Jason Strober

🚨 Big news from the frontlines of healthcare innovation: Ellipsis Health just announced a $45M funding round led by Salesforce Ventures, Khosla Ventures, and CVS Health Ventures—and it’s making headlines in the Wall Street Journal. We’ve been proud backers of Ellipsis Health at E12 Ventures because they’ve taken a bold, contrarian approach: instead of chasing efficiency at the cost of patient experience, they’ve built an emotionally intelligent AI that brings humanity back to healthcare. Meet Sage — the AI Care Manager trained on millions of real clinical conversations and powered by Ellipsis’ proprietary Empathy Engine. It delivers 24/7 support to patients with complex needs — the same patients who typically slip through the cracks because of capacity constraints and staffing shortages. This is a huge leap forward. Instead of forcing trade-offs between quality and scale, Sage shows we can have both. It’s not just automating workflows — it’s reimagining what scalable, empathetic care looks like. Strategic investors across the ecosystem — payers (CVS), platforms (Salesforce), and deep tech VCs (Khosla) — all recognize the same thing: emotionally intelligent AI is the next frontier in healthcare. Congrats to the Ellipsis team — thrilled to be on this journey with you. #AI #HealthcareInnovation #EllipsisHealth #EmpathyEngine #VentureCapital #DigitalHealth #E12Ventures https://lnkd.in/giVckKyq

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700 healthtech startups. One underestimated state. Colorado’s not next. It’s now. ✅ 30+ standouts across AI, virtual care, and diagnostics ✅ Over $1.2B raised in the past year alone ✅ Deep activity from Seed to Series E, IPO, and acquisition SF and NYC still get the headlines. But Colorado is where early signals become category shapers. I mapped the breakout momentum: 21 unicorns and near-unicorns, across AI, virtual care, and diagnostics. It’s how I spot where real signals are forming, and which stories are worth following early. Building your edge? Check out my newsletter for more insights 🔗https://t2m.io/72LJ9BxF

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Walking the HLTH conference floor felt like speed-dating in an AI echo chamber. Every other booth: "We use AI!" Me: "Cool. What problem does it actually solve?" Them: blinks in chatbot Look, I love a good buzzword as much as the next MBA, but let's talk about what's actually happening in hospitals right now: The brutal reality: *CFOs are still paying $118 to rework each denied claim (while payers pay $<5 to deny using automation) *89% of hospitals are drowning in escalating PA requirements *41% of RCM departments have over 50% of their positions vacant *Underpayments are bleeding millions that nobody's systematically recovering The AI theater: Most "automation solutions" do 50% of the job, create 100% of the invoice, and leave hospitals holding the bag when it doesn't actually move the needle on A/R days or denial rates. I've lost count of how many CFOs have told me: "We tried [big vendor name]. It didn't work. We're still doing everything manually." Why we're different (and why we keep every client we sign): We don't do demos that look great but fall apart in production. We don't "integrate" by making your team do the heavy lifting. We don't sell you technology and ghost. We build custom automations IN your Epic environment that actually: *Make appeals profitable (not just possible) *Fill your empty RCM seats with a digital workforce *Recover underpayments you didn't know existed *Handle PA workflows end-to-end And we only get paid when it works. Performance-based. Full stop. So yeah, HLTH was crowded with "AI" companies. But there's a reason hospitals keep finding us after the other solutions fail. If you're tired of automation theater and want to see what actually effective RCM automation looks like - the kind that makes your denial rate drop 40% in 90 days, not just look pretty in a PowerPoint - then let's talk. Natasha Schlinkert, MBA FACHE CEO, Innobot Health Recovering denied claims and sanity, one hospital at a time. #hlth2025 #LasVegas #Conference #RPA #AI #RCM #Hospitals 

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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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🧠 Digital Health Insight According to Intuition Labs, over 71 million Americans (which is about 26 percent of the population) are expected to use remote patient monitoring services in 2025. RPM is no longer a fringe solution. It represents a systemic shift in how care is delivered, especially for chronic conditions and proactive wellness. The adoption of tools like connected scales, blood pressure cuffs, and wearable sensors opens the door for more personalized, continuous care. 💬 How can we scale and optimize remote monitoring solutions to help more people stay well and out of the hospital? Source: https://lnkd.in/d2wUEU2J #DigitalHealth #RemoteMonitoring #ChronicCare #HealthTech #PatientEngagement #PreventiveCare #Wearables #CareTransformation

