Andrew Toy

The UC San Diego Health team led by Karandeep Singh Jeffrey Pan Josh Glandorf Alexander Khalessi, MD, MBA are leading the way in how to thoughtfully and systematically develop and deploy AI to transform healthcare operations, clinical outcomes, and patient experience -- at enterprise scale. They are breaking barriers and writing best practices in real-time as they create an industry-leading program. I guarantee that you will walk away from this session with dozens of practical tips that you can implement in your organization right away, whether you are a Notable customer or not. I know this because I learn something new every time I talk to this team.

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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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If you’re building a health tech startup, here’s a useful perspective from the accelerator side. At ViVE, I spoke with Keith Camhi, Managing Director of the Techstars Healthcare Accelerator powered by Permanente Medicine Mid-Atlantic States. The program invests in early-stage startups and helps founders run pilots with physicians in the Kaiser Permanente ecosystem. Some interesting takeaways: • Techstars still prioritizes “Team, team, team” above everything else; • Founders in the program are interviewed by physicians before acceptance; • The goal isn’t just mentorship — it’s getting real pilots with providers; • AI startups dominate applications, but the focus remains on real clinical problems. Applications for the next cohort open soon, so founders may want to keep an eye on it. Short interview from #ViVE 👇 🎥 Watch here: https://lnkd.in/gNN_5628 #Vive2026 #digitalhealthinsideout #health2tech #digitalhealth

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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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Physicians can be drowning in data yet starved for actionable insights. With our new generative AI capabilities, instead of digging through countless documents, you can now ask Counterpart Assistant (CA) questions about patient medical history in natural language and receive immediate responses with source links, all within a PHI-safe environment. This latest evolution of CA gives physicians back time and clarity by making the patient's story instantly accessible so they can focus on delivering care. This is another big step in how CA is redefining patient care — reducing data overload, streamlining workflows, and supporting high-quality value-based care. Read more below. 🔗 Full Press Release https://lnkd.in/gGrMN7R2

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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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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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𝗩𝗶𝗩𝗘 𝟮𝟬𝟮𝟲 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗗𝗿𝗶𝘃𝗲𝘀 𝗡𝗲𝘅𝘁 𝗪𝗮𝘃𝗲 𝗼𝗳 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗛𝗲𝗮𝗹𝘁𝗵 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 At ViVE 2026 in Los Angeles, 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 emerged as the dominant theme as startups and healthcare platforms showcased tools designed to automate complex workflows across patient engagement, clinical operations, and care coordination. Major announcements included new AI assistants for patient communication and care navigation from athenahealth and b.well Connected Health, workflow automation tools from Luma Health and TigerConnect, and expanded virtual care platforms from Fabric and Wheel. Across the conference, vendors emphasized embedding AI agents directly into clinical systems, patient apps, and operational platforms to automate scheduling, care navigation, referrals, and clinical decision support. From a product strategy perspective, the direction is becoming clearer: 𝗔𝗜-𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲𝗱 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 — 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗶𝘀𝗼𝗹𝗮𝘁𝗲𝗱 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗼𝗼𝗹𝘀. 𝗔𝗜 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝘁𝗼 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, with the greatest value emerging from platforms that integrate deeply with clinical data, EHRs, and care operations.

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Do physicians or AI offer better treatment recommendations? In a recent Cedars‑Sinai study published in the Annals of Internal Medicine, AI-powered virtual urgent care visits demonstrated that “AI Recommendations Were Graded Higher Than Physician Decisions” Well, at least that was the subheadline intended to grab the reader’s attention (and rank higher in SEO results)… But here’s the key. If you read the study and accompanying article, AI didn’t replace the physician and clinical staff — it empowered them. AI was good at certain things, such as identifying red flags (i.e., specific, finite signals or changes in side effects). Physicians were substantially better at understanding the complete patient history and adapting their treatment recommendations as needed. Physicians were the lead, and AI was best playing a supporting role. When AI handles structured intake, ongoing engagement, and data organization, the physicians and the clinical team can focus more energy on the nuanced, human side of care. That means more time available for listening, adapting, and building trust… and, most importantly, making the diagnosis and treatment decisions that really matter—decisions only a human can and should make. The opportunities for AI + clinician collaboration in patient engagement are clear. Use cases are growing every day. For example:  -- Streamlining intake: AI gathers detailed symptom histories in minutes, ensuring providers have a comprehensive picture before the visit begins -- Identify red flags early:Algorithms surface warning signs (e.g., side effects, possible antibiotic resistance) so providers can act faster. -- Support follow‑up care: Automated messaging keeps patients informed about lab results, referrals, and recovery steps—without tying up staff phone lines. -- Reduce cognitive load: By organizing patient data and history, AI helps clinicians spot patterns and make confident, informed decisions. When designed thoughtfully, AI is not a competitor—it’s the ultimate teammate, enabling medical professionals to spend more time practicing medicine and less time chasing data. How could your patient outcomes improve if AI handled administrative tasks and your team focused 100% on care? Cedars‑Sinai article: https://lnkd.in/gfayMimN Annals of Internal Medicine Study (Full Abstract) https://lnkd.in/gKdvcTZR #AIAgents #DigitalCare #HealthcareAutomation #PatientPrep #ThinkneticAI

