Kate McGinley, ACHE

Healthcare stakeholders face critical challenges with Gen AI implementation. Despite significant investment and hype, enterprise Gen AI in clinical settings remains fundamentally challenged by reliability, accuracy and explainability - right now, not in some distant future. When clinical outcomes are on the line, "prompt and pray" approaches aren't acceptable. Healthcare stakeholders find themselves caught between unreliable AI that cannot be trusted in clinical settings and rigid, hard-coded solutions that cannot adapt to the variability of patient care. As highlighted in a thought-provoking article by AI21 Labs Labs co-founders Ori Goshen and Yoav Shoham, what is required for Gen AI implementation at scale is a shift from "AI potential" to "guaranteed AI performance" through: 1. **Predictability**: Clinical decision support systems that deliver consistent results 2. **Transparency**: Clear visibility into how AI reaches conclusions 3. **Cost Control**: Performance within defined budget constraints The concept of Quality Level Agreements (QLAs), they lay out, is particularly relevant to healthcare, where specifying requirements and knowing AI will consistently deliver could transform implementation. Healthcare deserves AI systems that reliably process medical records, accurately identify drug interactions, consistently recommend evidence-based interventions, and seamlessly integrate into clinical workflows—all while maintaining the highest standards of patient safety. The future of healthcare AI isn't about flashy capabilities—it's about guaranteed performance where it matters most: patient care. Recommended read -> https://lnkd.in/dgdwR4A3

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The Mayo Clinic (the world’s top hospital) just announced a new cohort of 11 AI HealthTech start-ups for its Mayo Clinic Platform_Accelerate program. I wrote down a few thoughts. 1️⃣ 𝐀𝐥𝐦𝐨𝐬𝐭 𝟔𝟎 𝐀𝐈 𝐬𝐭𝐚𝐫𝐭-𝐮𝐩𝐬 𝐡𝐚𝐯𝐞 𝐣𝐨𝐢𝐧𝐞𝐝 𝐭𝐡𝐞 𝐩𝐫𝐨𝐠𝐫𝐚𝐦 𝐬𝐢𝐧𝐜𝐞 𝟐𝟎𝟐𝟐 The Mayo Clinic Platform_Accelerate program launched in 2022 with its first cohort. Last week, the sixth cohort was announced: ▪️Cohort 1 (2022): 4 AI start-ups ▪️Cohort 2 (2022): 7 AI start-ups ▪️Cohort 3 (2023): 12 AI start-ups ▪️Cohort 4 (2023/24): 9 AI start-ups ▪️Cohort 5 (2025): 15 AI start-ups ▪️Cohort 6 (2025): 11 AI start-ups Participating companies gain access to Mayo Clinic experts in regulatory, clinical, technology, and business domains, along with leading investors and healthcare executives. 2️⃣ 𝐓𝐡𝐢𝐬 𝐲𝐞𝐚𝐫’𝐬 𝐟𝐨𝐜𝐮𝐬: 𝐌𝐞𝐧𝐭𝐚𝐥 𝐡𝐞𝐚𝐥𝐭𝐡 / 𝐧𝐞𝐮𝐫𝐨, 𝐨𝐧𝐜𝐨𝐥𝐨𝐠𝐲, 𝐚𝐧𝐝 𝐂𝐚𝐫𝐞𝐎𝐩𝐬 CareOps, a non-clinical AI category, remains a lower-hanging fruit. It doesn’t require FDA or CE certification, addresses a huge unmet need (with about 50% of HCP time spent on admin tasks), and doesn’t compete with healthcare professionals, making adoption easier. On the clinical side, Mayo Clinic Platform is turning its attention to more complex areas such as mental health and oncology, moving beyond more mature ones like radiology AI. AI delivers its greatest value when things are complex. In mental health and oncology, diagnosis and treatment often rely on multi-modal data, which is exactly where AI can have the biggest impact. 3️⃣ 𝐏𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐯𝐞 𝐡𝐞𝐚𝐥𝐭𝐡 𝐚𝐧𝐝 𝐭𝐡𝐞𝐫𝐚𝐧𝐨𝐬𝐭𝐢𝐜𝐬 𝐚𝐫𝐞 𝐚𝐥𝐬𝐨 𝐢𝐧 𝐟𝐨𝐜𝐮𝐬 Three of the 11 start-ups focus on data-driven prevention: ▪️MyAtlas: AI + wearables for early mental health crisis prevention ▪️Kanjo: Early, personalized AI care for ADHD and autism ▪️PromedAI: At-home AI screening for general preventive health Two start-ups are entering the emerging theranostic space, a treatment strategy that combines diagnostics and therapeutics: ▪️MedLink Global: AI-driven precision psychiatry for diagnosis and personalized treatment ▪️Bowhead Health: Imaging-based genomic mutation prediction for cancer 4️⃣ 𝐌𝐚𝐲𝐨 𝐂𝐥𝐢𝐧𝐢𝐜 𝐚𝐬 𝐚 𝐛𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐟𝐨𝐫 𝐚𝐥𝐥 𝐨𝐭𝐡𝐞𝐫 𝐡𝐨𝐬𝐩𝐢𝐭𝐚𝐥𝐬 Mayo Clinic has invested heavily in its Mayo Clinic Platform, building a structured data foundation for both internal and external innovation. CEO John Halamka, a visionary and tech-savvy healthcare leader, drives this initiative. Through programs like Platform_Accelerate and Mayo Clinic Ventures, the Mayo Clinic is acting as both an accelerator and a venture investor, redefining what a modern hospital can be. ------- Make sure that you attend HLTH Europe next year in June in Amsterdam, where John Halamka and the Mayo Clinic are often attending. Follow HGM Advisory for daily reports of the most relevant #HealthTech news.

