Chris Rallo

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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Three incredible days at #JPM2026. Same conversation with 10+ health system execs: "We're building our AI strategy. Evaluating vendors. Need to pick the right partners." This sounds familiar. 2009-2012: The EHR decision. After the HITECH Act, health systems spent 18-24 months evaluating Epic vs. Cerner. Consultants. Committees. RFPs. Multi-year implementations. The logic made sense: EHRs were monolithic platforms with huge switching costs. You had to get it right. 2025: The AI decision. I'm watching health systems do the same thing. "AI steering committees." "Strategic AI partner" RFPs. Trying to pick winners. But there's a critical difference: EHRs were a single, massive platform decision. AI in healthcare is dozens of different problems that need dozens of different solutions. Here's what health systems are missing: They're looking for "the AI vendor" when they should be building "an AI portfolio." There's no single AI platform that solves:   → Documentation burden (ambient notes) → Radiology throughput (workflow optimization) → Clinical trials matching → Revenue cycle optimization (across specialties) → Clinical decision support These are different problems. Different technologies. Different vendors. Different timelines. Some need FDA clearance and 24-month validations. Some can pilot in 90 days. Some integrate with Epic. Some replace parts of Epic. You can't solve all of these with one "strategic AI partnership”....no matter that Anthropic is saying right now. <ducks> The AI landscape is evolving too fast for monogamous vendor relationships. 6 months ago: GPT-4 for clinical documentation. Today: Ambient listening, agentic workflows, multimodal imaging analysis. 6 months from now: Something else entirely. That's why you need exposure across the whole landscape—not a bet on one vendor. The mistake I see health systems making: Approach 1: "Strategic Partner" Strategy Pick one AI vendor → Enterprise deal → 3-year commitment → Hope they solve everything Result: You're locked in. Technology moves on. You're stuck in vendor negotiations while competitors move faster. Approach 2: "Portfolio Strategy" Deploy capital across multiple companies → Take equity → Co-develop solutions → Maintain optionality Result: You capture upside. You shape products. You're first to new capabilities. You have relationships across the ecosystem. After 20 years building innovation platforms at JPMorgan, Mousse Partners, Blueprint Health, Junto Health, Atlantic Health: In fast-moving markets, portfolio strategies beat single-vendor bets. Healthcare AI is moving fast. The question isn't "Which AI vendor should we pick?" The question is: "How do we build an AI portfolio that gives us the most optionality" If you're thinking about this, let's talk. 📩 douglas.m.hayes@gmail.com ☎️ 215-620-2032 #healthcareAI #digitalhealth #venturecapital #innovation

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𝗛𝗟𝗧𝗛𝟮𝟱: 𝗔𝗜 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗠𝗲𝗻𝘁𝗮𝗹 𝗮𝗻𝗱 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗛𝗲𝗮𝗹𝘁𝗵 At HLTH 2025, Spring Health, Oura, and Hinge Health debuted major AI advancements:  • 𝗦𝗽𝗿𝗶𝗻𝗴 𝗛𝗲𝗮𝗹𝘁𝗵 launched VERA-MH, an open-source benchmark to assess the ethics and safety of mental health chatbots.   • 𝗢𝘂𝗿𝗮 announced a large-scale study to detect early signs of hypertension through its wearable ring, expanding its cardiovascular health focus.   • 𝗛𝗶𝗻𝗴𝗲 𝗛𝗲𝗮𝗹𝘁𝗵 introduced AI-powered movement analysis and a 24/7 assistant, Robin, to support musculoskeletal care. Together, these innovations reflect the industry’s shift toward safer, more personalized, and preventive AI-driven healthcare.

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What needs to change first and foremost is healthcare finance. These insurance bizdiots consume more than half your healthcare dollar, up front. Then we need to make the big hospital systems run efficiently. Finally, we need to stop hemorrhaging 84% of healthcare expenditures on avoidable, behavior-based chronic disease. These three financial problems can be solved with education and automation. 1. Automate the health insurance industry. Provide the EMR, pay 150% of Medicare for procedures documented. 2. Provide an ERP style hospital administration system to find cash leaks and inefficiencies. 3. As the insurance company, offer double digit discounts for improved lab results. Problem solved Overview of the solution (that's in production, by the way): https://lnkd.in/eUt_HTQX

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Tremendous thanks to Sylvana Quader Sinha, Esq. for including me (and my book) in her Forbes series on the future of health AI — especially from the perspective of physician leaders and entrepreneurs. This week’s piece (the third in the series) https://lnkd.in/eezYSKvE dives into a point I’ve been making for some time — in my book, "Rushing Headlong: Health IT's Legacy and the Road to Responsible AI," and in talks around the country: You can’t layer AI on top of a broken health IT system and expect it to work. The truth is, our U.S. health IT architecture was never built for how care or research actually happens. It was built for billing and documentation — not for patients, and not for physicians. When we embed AI into those same legacy systems, we aren't creating a more efficient or more effective system. We simply are automating legacy dysfunction. The real opportunity is to stop and ask if we should keep patching the old system, or redesign it from the ground up? If we are willing, physicians — not administrators — must be at the center of that design. What I find powerful about Sylvana’s latest piece is how it shows emerging markets doing exactly this: starting from clinician shortages, uneven access, and minimal IT infrastructure, and using AI to extend frontline clinical capacity. In contrast, many U.S. efforts seem focused on making legacy billing systems more efficient, including AI for scribing and prior authorization. In emerging markets, conditions of clinical scarcity are forcing “architectural clarity” — local, clinically grounded designs that prioritize patient care, trust, and real-world constraints. In essence, a clinical-first mentality. ​ For more context, check out Sylvana's last article earlier this month about why healthcare AI is still failing the clinical setting. (https://lnkd.in/edaJwXBE) The future of healthcare AI will only succeed when physicians and clinicians lead the design — not simply adapt to it. #ResponsibleAI, #FutureofHealthcareAI; #HealthIT, #ClinicalCare, #ClinicalFirst, #AIHumanism

