Dr Matthew Tranter

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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EU Tightens Rules as Health Apps and App Stores Become Medical Device Providers: 🔘The EU has ruled that health apps, app stores, and AI developers with a medical purpose are now officially considered medical device makers 🔘These companies must comply with the same stringent rules as traditional medical device manufacturers, bringing Apple, Google, and others into the regulated healthcare supply chain 🔘Software offering diagnosis or therapy will usually be classified as Class IIa medical devices, with higher-risk apps moving into stricter Class IIb or III categories 🔘Apps claiming interoperability with electronic health records face even tougher scrutiny, needing to align with both EU medical device laws and the upcoming European Health Data Space Regulation 🔘Regulators say this step is needed after years of health apps and wearables making questionable medical claims without oversight 🔘While the move improves patient safety, transparency, and accountability, smaller startups and research projects may find the compliance burden challenging 🔘The changes build on the EU AI Act, which aims to set rules for safe, transparent, and trustworthy AI in healthcare and beyond #DigitalHealth #AI #DTx

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AI is a smart play for value-based care. But embedding AI directly into VBC clinical workflows? That’s a gamechanger. I like how this article, Securing a Competitive Edge in Value-Based Care with AI, from Navina breaks down 8 real-world strategies for thriving in value-based care. They share key insights from industry leaders on how to use AI to: • Decode contract details like attribution and risk adjustment • Prioritize high-impact initiatives that actually move the needle  • Use smarter risk prediction to keep patients healthier (and costs lower) See how organizations are embedding AI directly into clinical workflows—no extra clicks—to save hours each week, reduce burnout, and boost quality scores. Jefferson City Medical Group saw real results using these methods: faster chart prep, less clinician burnout, and a boost in colorectal cancer screening performance. If you want to see what applied AI in value-based care really looks like, read the article: https://lnkd.in/gG_RbvUy

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Hippocratic AI, Commure, Nabla, Tandem... AI agents are now scaling… And outpatient care needs a tech upgrade. Otherwise, agentic AI will just be a painkiller on a broken leg.   I wrote down a few additional thoughts:   1️⃣ 𝐓𝐡𝐞 𝐮𝐧𝐛𝐮𝐧𝐝𝐥𝐢𝐧𝐠 𝐨𝐟 𝐥𝐞𝐠𝐚𝐜𝐲 𝐄𝐇𝐑/𝐄𝐌𝐑 𝐢𝐬 𝐚𝐥𝐬𝐨 𝐡𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠 𝐢𝐧 𝐨𝐮𝐭𝐩𝐚𝐭𝐢𝐞𝐧𝐭 𝐜𝐚𝐫𝐞 In one of my earlier posts, I made a comparison with FinTech, where traditional banks were "unbundled" into focused, specialized services:   B2C Payments → PayPal B2B Payments → Stripe Banking → Revolut Lending → Klarna Investing → Robinhood ...and so on.   We’re seeing the same thing with EHR/EMR systems in outpatient care.   Vertical AI agents (like medical scribes, decision support, billing tools, or agentic outpatient systems) are solving specific problems much better than legacy systems.   2️⃣ 𝐓𝐡𝐞 𝐮𝐧𝐛𝐮𝐧𝐝𝐥𝐢𝐧𝐠 𝐢𝐬 𝐛𝐞𝐢𝐧𝐠 𝐩𝐮𝐬𝐡𝐞𝐝 𝐛𝐲 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐚𝐥 𝐭𝐫𝐞𝐧𝐝𝐬 One-third of GPs (typically covering 40% of practices) are set to retire within 5 years (in Germany).   They’ll be replaced by a younger generation of doctors who prefer to work in modern, tech-enabled practices.   The share of employed physicians (vs. resident/self-employed) has already jumped from 5% to 20% over the past 15+ years (in Germany).   The practices that win will be those that attract this new generation, by offering cutting-edge tech and keeping admin work to a minimum.   This will only speed up the move away from legacy systems.   3️⃣ 𝐒𝐨 𝐰𝐡𝐨 𝐰𝐢𝐥𝐥 𝐰𝐢𝐧? 𝐀𝐈-𝐧𝐚𝐭𝐢𝐯𝐞 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 … 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐩𝐥𝐚𝐲𝐞𝐫𝐬 𝐰𝐢𝐭𝐡𝐢𝐧 𝐭𝐡𝐢𝐬 𝐧𝐞𝐰 𝐞𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 Point solutions (these vertical #AI agents) need to be “rebundled” into platforms.   That’s what AI-native operating systems like ETERNO are doing.   But it’s not just about integration.   These operating systems also curate the right tools and give them market access, both technically (via APIs and architecture) and commercially (through distribution and go-to-market support).   4️⃣ 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 𝐞𝐪𝐮𝐢𝐭𝐲 𝐢𝐬 𝐰𝐚𝐭𝐜𝐡𝐢𝐧𝐠... I speak weekly with private equity firms about the #outpatient care software space in Germany and across Europe.   Everyone feels this is a turning point.   CVC went for the incumbent CompuGroup, acquiring 28%.... but it was their distressed assets team that made the move: that says a lot.   Meanwhile, 20% of outpatient practices in Germany (and growing) are already PE-owned.   For private equity, building this new AI-driven tech stack in outpatient care is the next frontier for value creation. -------- Follow HGM Advisory for daily reposts of the most relevant #healthtech news. #agentic #healthcare

