Andrej Kostresevic

🌍 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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Hot take: betting or investing in any niche EHR right now (e.g. AI-native EHR for X clinical specialist or X vertical like Longevity) when AI code development exists and only continues to get better with each new model release has to be dumbest pre-AI SaaS era thing an investor can do. Here's why ⤵️ The EHR market is essentially split into 5 categories: #1. Outpatient EHR-enabled private practice (doctor or clinical owned) #2. Outpatient tech-enabled practice (PE, VC, family office owned) #3. Outpatient tech-enabled practice (MBA or bootstrapped owned) #4. Startup enterprise (PE or VC backed) #5. Inpatient hospital VC backed EHRs primarily make their money selling to #2-4, with the Epic's of the world taking most if not all of 5. For #1 there really isn't any true TAM since it's difficult to acquire this customer even when giving the EHR product away for free. But here's what these EHRs are not telling you and why they're all racing to update their API docs. They hope that #2-4 will continue to use their solutions to build with AI vs exiting and vibe coding their own in-house solutions. So when you hear them say their EHR product is safe from AI, that's not true. Right now the Epics of the world are safe but it's customers in categories 1-4 who will 100% vibe code their own solutions. #VibeWithCline #LetDoctorsVibe

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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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Unpopular opinion: Silicon Valley's AI-in-healthcare revolution might be more hype than reality. I just published a contrarian take on Andreessen Horowitz's investment thesis about AI transforming healthcare, and honestly, I'm expecting some pushback. But someone needs to say what many of us in health tech are quietly thinking. The premise sounds compelling: healthcare follows "Eroom's Law" (costs doubling every 9 years) while tech follows Moore's Law (capabilities doubling while costs halve). So naturally, AI should flip healthcare from the bad curve to the good one, right? Not so fast. After digging into the specifics, I found some major blind spots in this thinking: Healthcare isn't just inefficient—it's fundamentally different. The "labor intensity" that VCs want to automate often involves clinical judgment, empathy, and navigating complex human situations that current AI simply cannot replicate. The regulatory environment isn't bureaucratic friction—it's a necessary immune system. When Facebook ships a buggy feature, users get annoyed. When medical AI makes mistakes, people die. The FDA's deliberate processes exist for good reason. Medical data is messier than consumer data. Electronic health records are inconsistent, incomplete, and often documented for billing rather than clinical insights. The clean datasets that powered AI breakthroughs elsewhere don't exist in healthcare. The economic math doesn't add up. Hidden implementation costs, new liability issues, and the need for specialized support staff often outweigh any labor savings. We're seeing cost shifting, not cost reduction. This isn't anti-innovation—it's pro-realism. AI will play important roles in healthcare, but probably through narrow, specialized applications that augment human expertise rather than grand platforms that replace clinical judgment. The most successful health tech companies I've seen understand healthcare's constraints and work within them, rather than trying to impose Silicon Valley's "move fast and break things" mentality on an industry where breaking things can be fatal. Disclaimer: These thoughts and opinions are my own and do not reflect those of my employer or any other entities. I know this is a playful contrarian take against a16z, but I think the conversation needs more voices challenging the groupthink around healthcare AI. What's your take? Are we being too pessimistic about AI's potential, or too optimistic about its near-term impact? Link to the full essay is in the comments below.

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Why most AI healthcare platforms won’t scale. Many AI healthcare platforms will generate impressive insights. Far fewer will survive contact with: - Reimbursement reality - Regulatory scrutiny - Longitudinal clinical accountability The primary failure mode isn’t model accuracy. It is execution without alignment. As care shifts from episodic encounters to continuous management, platforms must govern decisions across time — not just moments. That distinction separates experimentation from infrastructure.

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Healthcare pulled in $65B in VC in 2024. Second only to enterprise software/AI. Nearly double that of fintech. And I don’t think most investors are really clocking that. We all chase AI, right? But capital doesn’t just flow into code. It eventually needs walls. Labs. Real-world infrastructure. We saw this play out post-2021. Developers overbuilt lab space expecting the biotech boom to run hot forever. By 2023, vacancy ticked up—14%, give or take. And people started backing off. But the capital didn’t leave. It reallocated. Diagnostics, AI drug discovery, biomanufacturing... they're all still raising. You’ve got $65B in motion. You think that doesn’t need square footage? I’ve seen this cycle before, especially in healthcare real estate. Capital gets ahead of the buildings—and then the buildings catch up. So yeah. Lab space might feel soft right now. But if you're patient, and positioned, this could be the entry point that pays off for the next 10 years. I’m holding exposure. You?

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Signal's co-founder just launched an AI that actually keeps your secrets. Moxie Marlinspike has launched Confer, a privacy-focused AI assistant that promises something most competitors cannot: conversations that remain truly private. Unlike ChatGPT or Claude, Confer encrypts all interactions using WebAuthn passkeys and processes everything within Trusted Execution Environments, ensuring the host company never accesses your data. Marlinspike's motivation stems from recognizing AI chat as an inherently confessional technology that knows more about users than any previous platform. The architecture is deliberately complex, combining encryption, remote attestation, and open-weight models to deliver on its privacy promise. While the free tier offers 20 daily messages, unlimited access costs $35 monthly - significantly more than ChatGPT Plus. The premium reflects the reality that robust privacy infrastructure comes with higher operational costs. For users concerned about data collection and advertising targeting, Confer represents an intriguing alternative that prioritizes user privacy over profit maximization. Check out the article here: https://lnkd.in/e-3DTjGS If you like this content, consider subscribing to my weekly newsletter where I share 3 key events in the data and AI space, 2 BigQuery tips, 1 thing that piqued my curiosity - completely FREE. Follow the link below to sign up.☟ www.beardeddata.com/signup #Privacy #AI #ChatGPT #DataSecurity

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ICYMI: NVIDIA is betting big in healthcare. And one key company that it has chosen to work with: Verily— a titan in the field of precision medicine. Why this partnership, why now? Because the time has never been better with how deeply AI can make sense of large swaths of data to predict disease earlier and personalize medicine. This is the science and toolset that Verily is working on. This is huge news for the life-sciences world. Read more in the press release below. CC: Janette Ciborowski, Stephen Gillett, Steven Cooper, Trent Norris, Myoung Cha

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We lost a deal to a company with worse AI than ours. Yeah...sucks. But they deserved to win. We did show up with the best model and an impressive demo State-of-the-art stuff. They showed up with: 1. Seamless integration into the existing EMR Software 2. A clear $X saved per month proof 3. Zero disruption to current workflow All 3 points are super critical to get the sale. Our AI was better. Theirs was boring And boring won. Learning....Healthcare doesn’t reward the flashiest tech. It rewards tech that is reliable, proven, and had min friction to adopt. Master the boring parts so well that removing your product becomes unthinkable.

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