Petr Bartoš

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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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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So much talk about AI. It can be hard to find the signal in the noise. We’ve been building data-driven healthcare applications for years and have a very strong opinion: Applications should support the users, not the other way around. This has been a fundamental truth long before AI, and it will be as we adopt AI-enabled features in the years to come. https://lnkd.in/eTT3T2t4

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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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📢 New AI implementation into the diabetes workflow When it comes to diabetic foot complications, the traditional care model has often been about reaction, not prevention. My own father, previously managed under a large traditional care system, whose diabetic neuropathy and the severe progression to Charcot foot only came when it was already too late, lead to a toe amputation as his only remaining solution. This story is tragically common, and it’s why we at GluCare.Health and meta[bolic] are committed to a different path bringing in new technology earlier into the workflow. The Problem: A Silent Epidemic in the UAE 🇦🇪 In the UAE, diabetic neuropathy is a widespread affecting up to 70% of people with long-standing diabetes. This nerve damage robs you of sensation, which means a small blister or a cut can go unnoticed and develop into a severe foot ulcer. This is why more than half of all foot ulcers will become infected, with 20% of infections resulting in amputation. While standard care focuses on annual foot checks, we believe in a more proactive, data-rich approach. Our new AI thermal imaging detects subtle temperature changes that are the first sign of inflammation, appearing weeks or even months before a foot ulcer becomes visible. What This Means for our patients: Early Detection, Non-Invasive and Fast, and a practice of Preventative health as standard. Amplifai Health was developed in Saudi Arabia by an all Saudi team lead by Dr. Meshari F. Alwashmi. We need more technology built in the GCC to tackle issues widely prevalent in the GCC, and I'm proud to be an early adopter and supporter. This is more than just a new test. It's another crucial biometric that feeds into GluCare.Health’s continuous care model, giving us a richer, more complete dataset to provide patients with personalized and preventive care. Ali Hashemi Zeina A. Dr. Rashed Mahboubi

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The race for the “𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐇𝐞𝐚𝐥𝐭𝐡 𝐎𝐒” is on. Diagnostics, wearables, and AI are no longer separate — they’re converging fast. Entire ecosystems are racing to become the operating system for human health. What’s changed? ⚡ Labs → Apps 100+ biomarker panels, full-body scans, and clinician summaries are moving from boutique clinics into mainstream memberships. ⌚ Wearables → Ecosystems Wearable makers now connect 24/7 vitals with metabolic health, lab diagnostics, environmental signals, and more. 💪 Fitness → Healthspan Performance brands are pivoting into recovery, longevity, and whole-person wellness — not just workouts. 💻 Big Tech → Plumbing Apple, Google, Samsung Healthcare building the rails for interoperability, records & data liquidity. Who’s playing? 🧬 Diagnostics-led OS Function Health, Superpower etc. — bringing 50–100+ labs, MRI, and biomarker panels into memberships. ⌚ Wearable-led OS ŌURA, WHOOP, Ultrahuman, Withings etc. Expanding from signals to ecosystems. 💪 Fitness → Healthspan OS Peloton Interactive, Equinox, EGYM etc. — moving from workouts to recovery, longevity, and holistic wellness. 🥑 Nutrition & Metabolic OS Levels, Nutrisense, Dexcom etc — moving beyond glucose spikes and food logging toward metabolic coaching, personalized nutrition and holistic health. 💻 Platform Layers Apple Health, Google Health Connect, Samsung Health — owning the rails positioning themselves as infrastructure. What’s missing? One word: Context. Right now, most “Health OS” attempts are still islands. All handed back as dashboards. Useful? Sure. Somehow. But it still leaves the human to connect the dots The winners won’t have just more data They’ll deliver more meaning & intelligence The winning stack will look like this: 🧠 Contextualized intelligence → signals translated into why this matters today 🔁 Interoperability by default → device, app, and lab-agnostic 🎯 Last-mile coaching → real-time, hyper-personalized guidance 👥 Human-in-the-loop → empower health professionals, coaches & care teams with the same intelligence users see 🔒 Trust stack → privacy by design, on-device where possible, transparent rights 📱 Frictionless UX → one interface seamless enough to make every silo obsolete (PS: Curious how this works in practice? Check ONVY HealthTech — built for B2B integrations.) In other words: From tracking → understanding → timely action. Or, to borrow from software: from prompts → context engineering. Where it goes next: 🏠 Ambient health → light, air, noise, temperature, radiation & toxin exposure feed directly into recovery, health metrics, and daily behavior. Your home and environment aren’t background anymore — they become part of your health plan. 🤖 Agentic AI → not just explaining patterns, but handling tasks: scheduling labs, nudging routines, coordinating care. 🌍 Population → Personal → the same layer that helps one user scales to employers, insurers, and communities to actually move outcomes.

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🔍 Digital Health is about to shift not gradually, but structurally. At D11Z. Ventures, we’ve been analyzing how Agentic AI, LLMs with autonomy, memory, and task planning will rewrite the rules of digital healthcare. Not by replacing humans, but by rethinking how work happens. We’ve mapped 3 key shifts we expect to revisit in 6–12 months: ✅ 1. Clinical workflows aren’t assisted but orchestrated. Agents won’t just respond to doctors. They’ll initiate, triage, and escalate on their own. • A 2025 benchmark study showed that an autonomous LLM agent achieved 81 % diagnostic concordance and 99.2 % treatment-plan alignment with real-world cliniciansm outperforming doctors in 36.1% of mismatched cases. • In oncology, agentic systems correctly chose diagnostic tools in 97 % of cases and made complete, helpful decisions over 89 % of the time. • Review papers confirm: these agents go beyond rule-based tools, they adapt, act, and decide across time. 🧾 2. The boring stuff: documentation, billing and compliance are handled by agents. Agentic tools won’t start in surgery. They’ll start in paperwork. • A recent clinical study showed AI scribes cut documentation time by 69.5 %, saving providers 3 admin hours/week in live settings. • Salesforce’s 2024 report found U.S. health pros expect agents to reduce clerical workload by 36 %, roughly one full workday per week. • 80 % of General Practitioners in the UK using automated scribes reported time savings and better patient communication. 🎙️ 3. Apps are passive. Agents are the new patient interface Voice- and task-based agents are starting to replace app-based care. • A meta-review of AI mental health tools showed agent-based platforms can reduce anxiety/depression symptoms, rivaling brief human interventions, especially with long-term engagement. • Voice agents have also shown success in reducing loneliness and improving perceived social support among older adults. 📌 Bottom Line The shift from apps to agents is more than UX, it’s a new operating layer. The orchestrators, not just the assistants, are arriving. We're actively testing these predictions and will update them as real-world deployment unfolds. Let us know if you’re exploring similar shifts, or want to share use cases we should track. #DigitalHealth #AgenticAI #HealthcareAI #LLM #AIinMedicine #FutureOfHealth #StartupThinking #D11Z“

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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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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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