Brian Dolan

Wow, there's been a lot of AI/healthcare news this week 😅 For today's Endpoints News Health Tech newsletter, I tried to make sense of what ties all the news together: AI is here, and consumers and providers are involving it in the decisions they make about their health. And self-collected data is becoming a bigger piece of that. That's what made the updated FDA guidance on wellness devices so relevant, too. Read (and subscribe to??) the newsletter at the link in the comments ⬇️

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One explanation keeps popping up as to why medical costs are hitting insurers so unexpectedly hard: coding and billing by providers that's been supercharged by AI. In today's Endpoints News Health Tech newsletter, Shelby Livingston breaks down the implications of what's shaping up to be an AI payment war + Ngai Yeung on a new report from SVB that details how health tech investing is deviating from overall healthcare funding https://lnkd.in/gWJKBw3j

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Halving health-inequality gaps starts with interoperable data. Right now, care differs not because of medicine - but because of where data lives. Different systems. Different formats. Different truths. When records don’t move, inequality grows. Interoperability isn’t just a tech win. It’s a social one: A rural clinic sees the same record as a city hospital. Prevention isn’t blocked by postcode. AI works on everyone, not just the well-documented few. Health equity begins when data flows. Health equity begins when data flows. Because connected data saves lives.

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Exclusive: We surveyed prominent #digitalhealth vendors about their biggest challenges selling products to health systems and insurers. For the second year (mind you, we've only done this survey twice) in a row, slow sales cycles was the top challenge. “Because AI is so nascent and the book is still being written, it’s hard for me to internally sell that in a traditional [vendor contracting] model, if we don’t exactly know what we’re going to get.” - Aaron Miri, DHA, FCHIME, CHCIO I hopped on the phone with a few of my go-to guys, Miri, Matthew Kull and Alexander Lennox-Miller, who all agreed: #AI hype or not, health systems are not going to rush through this process. Fair or not, trusted vendors (i.e. Epic) will always have a leg up on new entrants because execs feel comfortable with them. https://lnkd.in/eD6EPktj via Modern Healthcare, Kevin Davis and Tim Broderick on the charts/data

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The hardest conversation in digital health fundraising isn't about your technology. It's about proving your revenue model works in healthcare's unique buying environment. When investors ask for "traction," they're really asking: Can you navigate the 18-month sales cycles, the committee-based purchasing decisions, and the risk-averse culture that defines healthcare procurement? Most health tech founders think traction means pilot programs with impressive user metrics. But healthcare buyers evaluate technology differently from other industries. They care less about your AI capabilities and more about whether you can reduce their cost per patient encounter or improve their HCAHPS scores. The gap between pilot interest and contracted revenue in healthcare is significant. Clinical champions support your solution, but procurement teams seek proof of ROI. IT departments are concerned about the challenges of integration. Finance requires budget justification that withstands annual planning cycles. Real traction in healthcare means demonstrating three things investors understand: Your pilots convert to paid contracts at predictable rates. You've proven your solution integrates without disrupting clinical workflows. Your customers can measure and defend the value you deliver internally. The challenge isn't developing better healthcare technology. It's creating a revenue model that fits healthcare's operational realities. Health systems don't invest in innovation just for the sake of it. They acquire solutions to address specific operational issues that affect their finances or clinical results. Your traction story should reflect this purchasing behavior. The companies that successfully scale in digital health aren't necessarily the ones with the most advanced technology. They're the ones who understand healthcare's unique market dynamics and build their go-to-market strategy accordingly.

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Health IT has spent years trying to fix interoperability. New frameworks. New acronyms. Same frustrations. In my latest HIStalk viewpoint, I look ahead to 2026 and why the industry is heading toward a long-overdue data quality reckoning. Yes, patient data is easier to access. No, it’s not easier to use. Clinicians are still navigating duplicative records, mismatched formats, and information that inspires more caution than confidence. And KLAS Research data shows that broader data sharing still hasn’t led to better data. That disconnect becomes impossible to ignore as AI adoption accelerates. Without accurate, complete patient data, AI doesn’t become transformative. It becomes unreliable. By 2026, the question isn’t how much data is moving through the system. It’s whether anyone can trust it. For PR and marketing teams in health IT, that changes the job: 📊 Back claims with proof, not just strong language 🩺 Show how data quality delivers real clinical value 🧠 Anchor AI messaging in accurate, complete, and reliable data Buyers, analysts, and reporters are asking tougher questions. Aspirational promises won’t carry the conversation anymore. The organizations that break through will be the ones that can point to evidence, outcomes, and responsible use of data and AI, and show exactly how it works in the real world. Supreme Group

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Google’s healthcare AI made up a body part — what happens when doctors don’t notice? When Bryan Moore, a board-certified neurologist and researcher with expertise in AI, flagged a mistake in Google's debut research paper for Med-Gemini, he tells The Verge, the company quietly edited the blog post to fix the error with no public acknowledgement — and the paper remained unchanged. Google calls the incident a simple misspelling of “basal ganglia.” Some medical professionals say it’s a dangerous error and an example of the limitations of healthcare AI. (The Verge) https://lnkd.in/ehiSuZvY

