Vijay Venkatesh

Abridge partners with Highmark on real-time prior authorization. Another example that AI Scribing is not about the note. The last few days brought rumors about Epic launching an AI scribe that will supposedly vaporize billions in investor money across the ambient AI space. But AI scribe valuations aren't high because they transcribe conversations. It's high because once you become trusted software in a physician workflow, you can go upstream and downstream into literally any other workflow. Abridge and Ambience Healthcare are doubling down on revenue cycle management (RCM) workflows, and this Highmark partnership is a perfect example. Their AI solution automates completing prior auth forms, submitting and reviewing requests, tracking status, and identifying when authorization is needed in the first place. Per my understanding, it works like a real-time checklist during patient encounters, outlining authorization criteria for specific procedures and letting clinicians push through approvals at the point of care. This is quite transformative because, as said in the article, prior auth is normally done POST encounter, while with Abridge it can be done "at the point of conversation". The AI scribe valuations won't go to zero. They'll consolidate around companies that find ways to go upstream or downstream where the money is made (or saved). But, important nuance (only the OGs will understand this reference), it requires deep EHR partnerships AND finding use cases EHRs don't want to build themselves. Prior auth is perfect: too messy and politically sensitive for Epic to own directly. Abridge figured this out early, went all-in on Epic partnership (literally opened a Wisconsin office to be closer to headquarters), and now they're reaping the benefits. Cool stuff! PS: I will say 95%+ of AI Scribes won't survive because EHRs won't build 130+ partnerships like this, so yes Epic will vaporize a lot of investor money but not all of it.

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TOP 20 US healthcare products that will create 2026 🔥 💫 Patient Engagement & Digital Health Platforms 1. Evva Health by Carl Hanna, PhD & Saurabh Vyas – AI companion platform for caregivers of people with dementia. 2. VitalTech® – helps patients with chronic conditions stay connected to care teams (by integrating with 70+ health systems and EHRs). 3. Thrive25 by Ryan Schneider & Max Schlather – helps individuals identify risk factors early, adopt healthier habits, and stay on top of their long-term wellbeing. 4. Higi, a Modivcare service by Thomas Denison – digital health monitoring that simplifies patient life. 5. Univfy by Mylene Yao, M.D. – AI predictions for IVF success rates and cost transparency. 💼 AI in Diagnostics & Clinical Decision Support 6. Paige by Razik Yousfi – deep learning for cancer diagnosis and treatment planning. 7. Flatiron Health by Nathan Hubbard – oncology data analytics platform advancing cancer research and care. 8. Prognos Health by Sundeep Bhan – AI platform using diagnostic data for early disease detection and risk prediction. 9. K Health by Allon Bloch – AI-driven app providing symptom checking and virtual doctor connections. 💻 Blockchain & Data Privacy 10. Embleema by Robert Chu – blockchain platform for secure real-time patient data sharing. 11. Hu-manity.co by Shaleen (Sheli) Gupta – blockchain solution for monetizing and protecting personal health data. 🧪 Biotech & 3D Bioprinting 12. Prellis Biologics by Mike Nohaile – 3D holographic bioprinting of human tissues and organs. 13. Onkos Surgical by Patrick Treacy – develops oncology-focused surgical solutions with 3D technology. 14. NeoLight, LLC by Vivek Kopparthi – infant-focused devices (e.g., phototherapy for neonatal jaundice). 15. Green Sun Medical by Jamie Haggard – dynamic braces for scoliosis and spinal deformities. ⚗️ AI in Drug Discovery & Genomics 16. Healx by Tim Guilliams – AI platform accelerating drug discovery for rare diseases. 17. Verge Genomics by Alice Zhang – uses AI and human genomics to discover new medicines for brain diseases. 18. Alector – immuno-neurology company developing therapies for neurodegenerative diseases. 19. BenevolentAI – AI-driven drug discovery targeting currently untreatable diseases. 20. Freenome – AI and multi-omics platform for early cancer detection via blood tests. #HealthTechUSA #HealthTech #USHealthcare

