Jared Sieling

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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Healthcare AI adoption is no longer theoretical. It’s operational. This week, OpenEvidence (often described as “ChatGPT for clinicians”) announced a $250M Series D at a $12B valuation, doubling in just two months. But the valuation isn’t the story. Adoption is. Reported metrics: • Used by ~40% of U.S. physicians • 100M+ Americans treated by a clinician using it in 2025 • ~18M physician consultations per month, up from ~3M a year ago • ~$150M annualized revenue with 90%+ gross margins For lab operators, this matters more than it might appear at first glance. Physicians are rapidly normalizing AI as part of clinical decision-making workflows, not as an experiment, but as infrastructure. Once that mindset shifts, expectations shift everywhere downstream. What this signals for laboratories: • Clinicians will expect faster answers, fewer manual steps, and smarter automation from labs • Data latency, manual review, and fragmented systems will become less tolerable • “System of record” alone won’t be enough, systems of action will win • Labs that integrate AI into accessioning, QA, utilization, billing, and client services will compound advantages quickly Healthcare adoption has historically been slow. This is not slow. When 40% of doctors adopt a new workflow in under three years, it’s a clear signal that AI is crossing from early adoption into standard of care. For labs, the question isn’t if AI becomes table stakes. It’s how fast you operationalize it before your customers expect it by default. Curious how other lab leaders are thinking about AI readiness inside their operations.

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700 healthtech startups. One underestimated state. Colorado’s not next. It’s now. ✅ 30+ standouts across AI, virtual care, and diagnostics ✅ Over $1.2B raised in the past year alone ✅ Deep activity from Seed to Series E, IPO, and acquisition SF and NYC still get the headlines. But Colorado is where early signals become category shapers. I mapped the breakout momentum: 21 unicorns and near-unicorns, across AI, virtual care, and diagnostics. It’s how I spot where real signals are forming, and which stories are worth following early. Building your edge? Check out my newsletter for more insights 🔗https://t2m.io/72LJ9BxF

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Prediction: 2026 will be the year we hear a LOT about clinical AI. Last week, we saw Doctronic's autonomous clinical AI announcement in Utah & OpenAI's healthcare announcement. These are both leading indicators of a trend we'll continue to see: more dollars and attention flowing to clinical AI. 2025 was clearly the year of provider operations AI. From revenue cycle tools like Smarter Technologies to AI scribes (that are morphing into clinical decision support) like Abridge, a massive amount of funding followed rapid health system adoption. But, in my view, the "obvious" categories are now saturated and the winners have been anointed. The winners will rapidly expand horizontally to reduce the surface area by which other startups could flank them. And, of course, Epic et al won't ignore this trend. With over 50% of healthcare VC dollars going to AI, VCs will be looking for new categories to invest in. Clinical AI is going to category that will get much more attention. Of course, there's a LOT more issues in this category: first and foremost - patient safety, but also reimbursement, regulation, liability/insurance, and so much more. But, I bet we'll start to see loves of movement in this area. #healthcare #AI #healthtech #digitalhealth

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A Cleveland Clinic AI leader just told Crain's Cleveland Business: "There is no scalable model for deploying, monitoring, and updating clinical AI." I worked inside that network. I know exactly why. 14+ years building infrastructure for health systems — including one of the most complex academic medical centers in the world. What those environments actually look like from the inside isn't what the AI vendors show in their decks. Hundreds of VLANs — clinical, research, guest, and medical device traffic all riding the same campus backbone. Mission-critical apps like Epic and PACS fighting for bandwidth with everything else on the wire. Security segmentation so layered that a simple change becomes a multi-team, multi-week process. Zero tolerance for downtime — which at a 24/7 hospital means always. Now drop AI agents into that. You're asking them to query EHR systems, pull live lab data, and route clinical decisions — in real time — over infrastructure that was designed before AI was a concept. Only 3% of hospital systems have AI agents running in live clinical workflows. That's not hesitation. That's infrastructure reality. 🔍 The gap between "piloting AI" and "AI reliably in production" isn't an AI problem. It's a foundation problem — and most health systems don't even know where to look. The scalable model Cleveland Clinic says doesn't exist yet? It exists. But it doesn't start where most people think. If your clinical AI keeps breaking, are you sure you're diagnosing the right layer? 🌐 #HealthcareAI #NetworkEngineering #HealthcareIT #AIOps