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TODAY's HEALTH-AI BUZZ AGENTIC AI IS REVOLUTIONIZING HEALTHCARE: FROM TOOLS TO TRUE TEAMMATES The latest in Healthcare AI Imagine an AI that not only processes data but actively solves problems, makes decisions, and learns autonomously. 🤖 That future is here, and it's set to profoundly reshape healthcare's core operating system. Here’s how agentic AI is transforming the landscape: ▶︎ Agentic AI transcends traditional AI tools, acting as autonomous agents that can plan, execute, and adapt without constant human oversight, moving beyond simple automation. ▶︎ This critical shift redefines the human-AI partnership, elevating AI from mere support to collaborative teammates across the entire healthcare spectrum, from clinics to research labs. ▶︎ Its applications are vast: optimizing complex administrative tasks like patient scheduling, hospital logistics, and supply chain management to enhance efficiency and reduce bottlenecks. ▶︎ Agentic AI promises to revolutionize patient care through automating medical coding, enabling more precise diagnostics, and crafting highly personalized treatment plans tailored to individual needs. ▶︎ Crucially, it will free up clinicians from repetitive, data-intensive tasks, allowing them to dedicate more time and focus to direct patient interaction, empathy, and complex, human-centric decision-making. ▶︎ Beyond operations, agentic AI is poised to accelerate breakthroughs in drug discovery and development, making healthcare more responsive, efficient, and innovative. This evolution signifies a fundamental transformation in how healthcare operates, moving from reactive responses to proactive, intelligent management. It promises unprecedented levels of efficiency, accuracy, and patient-centricity. We're stepping into an era where AI agents can not only identify issues but also autonomously implement solutions, fostering a more agile and effective healthcare ecosystem. This will redefine roles and demand new skills, emphasizing collaboration and ethical AI integration. As agentic AI integrates deeply into healthcare, what do you believe are the most significant opportunities and the key ethical considerations we must address? Share your insights and join the conversation! 👇 #HealthcareAI #AIinHealthcare #DigitalHealth #HealthTech #AgenticAI #Innovation #FutureofMedicine #SmartHealthcare #HealthcareTransformation We hope you liked the buzz or today Connect with us for more on health-AI #AI #healthcare #news

At HLTH USA, Sergei Polevikov, and I had the chance to talk to some of the most impressive founders and innovators in healthcare for our podcast, Digital Health Inside Out. One of them is Ryan Brotman, PhD, Founder & CEO of Compose Health — a startup that’s tackling one of the most painful (and expensive) problems in healthcare: creating compliant medical content at scale. Compose Health uses agentic AI to simulate how medical reviewers, regulatory experts, and legal teams would analyze every piece of content — ensuring it’s compliant before it even leaves the platform. The result? ⚡️ 85% faster content creation 💰 Up to 90% cheaper than traditional agency workflows 📈 Used by life science and healthcare organizations to streamline everything from clinical recruitment materials to medical marketing In our short HLTH interview, Ryan shared: 🧠 Why “agentic AI” is the next frontier beyond standard generative AI 🏥 How legacy workflows have slowed innovation in pharma and life sciences 🚀 What he learned going through Techstars AI Health Baltimore powered by Johns Hopkins University and CareFirst BlueCross BlueShield 💡 And why sometimes the best fundraising strategy is knowing when not to raise Thanks to Nick Culbertson for introducing us. 🎥 Watch the full interview below — and keep an eye on Compose Health, because this might be one of the most practical AI applications in the entire digital health space. https://lnkd.in/dN3_Mhni #HLTH2025 #DigitalHealth #AIinHealthcare #HealthTech #MedTech #LifeSciences #ComposeHealth #HealthcareInnovation #AgenticAI #DigitalHealthInsideOut

