About
Clinician innovator, entrepreneur and optimist. I care about healthcare access, building…
Experience
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Education
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Stanford University
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BioDesign Medical Innovation track
Founding member and President of the Big Ideas Club
Multiple projects with Engineering school including:
Using 3D Printing to Create Personalized Brain Models for Neurosurgical Training and Preoperative Planning. doi: 10.1016/j.wneu.2016.02.081. -
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Imad Ahmed
Rock Health H1 2025 Market Overview: Signals of Maturity The latest market scan from Rock Health is more than a funding recap—it’s a validation of digital health’s evolution from experimentation to execution. H1 2025 Market Overview: Proof in the Pudding Capital is consolidating around performance. With $6.4B deployed across fewer deals, investors are making bigger bets on platforms that show real-world traction. AI-enabled startups raised 62% of all funding, commanding an 83% premium over non-AI peers. This isn’t just hype—it’s a shift in how value is being priced. Provider workflow is the new infrastructure. Clinical and non-clinical workflow solutions drew 55% of total funding. That’s a clear signal: the market is rewarding tools that drive measurable ROI and integrate seamlessly into care delivery. Exits are back—and they’re meaningful. Hinge Health and Omada Health’s IPOs show that tech-enabled care delivery can scale sustainably. Their decade-long journey to evidence-based, AI-augmented models is a blueprint for what’s possible when product, outcomes, and trust align. Ambient documentation and medical reference tools are scaling faster than any tech in healthcare history. Abridge and OpenEvidence are proving that provider trust, intuitive UX, and capital efficiency can unlock breakout growth. These aren’t pilots—they’re platforms. This report reinforces: → Scalable impact requires trust, usability, and validated outcomes. https://lnkd.in/gVhtgK5e Would love to hear how others are interpreting this moment. What’s your capital allocation thesis in H2? #DigitalHealth #HealthTechInvesting #AIinHealthcare #CareNavigation #ReferWell #HealthTechStrategy #VentureCapital #HealthcareInnovation
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Manuel "Manny" Barroso
🚨 The dynamics reshaping healthcare technology appear to be on the verge of reaching dental. Within the next 6–12 months, I believe we’ll start to see similar shifts to those already happening in clearinghouses, where value migrates rapidly between players. In my view, this will begin to unfold more visibly in dental, likely on a larger scale and potentially with new entrants. Sam Altman has often spoken about a winner-take-all model in tech. That philosophy may matter here, because one of the earliest areas of impact could be X-Ray AI. In my opinion, this segment is particularly ripe for consolidation, moving from many vendors to a few dominant ones. OpenAI or XAI are possible contenders, simply because diagnostic imaging is low-hanging fruit for mass consumer adoption of AI in dental. For practices and patients, the product could look “free” under a bundled model, similar to how Amazon Prime works. For the broader ecosystem, the implications could be profound. OpenAI’s growth strategy seems primarily consumer-driven, with healthcare as a natural extension. To illustrate: if 100 million Americans subscribed to an “AI Healthcare Prime” at $20 per month, that alone would represent a $24B annual revenue stream from all healthcare verticals. The economics are staggering. In dentistry, as with the other verticals, the most natural entry point for this strategy is diagnosis and imaging. Once embedded, the downstream effects on workflows, vendor models, and practice economics could be significant. Importantly, this doesn’t require PMS integration, patients could simply photograph an X-ray from any imaging software with their iPhone. From there, the AI handles the rest. I’ve even seen early hints of this in my own offices. The bottom line: this kind of shift feels inevitable. In a winner-take-all landscape, no dental vendor can assume long-term safety. Venture backing offers limited protection when adoption is driven directly by consumers rather than B2B sales. To be clear, I may be wrong, but if I were in Mr. Altman’s position, this is the exact playbook I would consider. Recent hires in consumer and digital health tech at OpenAI, to me, suggest a possible direction of travel. Finally, there are ongoing discussions in the industry about how large AI players could eventually intersect with payers. While nothing is certain yet, that dynamic could add another layer of disruption. Time will tell. In these volatile times, the only real moat is speed. Everything else is noise. #OpenAI #nategross #ashleyalexander #grok #digitaltransformation #AI #RCM
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Ilir Kullolli
Some exciting news! Stanford Medicine Children's Health is partnering with OpenAI as a launch partner for the ChatGPT for Healthcare program. Together, we will leverage AI to transform patient care, streamline operations, and improve clinical workflows. https://lnkd.in/gvxpEQDb Tanya Townsend MSMI, CHCIO, CDH-E, LCHIME Brendan Watkins Keith Morse
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Lisa Anderson