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Sunday hot take: We won't see AI impact clinical care until we have more physicians as builders. The debate on technical vs non-technical or how technical shouldn't build without clinical is so tired. Because right now the entire focus should be on how to make non-technical CLINICAL technical. And no, not technical in the sense of retraining or understanding how to code. But technical in the sense that they pair program with AI directly within clinical applications and care delivery environments. EVERYTHING meant for clinical should be built this way, from scientists in labs, to doctors in rural health care settings. Builder first, not just user or buyers. ------- Hi, I'm Dr. Mario Amaro, Founder and CEO at Cline and we're working on something new that will finally help empower doctors to transition from being "just buyers" to becoming BUILDERS. Follow us to learn more. #HealthcareOnLinkedIn

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In 2020, Ron Li , Steve Asch, and Nigam Shah published a multidisciplinary framework for AI delivery science in healthcare (recently shared by Jan Beger). Five years later, I'm seeing a clear pattern: organizations that treat governance as a design principle, not a gate check, are consistently outpacing their peers. Why it works: embedding governance early reduces costly rework cycles that stall releases and drain budgets. While competitors navigate months-long review loops, teams that are compliant by design ship the next iteration. The playbook I keep seeing succeed: - Inventory all sensitive data via AI-driven discovery - Categorize models early under EU AI Act tiers, FDA SaMD rules, HIPAA - Embed privacy-preserving tech — redaction strategies, differential privacy, synthetic data, continuous monitoring, and remediation previews — wherever sharing is constrained The 2025 debate isn't whether to invest in AI governance. It's whether you'll use it to pull ahead of teams still bolting compliance on at the end. What governance tactic has saved your team the most time? I'd love to hear your thoughts and examples. Jan Beger, GE HealthCare, Ron Li , Steve Asch, and Nigam Shah

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Truly grateful for the discourse around this subject earlier this week and helpful comments (https://shorturl.at/YfzFo), some dot-dash ideas around commercial ideas began to percolate. 🚨 New commercial models needed for health data marketplaces
👇 SWIPE TO DISRUPT HEALTHCARE’S DATA SWAMP Amazon turned books into a $1.6T empire. The NHS’s 65M patient records could do the same – if we stop thinking like bureaucrats and start acting like platform giants. Clearly not suggesting health and books are the same, but we need a new lens to view the acceleration of health data marketplaces. 🔥 Your diabetes data as a Health Utility NFT 🤑 (Yes, Singapore’s doing this!)
→ NHS takes 10% equity in YOUR healthtech unicorn 🦄 (Israel’s Sheba Medical made $2B this way) 📈 INTERESTING STATS: •          Estonia’s genomic NFTs paid citizens LAST YEAR •          Kenya slashed insurance fraud substantially via API marketplaces •          The FDP as one of the key building blocks. #HealthDataRevolution #NHSUnlocked #PublicPrivatePower #HealthNFTs #AIForGood Reading material: ·     Some initial musings: marketplaces, bazaars and exchanges - https://lnkd.in/dgx7Vhja ·     From Musings to Action: Building a Marketplace for Health Data and Analytics (part 2) - https://lnkd.in/d_ZuVcWr ·     Stretching the musings on marketplaces in DDaT - https://lnkd.in/dqtKSEcx ·     And the most recent, which we had a vibrant discussion on - https://lnkd.in/d4ySHJAY 🔥 🔥

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