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The biggest barrier to innovation in healthcare isn’t technology. Healthcare systems today are surrounded by promising startups. Yet many still struggle to work with them. Across the health innovation ecosystem, the same pattern keeps appearing. The bottleneck isn’t capital. And it isn’t even technology. It’s the internal infrastructure required to engage with innovation. Research from Rock Health shows that many digital health solutions remain stuck in pilot phases, as health systems struggle to scale external innovation. Many startups encounter what industry leaders call “pilot purgatory”: solutions are tested, sometimes successfully, but rarely scaled across the organization. At the same time, a Health Tech Alliance (2025) report highlights that complex procurement processes and fragmented decision-making remain major barriers to adopting new health technologies. In practice, a single procurement cycle at a large hospital or pharma company can easily outlast a startup’s runway. By the time IT, legal, compliance, finance, and clinical teams align, the startup may have already pivoted, raised funding under new terms, or shut down. The result? Healthcare organizations invest heavily in innovation, but too many initiatives fail. Not because the solutions are weak. But because the system isn’t designed to absorb external innovation. The leading organizations will be the ones building the internal capability to identify, test, and scale external innovatio. At the speed startups actually operate. This is not primarily a technology problem. It’s a leadership, governance, and organizational design challenge. 📖 Sources • Rock Health — Digital Health Market research - 🔗 https://lnkd.in/eeFaMARe • MIT–Roche Report on Digital Health Adoption 🔗 https://lnkd.in/eqyiUjZy • Health Tech Alliance — Procurement Barriers to Health Technology Adoption (2025) - 🔗 https://lnkd.in/eHAN7Cqb #HealthInnovation #DigitalHealth #HealthTech #FutureOfHealthcare

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I don't fully trust GPT or any AI to work, but it is a good tool to rephrase some complex concepts. Always keep in mynd AI is not a "thinker" but a huge database with abilities to analyze information. However, at this point, it cannot be used as a source of information; it is still a tool.

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A Revolutionary approach to Care For most of modern history, healthcare was built on trust. Families knew their doctor. Doctors knew their patients. Care was personal, preventative, and grounded in a relationship. Somewhere along the way, that broke. Today, the United States spends nearly $5 trillion a year on healthcare, yet more than 100 million Americans don’t have a primary care doctor, according to U.S. Department of Health and Human Services (HHS). Even the World Health Organization estimates that roughly half the global population lacks access to essential health services. We have unprecedented data, science, and technology, but care is still fragmented, reactive, and expensive. That is not a lack of innovation. It is a failure of structure and incentives. When technology truly changes an industry, it does not just add efficiency. It realigns incentives, collapses cost, and restores trust. That is the real opportunity for AI in healthcare, not replacing clinicians, but extending their reach and making care continuous, personalized, and available when it actually matters. That is why Lotus Health AI caught our attention early on at LEAD for our Lake Nona Fund. Lotus is rebuilding primary care around a simple idea that feels almost radical today. Make world class care free, personal, and available 24/7. Their approach combines advanced medical AI with real physician oversight, creating a true medical provider that can accurately diagnose, prescribe, and refer, while keeping doctors focused on judgment, not paperwork. What makes this compelling from an investor’s perspective is not just the technology. It is the architecture. Unifying health records, wearable data, and clinical guidelines into one continuous system that works for humans, not billing codes. At scale, that kind of model does not just improve outcomes. It changes the economics. Today’s $41 million financing is an important milestone, not because of the number, but because it signals momentum toward adoption. People are ready for a better system. Employers, athletes, families, and everyday patients are all asking the same question. Why does healthcare still feel this hard? Congratulations to KJ Dhaliwal and the entire Lotus Health AI team on building something both ambitious and grounded in reality. And congratulations to LEAD, Kleiner Perkins, CRV, and the other early investors who recognized that fixing healthcare requires patience, conviction, and the willingness to rethink first principles. You deserve healthcare, not just sick care. Launch days do not fix systems, but they do make better ones possible. Onward: www.lotus.ai App Store link: https://lnkd.in/eFcmaDW9