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My journey in healthcare started back in 2008–2009 in Pune, India, when I worked with global leaders to build HIPAA and HL7-compliant products for insurance and healthcare companies. Since then, I’ve spent nearly two decades working with data and AI—transforming raw information into actionable insights across industries worldwide. At CureBay, we’ve taken this vision further by building a comprehensive Data & AI platform that: ✅ Acquires data from clinics, partners, mobile apps, and point-of-care devices ✅ Ingests, processes, and organizes data into a unified lake and warehouse ✅ Delivers real-time analytics and insights using AI/ML and Generative AI ✅ Powers on-demand reports through in-house language models and AI Agents ✅ Ensures compliance, governance, privacy, and ethical use of healthcare data This is not just a project—it’s a platform designed for scale, speed, and impact. Many global organizations are now modernizing towards similar architectures. We’re excited to collaborate with pharma companies, payers, research institutions, CROs, and health-tech innovators who want to leverage healthcare data and AI to transform patient care and outcomes. If this resonates with you, let’s connect (Sanjay Swain or Himanshu Dhiman). Together, we can shape the future of healthcare. #HealthcareInnovation #AI #DataScience #HealthTech #DigitalHealth #CureBay

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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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This month, 101 GenAI features a happy customer, Shalini Gupta, discussing Asima Health's utilization of agentic AI in healthcare. In this engaging conversation, Shalini Gupta shares her unconventional journey from academia to entrepreneurship, detailing her motivations for founding Asima Health, a company focused on cancer diagnostics. She discusses the technical challenges faced in developing innovative diagnostic tools, the importance of measuring success through clinical milestones, and her vision for democratizing access to cancer screening. Shalini also highlights the role of AI in enhancing diagnostic capabilities, her personal reflections on failure and success, and the balance between work and personal life. The conversation concludes with her recommendations for reading and influential figures in her life. Also, a big thank you to Shefali Lathwal, Smita Agrawal, Prashanth Aditya Susarla & Praveen Dua for their amazing work with Asima's team. Watch the full video here: https://lnkd.in/gZ3geZcu How are you utilizing Agentic AI to enhance healthcare? Please comment.

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TIME & Statista recently released their “World’s Top HealthTech Companies of 2025” report. Here are my observations 🔍 𝗪𝗵𝗮𝘁’𝘀 𝗥𝗶𝘀𝗶𝗻𝗴 𝟭. 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗳𝗿𝗼𝗻𝘁-𝗿𝘂𝗻𝗻𝗲𝗿𝘀. Firms leading in AI/data are not just getting noticed - they’re dominating. From screening to diagnostics, matching treatments, innovations here are what TIME marks as “most impactful.” 𝟮. 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰𝘀 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘂𝗻𝗰𝗼𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗲𝗮𝗻𝘀. Companies like Fedo (selfies to assess vitals), Canary Speech (voice-based detection) are pushing boundaries.  𝟯. 𝗪𝗲𝗮𝗿𝗮𝗯𝗹𝗲𝘀 & 𝗧𝗲𝗹𝗲𝗵𝗲𝗮𝗹𝘁𝗵 𝗱𝗲𝗺𝗮𝗻𝗱 𝗶𝘀 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲 We have been trained to be demanding consumers. Patients today expect transparency and instant access. These companies are delivering just that. ⚠ 𝗪𝗵𝗮𝘁’𝘀 𝗟𝗮𝗴𝗴𝗶𝗻𝗴 / 𝗥𝗶𝘀𝗸𝘆 𝟭. 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝘂𝗻𝗱𝗲𝗿𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲𝗱. Despite its obvious potential for cost-savings and improving population health, prevention is not getting equal attention. Innovations here aren’t scaling fast. Why? Maybe because systems (and incentives) tend to reward curative measures over preventive ones.  𝟮. 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝘃𝘀. 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗮 𝘁𝗲𝗻𝘀𝗶𝗼𝗻. Diagnostic tools are powerful but often expensive or resource-intensive. Scaling them globally (especially in low-resource settings) remains challenging. Without bridging that gap, health inequities could worsen. 💡 𝗪𝗵𝗮𝘁 𝗟𝗲𝗮𝗱𝗲𝗿𝘀/𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗼𝗿𝘀 𝗦𝗵𝗼𝘂𝗹𝗱 𝗗𝗼 𝟭. 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗮𝗻𝗱 𝗿𝗲𝘄𝗮𝗿𝗱 𝗽𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻. Health systems, governments, insurers must fix their incentives. We must reward keeping people healthy, not just treating them when things go wrong. 𝟮. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗱𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Tools shouldn’t just help those living in urban centers. The most insight (and impact) comes when diagnostics, wearables, telehealth are accessible everywhere. 🔮 𝗕𝗶𝗴 𝗣𝗶𝗰𝘁𝘂𝗿𝗲: 𝗪𝗵𝗮𝘁’𝘀 𝗖𝗼𝗺𝗶𝗻𝗴 1. More voice-based diagnostics (speech patterns, tone, and linguistic features) could flag health issues before symptoms are obvious. 2. Wearables predicting - not just tracking. Machine learning models integrated with wearables could anticipate changes in health status triggering early interventions. 3. Hybrid care models merging telehealth + local physical care + AI-driven diagnostics will become the norm. The pure in-person model is already being disrupted. ✅ 𝗜𝗻 𝗦𝘂𝗺 TIME’s list is more than a roster of winners; it’s a preview of where health tech is headed. Would love to hear your take: which of these trends do you think will have the biggest impact over the next 2-3 years. Where do you see the biggest risk of being left behind? #HealthTech #DigitalHealth #Innovation #AI #PreventiveCare #Diagnostics #Leadership