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There's a new question in every health tech buying cycle that didn't exist 18 months ago: "Is this already on the roadmap of our foundation model vendor?" As we discussed in our 2026 healthcare predictions, we are entering a moment where the end user, clinicians and operators included, can increasingly build software themselves using agentic coding environments. In practice, this shows up when a CIO asks whether a proposed point solution is meaningfully different from what their existing foundation model could deliver with internal prompts, guardrails, and a few weeks of engineering time. This shift is quietly reshaping the competitive calculus. Startups selling into healthcare enterprises no longer compete only with the EHR vendor’s roadmap. They also compete with what a foundation model provider can plausibly ship for that customer, often faster and at lower marginal cost. Importantly, while the moat around building software is eroding, the moat around running software in healthcare is not. Healthcare enterprises are not set up to independently handle deep workflow integration, continuous iteration, reliability at scale, and governance across clinical, legal, and regulatory dimensions. That operational gap is where durable companies are still built. We expect this dynamic to introduce real friction into buying cycles for point solutions with thin moats. At the same time, it expands opportunity for companies with defensible data assets, deep workflow entrenchment, and true platform potential. Artera and Atropos Health are two strong examples from Breyer Capital's portfolio, both highlighted in the article. Special thanks to Brian Gormley for featuring our perspective in today's The Wall Street Journal piece on OpenAI and Anthropic's entry into healthcare. Jim Breyer Daniel Breyer Andre Esteva, PhD Brigham Hyde

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ONC/ASTP says more hospitals have baseline patient engagement capabilities. But as policies have pushed these functions forward, have digital health disparities become more pronounced? More of the latest ONC/ASTP data brief here: https://lnkd.in/eHm4dpux

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In this week's edition of the Prompt: the FDA plans to use AI to ramp up reviews and find investigation targets, Meta aims to fully automate ad creation and AI models are becoming too agreeable. https://lnkd.in/gPYvBEWK

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A new AI-powered version of SafetyVista (https://safety-vista.com/) has just been released, now including 2M+ LLM-assisted drug-safety data points from 25K clinical trials and 2K congress abstracts We’ve been busy enhancing SafetyVista, with a laser focus on providing AI-powered high-quality safety data. By blending expert manual review with powerful LLM-assisted analysis, we’ve taken the platform’s depth, precision, and utility to a whole new level. Here’s what’s under the hood: - 2M+ treatment-side effect data points extracted from over 25K clinical trials, now with advanced detection of a few thousand treatment-related events not just co-mentions or reported effects. - 10K+ treatment-side effect data points from over 2K congress abstracts, bringing in the latest discussions from the scientific frontlines. - 500+ pipelines from pharmaceutical and biotechnology companies analysed and curated to deliver actionable, real-time safety intelligence. - 4,600 regulatory agency alerts tracked across 800+ medicinal products, providing a global view of emerging safety signals. With this major expansion, SafetyVista becomes an essential intelligence platform for translational safety. Additional NEW features to power your safety decisions. We’re excited to roll out powerful new tools designed to help you explore safety from every angle: - ADMET tab in Preclinical Toxicology: Quickly explore known and predicted properties related to Absorption, Distribution, Metabolism, Excretion, and Toxicity. - Read-across page: Compare the safety profile of multiple medicinal products side-by-side. Build sets by chemical similarity, shared ATC class, or shared indication. Use it for hypothesis generation, safety signal comparison, and portfolio optimization. - Allosteric affinity data: We now highlight whether a reported affinity value is associated with allosteric binding to the target protein, providing richer context for pharmacological evaluation. What’s Next? We’re not stopping here. Coming soon: deeper integration of real-world evidence and advanced visualizations to help you interpret safety data faster and forecast safety signals earlier. As always, your insights shape the future of SafetyVista. You are welcome to send comments to info@safety-vista.com. #DrugSafety #SignalDetection #LLMs #MLmodeling #pharmacovigilance

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ECRI received dangerously inaccurate information from LLMs when testing them with questions about medical products and technologies, leading the healthcare safety nonprofit to put AI chatbots at the top of its annual list of health tech hazards. "Medicine is a fundamentally human endeavor. While chatbots are powerful tools, the algorithms cannot replace the expertise, education, and experience of medical professionals,” ECRI President and CEO Dr. Marcus Schabacker said. “Realizing AI’s promise while protecting people requires disciplined oversight, detailed guidelines, and a clear-eyed understanding of AI’s limitations.” I've got more at the Medical Design & Outsourcing article in the comments below, including the other nine top hazards of 2026.

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A new DOJ hotspot in 2026 for clinical labs? "One side that we haven’t seen a lot of focus on until recently is Medicare Advantage," says attorney Danielle Tangorre. Read more 2026 compliance predictions in G2 Intelligence. The Dark Intelligence Group Today's Clinical Lab Robinson+Cole Geri Rosman #clinicallab https://lnkd.in/eWBVtXW6

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BREAKING NEWS: The White House & CMS just rallied 60+ tech and healthcare heavyweights—from Amazon and Apple to Epic and leading HIEs—to sign a pledge that promises to closes the “last mile” of interoperability. Expect CMS‑Aligned Networks, app libraries, patient‑controlled FHIR APIs, and digital IDs that replace the clipboard—all rolling out by 2026. https://lnkd.in/gHP2QdMG Why it matters: HIEs must gap‑assess and align now, or watch national networks claim their turf. Game on. #lastmileinteroperability #HealthIT #HIE #DigitalHealth

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