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Clinicians want to use AI but don’t have the bandwidth to evaluate all the different models. Meet Health Olymp, an AI tool purpose-built for clinicians. Here’s why this matters 👇 1. Health and life science experts gain instant access to frontier models for free. No procurement hoops or IT bottlenecks. Every clinician gets their own sandbox to explore. 2. Clinicians can compare models side by side and pick the one that works best for their use case. That transparency builds trust and sparks ideas for novel AI applications in care delivery, diagnostics, and research. 3. Most AI benchmarks are too generic or disconnected from clinical reality. Health Olymp brings frontline feedback into the loop, helping the industry identify models that perform reliably in real world clinical environments. Congrats to Ruslan Nazarenko and the Lumos team as they put AI model benchmarking directly into the hands of healthcare professionals. ⬇️ (link in comments)

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KLAS Research recently evaluated Suki's ambient clinical AI and published a detailed report. KLAS evaluated real-world deployments of Suki’s ambient documentation across three large Epic health systems: FMOL Health, McLeod Health, and Rush University System for Health. Highlighted results: • ~27% reduction in documentation time, translating to ~3.6 hours saved per provider per month • ~$1,000+ in additional revenue per provider per month driven by improved E/M coding capture • ~$862 added revenue per user per month at another system from more complete documentation • 21% reduction in time spent on notes • 43% reduction in notes remaining open after seven days These numbers reflect gains across clinician productivity, chart closure, and revenue capture, measured using production EHR data rather than pilot metrics. This kind of validation signals where the market is heading. AI solutions are increasingly being judged on quantified time savings, financial impact and durability at scale. #AIinhealthcare #KLAS

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Benchmarking the Future of Medical AI Agents: Really interesting paper introducing the first benchmark to evaluate how large language models function as medical agents inside EHRs. Traditional LLMs (like ChatGPT) act as chatbots providing answers to prompts. But "agents can proactively interpret high-level instructions, plan actions, interact with external systems, and iteratively refine their responses." That shift could help reduce clinician burnout, improve clinical care quality, and close workforce gaps. MedAgentBench tests 300 clinically derived tasks across 100 patient profiles (700K+ data points) in a FHIR-compliant virtual EHR. State-of-the-art models showed promise but with wide variation. Claude 3.5 Sonnet v2 topped the field at a 69.7% success rate. Impressive, but still far from reliable for clinical use where errors in medical records can have serious consequences. However, if current trends hold (as METR notes, AI’s ability to complete long tasks is doubling roughly every 7 months), today’s ~70% performance could reach clinically sound thresholds much sooner than expected. Just as MedAgentBench creates a virtual EHR to test AI agents in medicine, education needs its own suite of agentic benchmarks. Imagine an assessment agent that continuously measures student progress, an SIS agent (like an educational EHR) that integrates student records, a science of reading agent that adapts instruction to phonics and fluency needs, an HQIM agent that generates and sequences high-quality instructional materials, and a coherence agent that reviews all of this to ensure alignment with learning standards and the evidence base. Benchmarks could test whether these agents work together reliably—retrieving data, planning instruction, scaffolding practice, and adjusting in real time. Without such benchmarks, we risk deploying systems that look impressive in demos but fail to deliver meaningful learning gains. https://lnkd.in/gnzCwbi7

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𝗢𝗽𝗲𝗻𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗥𝗮𝗶𝘀𝗲𝘀 $𝟮𝟬𝟬𝗠, 𝗩𝗮𝗹𝘂𝗲𝗱 𝗮𝘁 $𝟲𝗕, 𝘁𝗼 𝗔𝗱𝘃𝗮𝗻𝗰𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗖𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 AI startup OpenEvidence secured $𝟮𝟬𝟬𝗠 in Series C funding led by Google Ventures, just 3 months after its $210M Series B, bringing its valuation to $6B. The company has raised nearly $500M since its founding in 2022. OpenEvidence developed an AI-powered medical search engine and generative chatbot built exclusively for physicians. The platform summarizes and ranks evidence-based medical information from trusted sources like the American Medical Association and The New England Journal of Medicine, helping doctors access reliable research in seconds. Usage among US clinicians has surged 830% over the past year, supporting more than 16M clinical consultations per month across 10,000 hospitals and medical centers. The new funding will expand its AI research in natural language processing and computer vision, supporting development of advanced tools like DeepConsult, which generates PhD-level research summaries for doctors. The company aims to make evidence-based care faster, easier, and more accessible for clinicians nationwide.

