Jean Barmash

Here’s your recap of last week’s health IT news 🗞️ 👇 • In case you missed it, we here at Elion raised a $9.3M seed round.   https://lnkd.in/eWa5JEdZ   🤖 𝗔𝗜 𝗖𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 • Epic plans to announce its own ambient scribe tool later this month, according to multiple reports.    • Creyos expanded its Session Notes feature to support structured assessment protocols.    • Doximity acquired Pathway Medical in a transaction valued at $63M. 🩻 𝗔𝗜 𝗜𝗺𝗮𝗴𝗶𝗻𝗴 • iRhythm Technologies, Inc. is partnering with Lucem Health to flag elevated arrhythmia risk using clinical data and route identified patients to Zio monitoring—positioned for population health and value-based care programs. 🌡️ 𝗔𝗜 𝗥𝗲𝗺𝗼𝘁𝗲 𝗣𝗮𝘁𝗶𝗲𝗻𝘁 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗧𝗿𝗶𝗮𝗴𝗲 • Amwell received a contract extension from the Defense Health Agency to continue powering virtual care for the Military Health System, integrated with MHS GENESIS.    • OU Health partnered with Nexus Bedside to launch a hybrid nursing model that pairs virtual nurses with bedside teams; initial rollout is in neurology/neurosurgery with plans to expand. 💼 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿 𝗮𝗻𝗱 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗔𝗱𝗺𝗶𝗻𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 • Press Ganey launched an integration with UKG that integrates data across the two to automate submissions and inform staffing decisions.    • Capsa Healthcare acquired BlueBin Inc. to “enhance visibility, efficiency, and reliability across the entire clinical supply chain.” 💸 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗖𝘆𝗰𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 • Rectangle Health and ECHO Health formed a partnership focused on streamlining payer-to-provider payments for healthcare organizations. 📈 𝗗𝗮𝘁𝗮 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 • Rhapsody announced general availability of its Envoy platform for health systems, labs, and payers.    • Duke Health is partnering with Trilliant Health to “inform enterprise strategies in growth planning, network design and patient access.”    • Datavant completed its acquisition of Ontellus. ⚙️ 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 • Xsolis launched Dragonfly Navigate, an AI-powered workflow tool for discharge planning and capacity management.    • Zyter|TruCare unveiled Zyter Symphony, an agentic orchestration layer to automate enterprise workflows.    • CLEAR is “under construction” in Epic's Toolbox to streamline provider access.    • Cognizant debuted the TriZetto AI Gateway to embed governed generative-AI capabilities across the TriZetto portfolio (claims, care management, customer service).    • Elation Health announced a major AI expansion, adding native AI features (e.g., Note Assist, Actions, Clinical Insights) across its EHR and billing platform.