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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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App Ecosystem across EHRs: Patient Care for Epic, Workflow for Athena? After Brendan Keeler posted about the new datasets from the Assistant Secretary for Technology Policy (ASTP), I got tagged and a few people DM'ed me to load these datasets to mimilabs. (Thanks Brendan! ;)) And yes, I loaded them. Now we have data on which hospital/provider uses which EHR, which hospitals are connected to HIE, and what kinds of apps are available in EHR marketplaces. To know more about these datasets, see Brendan's post (https://lnkd.in/eXbfn6va) — he outlined and explained it very well. I also wanted to take a look at the data, but a bit differently from what Brendan found. So here's what I did: I took the EHR app data and decided to look at the distribution of app categories across different EHRs. Since the category names weren't quite standardized, I did a bit of cleaning up. The chart shows the categories of available apps and where those apps are coming from. A few observations: - Epic has all sorts of categories. All possible categories are there. - The #1 category for Epic is Patient Care & Engagement. - The #1 category for athenahealth is Administrative and Workflow. - Since Epic has so many interesting app categories, #2 is misc. - Billing and Revenue, Documentation categories are important for Epic, Athena, as well as other platforms. I wonder if the large driver of Epic having so many patient care apps is due to its client base being hospitals? Doctor-preneurs see more of those needs, maybe? And Athena having more independent practices—they need more help on administrative and workflow to automate with limited resources? What are your thoughts? It's fascinating to see these app ecosystems vary so much across platforms!

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💡 I spent 10+ years at Google to learn how to change medicine in 45 minutes. Here's how I did it ⬇️ Last week I posted about BrachyStories, Mission-Driven Tech's outcome improvement research. I had an incredible response, now I'm sharing the behind-the-scenes on we did it. ☢️ Brachytherapy cures cervical cancer. It also brutalizes and forever changes a person's life. At MDT, we know it's possible to treat *and* achieve high quality outcomes. 🟠 Enter, BrachyStories. In under and hour, BrachyStories takes clinicians from 0 to 1 on 3 key patient safety principles: 1️⃣ The patient experience 2️⃣ Pain control management 3️⃣ Treatment recovery resources + referrals ⁉️ How did MDT do it? ✅ Feature real survivors: for the first time on camera, international cervical cancer survivors from the Cervivor, Inc. community + beyond bravely contributed their real, raw and unfiltered stories surviving brachytherapy. In some rooms, it’s the first time clinicians have heard patients recall their experience surviving surgery while awake. 🏆 Work with the best partners: Leyla Tatiana Rosario, Jadalyn Ortiz and Jessica L. Ransom of @The Gig Media, Andrew Strickman & Kim Cortese came together to support MDT's vision of bold storytelling to create change 🎬 Produce a short doc film like a top-performing YouTube video: ⏲️ grab attention in the first 5 seconds 💥 use bold supers to underscore key points + aid recall ➡️ feature a clear call to action that moves people to act 🔬 Get in front of leaders responsible for change: MDT debuted BrachyStories at the 2024 AMERICAN BRACHYTHERAPY SOCIETY World Congress to an audience of global radiation oncologists and medical physicists. It was the first time survivor voices have been featured at the conference. 📊 Measure in real time: Google surveys powered simple pre & post surveys to quantify lift against BrachyStories' 3 key safety principles. This flexible measurement format quantified competency lift and quickly built proof points. Want to learn more? Check out BrachyStories here: 🔗 https://lnkd.in/eEjsENiP

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Doximity just released a free AI scribe for US clinicians, accelerating commoditization/the race to the bottom. Their product won’t integrate into the EHR like Abridge, but it’ll be interesting to see how much it impacts their ability to acquire customers, drive usage and maintain retention. This is the start of a trend that will continue across every vertical market. Counterpositioning with free products to undercut upstarts and incumbents alike. Buckle up. Your margin is someone else’s opportunity.