KLAS just told three health systems they'd been measuring AI ROI wrong for the first three months. Not the AI. The clock. Three health systems deployed Suki's ambient AI platform across ambulatory and ED settings. KLAS Research, the independent firm that audits health IT vendor performance, went back and measured what actually happened over time. Documentation time dropped 21 to 27 percent. Coding accuracy improved enough to add over a thousand dollars in revenue per provider per month at some sites. None of that showed up in week one. I ran an AI Care Assistant rollout where the 30-day number was 20 percent automated outreach, with bugs we were still chasing down. Some members were getting contacted too often and said so. By month three, automated outreach hit 30 percent, the target we had set going in. We hit the number. Leadership pulled the plug anyway, because the friction complaints had become the louder signal in the room. Here's what that taught me. Extending the measurement window to three months does not automatically fix the problem. It just changes which number gets to be the loudest at the meeting. If you do not decide in advance which signals actually get a vote, whatever spikes loudest in the room wins, regardless of whether it was the metric you set out to hit. If you are running an AI rollout in any regulated industry, the real fix is deciding upfront which three or four signals determine success, and agreeing now that a spike in one of them does not override the rest without a real conversation. What's the metric that quietly killed an AI rollout at your org, even after the official target was hit? Follow for more on how health IT leaders separate signal from noise in AI rollouts. #healthit #operationalexcellence
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Sean Duffy
Another milestone quarter for $OMDA. 📈 Revenue up 49% YoY to $68M 👥 831K members, up 53% YoY 📊 GAAP Net Loss narrowed to $3M 💫 First quarter of positive Adjusted EBITDA ($2M) Today we also announced significant steps forward in both AI capabilities and GLP-1 care options, including medication prescribing. These innovations reflect our commitment to delivering outcomes for members and cost-savings for customers on our mission to Bend the Curve. This year and next is truly the "year of the G's" - GLPs and GPTs - as we continue building the future of between-visit care. Read more about our Q3 performance, including a reconciliation of GAAP to Non-GAAP measures: https://buff.ly/hpBKj3p
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Stephen Macharia
🌍 Proof of Concept: Standardizing Oncology Care with FHIR. I'm excited to present Oncology Implementation Guide;a proof of concept developed to demonstrate how HL7® FHIR® standards can streamline oncology care. This guide provides a structured framework for capturing, managing, and exchanging oncology data—spanning prevention, diagnosis, treatment, and follow-up—enabling interoperability across healthcare systems. Explore the guide here: https://lnkd.in/ggMugMUj #Kenya #OncologyCare #FHIR #DigitalHealth #HealthTech #Interoperability #CancerCare #ProofOfConcept
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Joshua Liu, MD
KLAS spoke with 35 Epic customers on what they think about Epic’s foray into AI scribes. Here were 5 interesting takeaways (courtesy of Becker’s) and my thoughts on them: 1️⃣ Some health systems expect Epic to deliver the same value as third party AI scribes, with better integrations and at a lower cost → This seems like an unreasonable expectation to me. The third parties will provide way more white glove change management support, support a broader diversity of specialty-focused customizations and manage edge cases way better. → I do agree with better integrations and a lower price point though - but you also get what you pay for - e.g. no white glove change management service, lower professional services costs. 2️⃣ Some health system executives are frustrated - they felt Epic endorsed some of the third party AI scribes and were caught by surprise that Epic developed scribing anyways → While some folks might point to how Epic often supports other third party products and then competes with them over time and perhaps this was foreseeable - I have empathy for health systems because this time was technically kind of different. → Epic Workshop and co-development with companies like Abridge - including supposedly getting warrants or shares in the company (before supposedly selling them earlier this year) - was very, very new. → I would guess that these Workshop partners were promoted more significantly than previous product partners, and now that the messaging might be changing, health systems were caught off guard. 3️⃣ 54% of health systems were Very Likely or Likely to choose Epic’s AI scribe within 2 years - with better integrations as the primary reason → The vast majority (85%) of the respondents to the KLAS survey do have some AI scribe already. So roughly speaking, ~50% of health systems who already have an AI Scribe are telling themselves they’ll switch to Epic’s AI scribe within 2 years. → I would be surprised if that many successfully, fully switch to Epic’s AI scribe within 2 years (especially since the scribe won’t come out for another 6-12 months), but we’ll see. 4️⃣ Health systems already on Microsoft (I assume Nuance DAX) were more likely to believe they’ll just move to Epic’s AI scribe → Not surprising at all - in their minds, the future Epic AI Scribe is basically just the current Microsoft/Nuance product. 5️⃣ While most health systems expect Epic will be their exclusive AI scribe long-term, some expect to need specialty-specific tools → It may be true that purpose-built AI Scribes for specialties with a lot more complexity and edge cases (e.g. oncology) will still be something Epic won’t touch. Personally I don’t think these answers are all that meaningful. Everything hinges on: → The quality of AI scribe that Epic releases and whether self-serve works → How much of a lead in product development the third party AI scribes build between now and then What do you think?
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