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This is so such a good video. The headline: "doctor argues AI can ‘clearly’ make better diagnoses than ‘some’ medical professionals" is true but the best part about this is that AI doesn't need to (and darn well shouldn't) replace doctors, it enhances doctors, and reduces doctor burnout. https://lnkd.in/eCMuRp6X

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Finally got a moment to put down some reflections from the HIMSS #AI in #Healthcare Forum 2025 in NYC. In the first place, why attend a healthcare AI conference at all when there's so much content online and every other post is from a self-proclaimed AI expert? Because sometimes, you need to cut through the noise. You need to hear from people actually doing the work — building tools, failing fast, leading change inside hospitals, startups, and health systems. You need real talk, not hype. That’s exactly what this forum offered: genuine, candid conversations and a chance to see AI tools in action — what works, what doesn’t, and what still needs fixing. Here are some of my #reflections on achieving value at scale with AI in Healthcare: 1. You don’t have an AI #strategy if you don’t have a #data strategy. Strong data foundations are essential — before anything intelligent can happen. 2. Start with #WHY. Every AI initiative needs purpose, measurable outcomes, and an exit path if it doesn’t deliver. 3. #Trust is earned, not assumed. We can’t look AI in the eye — it must earn trust through reliability, #explainability, and workflow harmony. 4. #Startups: Build with someone who’s walked the floors. Deep workflow knowledge beats generic AI skills every time. 5. #Governance first. AI second. Unlike other tech, early adopters of AI can run ahead of governance. That’s why our governance models must evolve fast — to match new behaviors and risks. No scaling without ethics, model drift monitoring, and transparent decision logic. 6. Avoid long-term #vendor lock-in. Unlike legacy tech, AI contracts should be short, flexible, and tied to utilization — not a 5-year leap of faith. 7. #Ambient is here. But #UX still breaks it. #Rage clicks are real. If a tool frustrates users, even good AI won’t survive adoption. 8. #Tech stack is nothing without a #human stack. From Minnesota’s playbook: Tech = data, models. Human = skills, governance, trust. Build both if you want adoption. 9. Choose use cases wisely. Start with #high-impact, #low-risk tasks (like ambient scribing or scheduling), then move toward high-risk, high-impact domains like clinical decision support. 10. #ROI isn’t just numbers. Burnout relief, cognitive ease, and clinician retention are just as valuable as hours saved. AI should give time back, not take more away. 11. The road ahead? Think ambient assistants, personalized nudges, passive monitoring — and tools that improve the #patient #experience too: managing unstructured data, PROMs, and supporting real-time feedback. These are just a few takeaways — there was so much wisdom from the trenches, shared by those leading the charge with curiosity, caution, and clarity. Kudos to the incredible speakers, vendors, and the HIMSS team for curating such an immersive, grounded, and forward-looking experience !

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Three types of healthcare AI companies are emerging—and which one you bet on could determine if you succeed, get acquired, or vanish in the next five years. Here's what you should know: 1. The Platform Players: These are the ambitious companies trying to become the operating system of digital health. They unify patient records, analytics, communication, and care pathways under one umbrella. The dream? To lock in customers (hospitals, clinics, providers) for a decade. The risk? Big, slow, and at risk of bad rollouts. Epic and Innovaccer unify records, analytics, comms, and care into true digital health “operating systems”—locking in hospitals for years. 2. Agile Point Solution Specialists: These are nimble companies going deep on a single problem: automating documentation, accelerating prior authorizations, catching hidden diseases, or improving coding accuracy. They get pilots quickly and prove ROI. The upside? Easier entry and fast wins. The catch? They might get copied, acquired, or sunsetted when platforms roll out “good enough” features. Tempus (AI oncology) and Aidoc (radiology triage) dominate single high-value workflows and quickly prove ROI before expanding. 3. Infrastructure Enablers: Quietly powering the rest, these companies focus on secure interoperability, privacy, and compliance for everyone else. They offer the tools that allow others to scale or launch new features. They’re essential but low-key, often acting as the backbone for the industry. AKASA (revenue cycle automation) and Cedar (billing/payment AI) quietly power system-wide efficiency and compliance for everyone. Why does this matter? Because healthcare is about to consolidate again—platforms will be acquisitive, point solutions will either scale or sell, and infrastructure is the glue holding it all together. If you’re picking a partner or building your own offering, ask: Are you a platform, a point solution, or enabling critical infrastructure for the rest? And who’s best positioned for the next wave? What healthcare AI play are you watching—and have you seen a category-busting startup others have missed? Share below! Check out quick.md, @ryanlissack on X for more insights.

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