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🚨 $180 per patient per year. That's the new reality of chronic MSK care under CMS' ACCESS model. After 28 years in spine surgery — including time in CMS' BPCI bundled payment pilot and later as a CMO in the bundled payment space — I've seen what happens when Medicare misprices complex care: clinicians adapt, or they get pushed out. The ACCESS model pays a fixed annual amount to manage chronic conditions across four tracks: musculoskeletal disorders, cardiovascular-kidney-metabolic (CKM), and behavioral health. For MSK, that's $180 per patient per year with 50% held back until 12-month clinical targets are met. Less than $20 a month. With HALF contingent on long-horizon outcomes you don't fully control. 💡 What stands out is how explicitly these numbers assume automation and scale. CMS reportedly reviewed dozens of company business models before landing on these rates. The math favors tech-enabled, low-touch programs built around large panels, asynchronous outreach, and standardized pathways. High-touch, clinician-heavy models — the ones that sit with the patient whose "routine back pain" is masking evolving neurologic compromise or complex psychosocial drivers — are going to struggle to survive this rate structure. ⚠️ When we get chronic care pricing wrong, care doesn't disappear — it shifts. The default becomes app-first, remote-only, and protocol-driven. The risk is that the chronic spine patient with early red flags looks identical to everyone else in a dashboard until it isn't. For clinicians, this is the recurring message from value-based care: if you don't redesign your model around these incentives, someone else will — and they may not share your tolerance for the time and nuance complex MSK care requires. 🔍 That same pressure, though, creates real opportunity. Care models that use technology to extend clinical judgment — rather than replace it — are going to be in high demand. The practices and platforms that make the ACCESS math work without sacrificing clinical integrity are the ones worth watching. If you're managing MSK, CKM, or behavioral health populations, do these ACCESS rates support how you actually deliver care or do they force a complete rethink? PhyCap Fund #ValueBasedCare #HealthcarePolicy #SpineSurgery #ClinicalLeadership #HealthcareInnovation #MSK #DigitalHealth

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Bessemer Venture Partners published their Vertical AI playbook this week. One line stood out: "Vertical AI isn't competing for IT budgets. It's competing for labor budgets." This is the thing most healthcare AI companies get wrong. They pitch CIOs and Ops leaders on features and integrations. Meanwhile, practice administrators are staring at a staffing crisis that isn't getting better. Referral coordinators. MAs doing prior auths. Care gap closure teams. These roles cost $45-60K each, turn over every 18 months, take 3 months to train and the candidate pool keeps shrinking. For some organisations, AI means margin improvement. For others, it means finally being able to do the work they couldn't hire fast enough to do. Either way, the conversation isn't "does your AI integrate with our EHR?" It's "can you reliably do work we can't find people to do anymore?" That's a labour economics conversation, not a technology conversation. And it's why operational AI will be a bigger market than clinical AI. You're not competing for the IT line. You're competing for headcount that doesn't exist.

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Tennr just raised $101M Series C to tackle one of the ugliest bottlenecks in healthcare: referrals. Faxed forms. EMR silos. Manual intake. It's a mess, especially in imaging and specialty care, where 1 in 3 referrals fall through. Tennr’s approach? Fix the system without changing provider behavior. What they’re doing right: - Using a referral-specific language model trained on 100M+ documents - No EMR ripouts, no retraining, just automation layered onto existing workflows - Launched the Tennr Network, offering real-time referral visibility for radiologists and specialists - Already processed millions of referrals across hundreds of sites - Revenue has tripled since Series B Backed by a stacked list: a16z speedrun, IVP, GV (Google Ventures), Lightspeed, ICONIQ But here’s what I’m watching: - Can they maintain performance across fragmented EHR and fax-heavy environments? - Scaling orchestration logic across different payers isn’t trivial - At $600M+ valuation, expectations just went through the roof This isn’t just another referral platform. It’s infrastructure. And if they get it right, it quietly unlocks faster care and better outcomes across the entire imaging chain. One to watch. https://lnkd.in/gWM-kp4J