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Epic announced 160+ AI projects at its recent UGM conference, while rival Oracle Cerner just launched an AI-backed EHR. But can Epic or Cerner really “run care operations”? A hospital executive I spoke with (whose hospital runs Epic) was clear: “No, Epic cannot run operations.”   I wrote down a few thoughts:   1️⃣ 𝐋𝐞𝐠𝐚𝐜𝐲 𝐄𝐇𝐑𝐬 𝐰𝐞𝐫𝐞 𝐧𝐞𝐯𝐞𝐫 𝐛𝐮𝐢𝐥𝐭 𝐭𝐨 “𝐫𝐮𝐧 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬.”   Hospital EHRs began as systems of record: tools for admin tasks and billing.   Running operations is a different challenge: it’s about process optimization and automation.   EHRs were the accountants; now they’re willing to become COOs.   2️⃣ 𝐍𝐞𝐰 𝐂𝐚𝐫𝐞𝐎𝐩𝐬 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐚𝐫𝐞 𝐞𝐦𝐞𝐫𝐠𝐢𝐧𝐠.   Provider to provider: Sword Health, a leading digital & AI rehab scale-up from Portugal, launched Sword Intelligence, sharing the tools it built to optimize its own operations (see visual)   Oil & gas to #healthcare: DNV Imatis, a CareOps player from Norway, has its roots in the oil and gas industry. The company originally optimized processes to transport oil from offshore platforms in the North Sea to continental Europe. A physician on its board pointed out that similar process optimization was urgently needed in hospitals, leading DNV-Imatis to expand into healthcare more than 10 years ago.   Airports to hospitals: Singapore’s ST Engineering, originally managing airport flows, was brought into hospitals during COVID to improve patient flow.   3️⃣ 𝐓𝐡𝐞 𝐂𝐚𝐫𝐞𝐎𝐩𝐬 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐜𝐚𝐬𝐞 𝐢𝐬 𝐜𝐨𝐦𝐩𝐞𝐥𝐥𝐢𝐧𝐠.   ST Engineering: A 600-bed hospital typically spends ~$6M/year on 60 senior nurses to manage operations. With ST Engineering’s 300+ standardized processes and command center model, the same work can be done by 3 non-clinical staff for ~$150k/year (and much better results)   DNV-Imatis: Clients report 20% productivity gains through automation and better resource use. Sword Health, the digital rehab scale-up, delivers 3.7× ROI per member per year in MSK care with its AI Agents. Key drivers: ◾14× clinician capacity ◾74% lower session cost ◾71% less clinical workload Now spinning off its #CareOps tech into Sword Intelligence, powered by AI Care Manager Agents: ◾Missed calls cut from 40% → 0% (24/7 coverage) ◾84% of patients scheduled instantly (eligibility auto-verified) ◾$100k new monthly revenue in 4 weeks 4️⃣ 𝐓𝐡𝐞 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐞𝐧𝐚𝐛𝐥𝐞𝐫: 𝐨𝐩𝐞𝐧, 𝐦𝐨𝐝𝐮𝐥𝐚𝐫 𝐭𝐞𝐜𝐡 𝐬𝐭𝐚𝐜𝐤𝐬.   Legacy EHRs aim to vertically integrate everything: from databases to CareOps to care pathways.   But advances in interoperability and AI now allow hospitals to build open, modular stacks.   Unfortunately, most hospital IT teams are underfunded and tasked with maintenance, not transformation.   To shift, budgets would need to rise from ~2% to 10%+ of hospital spend, and hiring would need to shift from traditional IT to AI and tech talent. --- Follow HGM Advisory for daily reposts of the most relevant #HealthTech news

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Big news in healthcare AI—Google just dropped MedGemma and MedSigLIP to the public, and they’re seriously impressive. These models can analyze chest X-rays, medical images, and even patient histories like a digital second opinion. What’s wild is that they don’t just understand text—they can actually see. The larger model, MedGemma 27B, takes in both images and clinical records, and it nailed an 87.7% score on MedQA, which is one of the toughest benchmarks in medical AI. Even the smaller model held its own—U.S. radiologists rated it at 81% accuracy. That’s not just research-grade; it’s actually usable in real patient care. Now here’s the part that hits home for me: I grew up on open-source—Linux distros, GNU apps, the whole ecosystem. So it’s amazing to see Google release these models freely. No paywalls. No cloud-only access. You can download them, tweak them, and run them locally. That’s a huge win for hospitals, researchers, and devs all over the world. It feels like we’re at the start of a new wave—one where powerful AI in healthcare isn’t just for the few with deep pockets, but for anyone who wants to build and improve on it. 🦾 #RoboSapiens #AI #MedTech #ArtificialIntelligence #MachineLearning

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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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We’ve solved harder technical problems than adoption. In healthcare, the engineering behind AI is rarely the bottleneck. We can fine-tune models, train on multimodal data, even deploy agentic AI that adapts and learns in real time. 🤔 The real hurdle? Scaling belief. I’ve sat in boardrooms where AI projects had the right architecture, governance, and infrastructure — yet stalled because the people using them didn’t see themselves in the story. A physician might hear “automated utilization review” and think “more bureaucracy.” A patient might see predictive analytics as surveillance, not protection. The most advanced AI still depends on a human operating system. And that OS is powered by narrative: • For clinicians: stories that show how AI reduces cognitive load and restores time with patients. • For executives: stories that connect algorithmic precision to measurable outcomes and sustainable margins. • For patients: stories where AI is a partner with personally-felt impact, not just intervention. We don’t build trust in healthcare AI with technical documentation alone. We build it by showing how the technology behaves in the real world. How it works alongside people to make care more timely, equitable, and effective. If you want AI to scale across a complex health enterprise, architect the communication layer as deliberately as you architect the tech stack.