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Things are about to get a lot harder for AI startups targeting health systems. At Epic’s UGM this week, we should expect an announcement about an Epic ambient scribe. Add this to the list of 100’s of AI projects they’ve already previewed. Picking our head up we are going to see this motion across many agentic automation use cases that touch the Epic core platform. Whenever Epic wants to move to augment their user experience with AI, they will have the built-in advantage of a compound company. These benefits include: 🛠️ Shared tools, context, and capabilities 🧩 Complete leverage over integrations 🪶 Common user experience driving adoption friction to zero 💰 Bundling the product portfolio to reduce per unit price But all is not lost for startups that want to sell to hospitals. They can move out from the Epic-adjacent wedge into financial and operational automations that Epic is less likely to build. Finding service-heavy processes and manual tasks that can be replaced with agentic automation. My belief is that startups will need to cede the space occupied by the clinical user to Epic; it is simply too close to the core customer value Epic provides, and inconsistent with their product and commercial strategy, to give up this space to any other software company in the long run. Further advancing Epic’s advantage are ready-to-go integration capabilities of state of the art models and AI clients from the leading foundation model providers. These startups will need to be agile and move quickly into adjacent spaces, accepting revenue churn as Epic comes for their initial wedge. The good news is that there are big opportunities across the many thousands of non-clinical employees in health systems. Just don’t call it a SaaS business, or fall into the trap of thinking that these tools will displace the EMR.

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Something to note from Menlo Ventures's incredible AI in healthcare report....their data confirm what many of us in health systems already feel coming: AI adoption will INCREASE administrative volume before it reduces it. Payers expect, in the next 12–36 months: 63% major jump in call volume 50% more codes per claim 40%+ higher claim volume 38% more prior auths 31% more appeals + resubmissions When documentation gets easier, volume is going to skyrocket. Implications we’re already planning against: - AI adoption doesn’t remove work inherently; it redistributes it across the ecosystem. Workflow redesign is critical. - Providers need guardrails to prevent over-generation of documentation and requests. More noise helps noone. - Startups must show cross-stakeholder ROI, not siloed productivity gains... - Health systems need a system-wide AI playbook, or every local win creates a downstream backlog. This next 2-3 years will either widen payer–provider tension or force real alignment. I'm not counting on the latter. Curious how others are preparing.

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We call this "automating brass polishing on the Titanic." You won't get any forward progress until you get rid of the thousands of little point solutions and quit documenting the patient encounter with text. Health care needs a total rewrite from the ground up. I get that you are scared, but we will help you. In fact, we have this process completed. We have ONE application that documents the patient encounter with data. It automates coverage. It prescribes patient education automatically and coming soon, an ERP style Practice/Hospital Management System (PHM) that will allow administration to run a P&L on employees, equipment, rooms, departments, wings or the entire enterprise. One Click. This will cut more than 25% from the cost of running your hospital or practice. It will also cut about half from the cost of coverage for the patient. Read this: https://lnkd.in/gSzn8wez

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Hot take: betting or investing in any niche EHR right now (e.g. AI-native EHR for X clinical specialist or X vertical like Longevity) when AI code development exists and only continues to get better with each new model release has to be dumbest pre-AI SaaS era thing an investor can do. Here's why ⤵️ The EHR market is essentially split into 5 categories: #1. Outpatient EHR-enabled private practice (doctor or clinical owned) #2. Outpatient tech-enabled practice (PE, VC, family office owned) #3. Outpatient tech-enabled practice (MBA or bootstrapped owned) #4. Startup enterprise (PE or VC backed) #5. Inpatient hospital VC backed EHRs primarily make their money selling to #2-4, with the Epic's of the world taking most if not all of 5. For #1 there really isn't any true TAM since it's difficult to acquire this customer even when giving the EHR product away for free. But here's what these EHRs are not telling you and why they're all racing to update their API docs. They hope that #2-4 will continue to use their solutions to build with AI vs exiting and vibe coding their own in-house solutions. So when you hear them say their EHR product is safe from AI, that's not true. Right now the Epics of the world are safe but it's customers in categories 1-4 who will 100% vibe code their own solutions. #VibeWithCline #LetDoctorsVibe

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