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Hickam's Dictum - the one thing you need to know if you are at HLTH and looking at tech that affects clinical descion making Does this AI tool help me with Type 1 or Type 2 thinking? Type 1: quick, fast, simplest explanation - think attending rapid fire q’s on round - patient vignette during boards - snap judgement AKA Occam’s Razor Type 2: “as many diseases as he pleases” - systems based thinking - broad differential based on the constellation of symptoms - slower, intentional AKA Hickam's Dictum As we think toward implementation, we need to think about how the tech helps or hurts our clinical judgement. Same goes for tech that helps guide treatment. I recently saw a company posting about how their tech doesn’t only give deterioration score but “actionable treatment recommendations at the click of a button” Which - don’t get me wrong - actionable recs are awesome, I’m over sterile deterioration scores without helpful insight But does the “treatment at the click of a button” augment my clinical prowess? Or make me a glorified button pusher? These are important thoughts as we evaluate AI driven tech for inpatient use

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Last week, we were thrilled to be named one of the Top 3 Medtech Startups by LSI. Looking back at our Fireside Chat at the LSI Dana Point Conference, I was joined by Liam J. Burns and John Erbey to talk about analyzing real-world data from multiple data silos in the hospital, to create a holistic view of technology use in the real world. After my recent conversations with leaders in robotic surgery, and in preparation for my panel discussion at the American Hernia Society, I keep hearing how real-world data is more important than ever. Hospitals have been trying for years to measure the value of their robotic surgery programs. With all the new robots (and supportive technologies) coming to market this year, we have had so many potential partners ask the simple question: How can you help me show the value of my product right now? It starts at the site level: unlocking data silos, cleaning and curating data, and using systems science to help identify the optimal value proposition for any product, even before they come to market. We already did this for one of our academic hospital partners and presented the findings at SRS - creating what we believe is the first true clinical and financial value-based analysis of robot vs lap vs open surgery. My next blog piece will dive into that analysis and what it means for the clinicians, and also for industry players who are trying to compete in this challenging market. Read more on LSI's Top 3 Medtech Startups here: https://lnkd.in/eKn4Yktt Watch the full Fireside Chat at #LSIUSA25 here: https://lnkd.in/eetZFEBT Caresyntax Society of Robotic Surgery | SRS Qaelon Medical Roivios, Ltd.

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This is a really great piece on the realities of digital transformation in healthcare. One of my goto discussions with execs, Board members or industry who are always keen to launch rockets to the moon (and I love rockets, by the way), is that no one who is working with broken tools wants to hear about rocket ships. Foundations are critical. You don't buy a house because its got sexy foundations. You more often than not don't even see the foundations. What you do see is the result of poor foundations - crumbling and cracked walls that won't support any additional building. This is EXACTLY the same as digital solutions, in health or anywhere. It is so important to invest in the foundations. Policies, SOPs, Cyber and Information Governance are not sexy. Laptop refreshes and updates are just the price of entry to the game. Working internet and solid network infrastrucure are non-negotiable. So before we ramble on about AI and automation and ambient clinical technology etc., please speak to the people who deliver services and see what is really important to them. Solve that, and you get yourself the credibility and relationship capital to launch many many rockets to the moon and beyond...

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🏥 How Doximity Built a $9.4B company in Plain Sight We just dropped a 4-hour deep dive into one of the most under-the-radar success stories in health tech. In 2010, doctors were still coordinating patient care through fax machines and pagers. While Silicon Valley chased consumer apps, Jeff Tangney spotted something everyone missed. Today, 80% of US physicians are on Doximity. More than half of NPs and PAs, too. Osama Hyder and I spent 50+ hours researching the playbook behind the "LinkedIn for Doctors" — and we're giving you the full story. What we cover: → Jeff Tangney's journey from Epocrates to Doximity → Why Steve Jobs' personal physician refused to switch to iPhone (until it had Epocrates) → How Doximity went from 0 → 1M+ physicians → The COVID explosion: 1M calls/month → 1M calls/day → Their $9.4B IPO and strategic acquisitions (Curative, Amion, Pathway) → The AI pivot: Doximity GPT and the Open Evidence lawsuit Key lessons for health tech founders: - Understand the physician workflow before building - Mass adoption ≠ users will pay Stay Tuned, next month we are releasing a story of another company with so many lessons to uncover Link to the episode in the comments Jeff Tangney Nate Gross, MD Shari Buck Konstantin Guericke Amit Phull Jon Hershon Doximity PMWC - Precision Medicine World Conference Tal Behar

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