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Just wrapped-up a demo of Elixir EHR for a large Adult Foster Care (AFC) provider — and it was an energizing conversation. Was very impressed with the executive team at their end. We walked through AFC and HC end-to-end workflows including - Care staff management, Referral Management, Patient care plans, Authorization, AFC caregiver workflows, Service logs, Invoice management, Claims processing and Operational workflows across the platform. What stood out most was the client’s engagement around two themes: A) The strength of Elixir’s core capabilities across clinical, operational, and billing functions. B) The flexibility and customizability to adapt to the unique realities of AFC and homecare providers. I have sat through quite a few demos and have had many in-depth conversations with the executives in Eldercare, Homecare and AFC space. The common theme is that they all have tried a cookie-cutter EHR and RCM before and it doesn't work as you scale. There is tremendous opportunity for innovation in this space — especially in empowering caregivers and providers with AI-assisted documentation, smarter service log validation, automated claims intelligence, and workflow optimization. Homecare and AFC organizations deserve technology that reduces administrative burden and lets caregivers focus on what truly matters: delivering quality care. Excited about what’s ahead and the possibilities of combining Elixir with AI to transform care delivery in this space. #HealthcareInnovation #Homecare #AFC #DigitalHealth #EHR #medicalclaims #RCM #CaregiverEmpowerment #Salesforce #Elixirehr

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How do you turn a photoplethysmogram (PPG) signal into a clinical-grade insight? Our newest white paper explores the science behind Oura’s cardiovascular features, ongoing clinical trials, and advances in PPG and machine learning. Key highlights from the report: Cardiovascular Age: How we estimate arterial stiffness to gauge vascular health.  Cardio Capacity: Moving VO₂ Max from a sports metric to a longevity benchmark.  Women’s Health: How we’re using large-scale, anonymized data to close the research gap in female cardiovascular health. I am Proud of the ŌURA team for continuing to push the boundaries of what is possible with Oura Ring and to advance the scientific understanding of cardiovascular health.

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Early cancer detection saves lives—but most tools are invasive, slow, or used too late. On the latest Inside Startup Investing, I sit down with Ivan Lo, CEO of Breath Diagnostics, Inc., which is building the “Cologuard for lung cancer.” One breath. Ten minutes. A non-invasive platform that can flag early-stage lung cancer—and potentially pneumonia, TB, and more—through VOC biomarkers. What stood out: First-line screening vision: simple breath test before costly scans Speed & sensitivity: minutes vs. weeks; strong early sensitivity/specificity data Regulatory path: pursuing post-op pneumonia first (faster FDA route), then lung cancer Platform play: same tech could support broader disease monitoring If you're interested in early detection, medtech, and platform diagnostics, this is a must-listen.

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Scribes, AI or otherwise, are completely useless in healthcare. You CANNOT document patient encounters with "notes" in language. All you've accomplished is putting paper records behind the glass of the monitor. That's stupid and you can tell that bizdiot Judy Faulkner I said so. You need a data driven EMR that is searchable and relatable. Before you say "mine is" or "AI," no it's not. Sentia has provided a data driven EMR that relates symptoms to observations to clinical measurements to lab results to diagnoses to treatments to outcomes. You can literally pick a list of symptoms and get her back a collection of cases with positive outcomes. You can't do that with language. It's also faster, easier and cheaper than typing "notes." Stop. You are doing it wrong. Start over with a real EMR that is built the way it should have been in the 70s if Judy had a brain in her head. Read this: https://lnkd.in/gR3ArMD9

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