantify value in operational terms (fewer repeats, faster discharge notes, shorter queues), then translate that into payor/provider ROI. One portfolio example: Raidium. Co-founded by Paul Herent, M.D., Msc and Pierre Manceron, the team is building an AI-native radiology viewer and focusing on concrete biomarkers that are slow and error-prone to read manually. What stood out wasn’t the model—it was the clinical scoping and the integration plan with reference centers. That’s what gets you from demo to deployment. For investors, this changes how we diligence: less model-benchmark theater, more data plumbing, consent pathways, and clinician time-saved. For founders: own one use case end-to-end, earn trust, then expand. Anne-Sophie Saint-Martin Pierre Frouin Constant Beroulle Henri Deshays Jean-David Zeitoun #AI #healthcare #breakthrough #data

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