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𝟲 𝗵𝗲𝗮𝗹𝘁𝗵𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀 𝘁𝗵𝗮𝘁 𝗷𝘂𝘀𝘁 𝗿𝗮𝗶𝘀𝗲𝗱 𝗰𝗮𝗽𝗶𝘁𝗮𝗹 𝗮𝗻𝗱 𝘄𝗶𝗹𝗹 𝗺𝗮𝗸𝗲 𝗻𝗼𝗶𝘀𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗺𝗼𝗻𝘁𝗵𝘀 🚀 Hello Patient– $22.5M Series A (Austin): Conversational AI agents integrated with EHRs to handle calls, triage, scheduling, and follow-ups. CEO Alex Cohen, Director of Engineering Patrick McJury & Head of Design & Product Taylor Dunham FallCall Solutions – $1.5M Seed (Connecticut) Mobile + wearable emergency platform for seniors, integrated with Apple Watch and smart devices. CEO Shea Gregg, MD & Kristin Gregg Noctrix Health, Inc. Health – $33.5M (California): Wearable therapy device (Nidra™) for Restless Legs Syndrome, offering a non-pharma treatment alternative. CEO Shri Raghunathan & Director of Software Engineering Ben Shelly Predoc – $30M (New York) AI engine that automates clinical document processing (including faxes), already reducing oncologist review time by 70%. CEO Nish Hari, Director of Engineering Ethan Brown & Director of AI Michael Frantz, PhD Citizen Health – $30M Series A (San Mateo) AI-powered virtual advocate for rare disease patients, combining longitudinal community data with personalized support. CEO Farid Vij, Head of Engineering  Lin Wan & COO Hila Moyal Isaac Health – $10.5M Series A (New York) Home-based cognitive care platform using AI to detect, diagnose, and manage Alzheimer’s earlier. CEO Julius Bruch, MD PhD & Head of Engineering Karlan Eberhardt Adding direct links to the funding news in the comments. 👋 follow me for more lists like this every week ✨ thank you to MedCity News, Fierce Healthcare & Crunchbase for sourcing #HealthTech #AI #Biotech #HealthcareInnovation #Startups #Funding

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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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The Single Patient Record is finally moving from concept to evidence, and NHS England has kicked off the Test and Learn phase. Three technical proofs of concept are being tested in the Test and Learn phase: 1. A Hub & Spoke model to link regional records via shared APIs 2. A Central Integration Model for real-time national access 3. A Virtual Data Layer that joins data on demand, not by duplication Each approach asks the same question 👉 Can we join up care safely, securely, and simply - without starting from scratch? For years, we’ve talked about interoperability like it’s a technology problem. It isn't. It’s a design and delivery challenge - and this phase is a chance to test what "good" really looks like at system scale. The interesting part is the approach is open, collaborative, and grounded in reality - discovery with frontline users, not just design in meeting rooms. If it works, it could redefine how health data flows across the NHS. Not a single system. A single understanding of the patient. Finally.

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🎖️Hospital Execs speak out: This is the one feature your Health AI product must have... 👇 Two great articles from Modern Healthcare highlight the strategic shift for health systems... ‼️ Its all about the ROI Pam Teufel at Main Line Health: "We don't have the luxury of doing something and not worrying about the profit." This isn't just talk - it's the new reality for healthtech vendors as healthcare organizations restructure and consolidate. Let's look at the hard truth: 💼 Only 23% of healthcare orgs using AI can quantify ROI (McKinsey, April 2024) 💼 As Corewell Health's CIO puts it: "Every dollar spent on technology is a dollar not going to a nurse" 💼 Out of 185 AI solutions Corewell evaluated, only 35 made it to production Why such low numbers? Because many solutions "weren't good AI - weren't even good math." ⚡ The message is clear: No ROI = No Deal. If you can't quantify your value, you're just adding to the noise. At Soothien HealthTech Advisory, we're helping healthtech companies navigate this new landscape with our Clinical Enterprise Value Stack (CEVS) by: • Equipping mid-management with performance tools • Proving executive-level ROI • Ensuring frontline adoption If this resonates with your healthtech challenges, let's connect. I'm here to help you speak ROI fluently and fast. ➡️ DM me to ensure your healthtech product is driving differentiated value (Article link in the comments)

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