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Excited to share our new integrated ambient scribing feature within Counterpart Assistant! We've long recognized that value-based care is effective if delivered at the point-of-care, with the primary goal of earlier diagnosis and better management of chronic diseases. With the integration of ambient scribing, Counterpart Assistant (CA) now makes it even easier for providers to integrate value-based care workflows into their conversations with patients. A few ways we differentiate: 💲 Advanced technology at no added cost to CA providers - independent practices should have the latest technologies for care, even if they don't have large IT budgets. 🩺 Clinical-first approach - Actionable and personalized clinical insights specific to the patient you're having a conversation with is what truly impacts clinical care. ⚕️ Integration of Value-based Care Best Practices - reducing documentation time is not meaningful if we're not bending the curve on care quality. It is truly an exciting time where technologies such as AI can support clinicians to do what they do best - care and manage their patients in a way to avoid complications and live healthier lives.

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Shubh Sinha joined the CareTalk Podcast: Healthcare. Unfiltered. to break down the math behind compliant data infrastructure. A few numbers that stood out: 12+ weeks → in as little as 72 hours That's the typical timeline compression for data certification we're seeing through our work at Integral. One pharma customer went from activating 2-3 datasets per year to monthly deployments after switching to our automated pipelines. 30-80% data retention improvement The difference between HIPAA Safe Harbor's one size fits all approach and expert determination's contoured analysis. The ROI calculation with automated compliance review is straightforward: → Recoup full data purchase budgets (you're in the red until you activate) → Accelerate experimentation cycles (monthly vs quarterly data refreshes) → Preserve variable richness for model training Shubh also previewed our remediation preview capability, which is our compliance impact forecast before data purchase: high-fidelity risk predictions that inform procurement decisions and eliminate feasibility uncertainty. The architecture decision we made early on was to treat compliance and data infrastructure as a unified system rather than separate stacks. When you build compliance as a configuration layer instead of bolting it on afterward, you eliminate the organizational handoffs and waiting periods that create 10+ week delays. Worth a listen if you're thinking about how to operationalize AI in regulated environments! https://lnkd.in/eRiN8J-p

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Can AI actually improve oncology care—without adding complexity for clinicians? On March 24 at 11 AM PDT, I’m sitting down with Dr. Dana Chase from UCLA Health to share how AI is being deployed in real gynecologic oncology settings to support patients and expand access to clinical trials. We’ll walk through two Botco.ai implementations being studied at UCLA: • AskGRACE — a 24/7 AI patient companion for symptom guidance, medication safety, and education • OncoTrial Match™ — AI-powered trial matching that identifies nearby recruiting studies and automates referrals in under 90 seconds We’ll also talk candidly about what it takes to implement AI in oncology—governance, clinician adoption, workflow integration, and measuring real impact. If you care about practical AI in healthcare, this one’s for you. 📅 March 24 | ⏰ 11 AM PDT Join us. Reg below link in comments.

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We’ve never had more health information. And we’ve never had worse access to healthcare. You can ask ChatGPT anything. Symptoms. Side effects. What this might mean. What you should do next. And it’s powerful. Incredibly so. But there’s a ceiling no prompt can break. ❌No treatment. ❌No prescriptions.  ❌No referrals. ❌No licensed professional accountable for what happens next. Answers aren’t care. Meanwhile, the system people actually rely on looks like this. ↳ Weeks to get an appointment. ↳ Five rushed minutes. ↳ Bills you don’t understand. ↳ Costs you only discover after the fact. So people fill the gap the only way they can. They Google. They prompt. They hope. AI gave us knowledge. But access is still the bottleneck. ⚠️That’s the part Lotus is rebuilding. Not an AI doctor. Not a chatbot with disclaimers. And not a replacement for professionals. Lotus is AI powered by real doctors. Doctors who review. Oversee. And take responsibility for care. Which changes everything. ✅Free primary care for all of America. ✅Actual treatment. 🕞 Available 24/7. Diagnose. Prescribe. Refer. ❌No invoices. ❌No insurance games. ❌No surprise charges. And here is the true breakthrough - patients don’t pay. Lotus makes money through premium sponsorships inside the app, not by charging you for care. The experience feels different. It's like having 1,000 doctors in your pocket. Focused on protecting you. Personalized. Always on. ChatGPT can help you understand what might be wrong. 👉Lotus can actually treat you. And that distinction, between information and care... Is the line the future of healthcare will be built on.

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