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Yesterday I wrote about why Epic would launch its own AI scribe. If you’re an Ambient vendor, how should your strategy change? My thoughts: 1️⃣ Should you just be patient and stay the course? Because eventually folks will realize delivering a successful Ambient solution is incredibly complex and come back to you anyways This is probably the path most Ambient vendors will take and it’s often the right one. It wouldn’t be the first time that this cycle occurs: → An EHR releases its own alternative product to best-of-breed third parties → The market cools as health systems feel obligated to try the “cheaper” or “free” product from the EHR → Health systems realize it actually isn’t good enough to solve the problem → The market eventually migrates back to the best-of-breed products Now this often delays market adoption of the third parties by a few years, but if the startups can be patient and survive long enough, they can still win in the end. And by then the EHR moves onto other core problems to solve. But you have to be pretty darn sure that the ambient use case is complex enough (e.g. due to specialty-level customizations, languages, etc.) that health systems will come back to you. Because if you’re wrong and you just wait… it could be a death sentence. 2️⃣ Could you win through business model innovation? This would mean switching from what is currently a very good subscription business model to something riskier and potentially less lucrative in the short term. For example, maybe startups should go “scorched earth” and make the AI scribe close to no cost - and only charge for the downstream products (e.g. improved revenue capture via better medical coding / compliance). So that way you at least keep health systems choosing your baseline ambient solution (so you get a foot in the door) while you upsell them later. The challenge with this is that the startups will have higher costs than Epic because their product is more complex to maintain and they are providing more robust professional services (e.g. change management). Startups really do need to charge much more than Epic to justify these investments… but it’s very hard to compete against Epic’s “essentially free”. 3️⃣ Do you consider getting acquired while your valuation is still sky high in a very frothy AI market? To be honest I really hate this option and believe it would be an overreaction for most startups. But if you’re not the No. 1 or 2 player, with no compelling thesis as to why you’ll eventually be No. 1 or 2 and you’re also not profitable… it’s as good a time as any to consider your options. I still think Oracle Health should acquire Abridge and give them control over accelerating AI across Oracle customers. Epic potentially competing with Abridge now may be the compelling event that’s needed for Abridge or a similar startup to seriously consider partnering with the other team. 4️⃣ Do you more aggressively pursue non-Epic customers? Continued in comments 👇

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Every week, another “health AI” startup makes headlines. Some will transform care delivery. Most will fade into the background. The difference? Signal vs. Noise. Signal looks like: Deep domain expertise in healthcare (not just generic AI) Measurable clinical or operational outcomes Seamless integration into existing workflows   Noise looks like: “health AI” without proof points Flashy demos that don’t scale in clinical settings Technology searching for a problem As the market matures, investors, health systems, and founders will need sharper filters to separate real impact from hype. The stakes are too high—for patients, providers, and payers—to waste cycles on the wrong side of the signal-to-noise equation. How do you separate signal from noise in health AI? #HealthAI #DigitalHealth #HealthcareInnovation #ArtificialIntelligence #ValueBasedCare Saltgrass Advisory

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What topics in AI x healthcare would you be most excited to read about? I’m bringing back my Substack (https://lnkd.in/gu5ay4f) and thinking through topics to dive into around AI × healthcare. Given my background — founder of Avon Health (the first AI-native EMR to receive ONC certification), prior work in ML at Microsoft, PathAI, Carnegie Mellon University, and Stanford University, a patent in cloud ML, and a thesis on computer vision for restoring functional vision in the blind — I’ve been deep in both the technical and business sides of AI in healthcare for almost 15 years. Here are some areas I could explore: - Emerging AI business models in healthcare (pricing, reimbursement, value-based adoption) - Regulatory pathways and ONC/FDA/CMS policy shaping AI adoption - How large language models are transforming clinical workflows - Case studies of AI products that actually made it to production in health systems - Interoperability challenges and how AI can bridge them - The real economics behind AI scribes and automation in care delivery What would you be most excited to read about?

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In massive, fragmented markets like healthcare AI where it’s rarely winner-take-all, being a late entrant can actually be an advantage. 1/ Early entrants expend time and capital meandering through countless GTM experiments. 2/ Falling input costs (vendors, inference) lowers the cost to launch and get a sellable product to market. 3/ Each marginal dollar later in the cycle works harder than the tens of millions spent educating customers. But late-entry advantage only materializes if founders are deeply informed about their own market. The bar is higher from investors: a clear, differentiated raison d’être; elite execution velocity; and an intense, underdog sense of urgency.

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