Stephen S. Hau

Build for the 3 Waves of Innovation in Healthcare Healthcare is poised for a dramatic shift. It's not just incremental change; we're talking about a fundamental rewrite driven by three converging waves of innovation. Imagine the landscape of healthcare being completely reshaped, leaving no aspect untouched. This isn’t a gradual evolution; it's a rapid transformation. The 3 Waves of Innovation AI is accelerating everything from diagnostics to personalized treatment plans at an unprecedented pace. It's like having a supercharged research and development team at your fingertips. Simultaneously, Web3 is laying the groundwork for secure, decentralized data, effectively putting patient records back into the hands of the individual, much like how you built your businesses by empowering your teams. Finally, Biotech is pushing the boundaries of what's possible in human health, from gene editing to regenerative medicine. This convergence isn’t just adding up; it’s multiplying, creating a new paradigm for healthcare delivery. It's akin to identifying and capitalizing on a market disruption before anyone else. Healthcare access will be redefined, with remote diagnostics and personalized medicine reaching underserved populations. Professionals will evolve, with AI handling routine tasks, allowing them to focus on complex cases. Data-driven decisions will become the norm, with predictive analytics anticipating health crises before they occur. Picture a world where healthcare is proactive, not reactive. It's like moving from reactive problem-solving to proactive strategic planning, a skill you’ve honed over your career. A Builder’s Market We're moving beyond the Information Age, where data was king. Now, intelligence is the driving force. This shift will democratize knowledge and empower individuals and small teams to innovate, much like your own journey in building successful businesses. Millennials, with their unique perspectives, will lead this transformation, shaping healthcare for generations to come. This transition will be dynamic, and we need to be prepared. Elevate Health 2050 is designed to help navigate this change, providing the insights and connections needed to capitalize on these emerging trends. It's about staying ahead of the curve, something you understand well. How will these three waves of innovation impact your role in healthcare? What opportunities do you see emerging? Join the conversation and explore the possibilities of the Intelligence Age at the Elevate Health 2050 substack: https://bit.ly/4efm4BE Let's discuss and learn together. Join our community and discover how to leverage these innovations.

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New episode live! I sat down with Mezue Eneh on K IN KLARITY to explore the intersection of AI and healthcare—from strategy to bedside. We discuss why successful AI starts with data management, privacy, and security, how AI governance can augment human decision-making, and the hard realities of interoperability between consumer wearables and medical-grade systems. We also dig into the role of education for clinicians and leaders navigating this shift, and close with a call for ethical oversight so AI delivers a net-positive impact on humanity. If you’re building, buying, or regulating healthcare AI, this conversation offers a pragmatic blueprint for doing it responsibly. Watch here: https://lnkd.in/eqxcZfUv #AI #Healthcare #DigitalHealth #Ethics #DataGovernance #AIGovernance #Interoperability #MedTech #HealthIT

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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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This week feels like a real inflection point for AI in healthcare—not because of a single breakthrough model, but because the ecosystem is finally ready. After more than a decade working at the intersection of health and tech, I can’t recall another moment when capability, incentives, and trust felt this aligned. We’ve long known healthcare is massive and structurally broken, and that health information is one of technology’s most valuable use cases. The constraint was never opportunity—it was readiness. For the first time, patients and health systems seem prepared to absorb AI in ways that are genuinely useful, responsible, and scalable. With launches spanning both consumer and enterprise, OpenAI isn’t just shipping products; it’s signaling a long-anticipated “slowly, then all at once” shift. I entered healthtech through Google more than a decade ago, working on healthcare knowledge cards in Search, early symptom-to-condition frameworks, and Google’s first HIPAA-compliant product. Even then, the signal was clear: when people have health questions, they turn to tech first—often before they ever touch a health system. Health queries made up roughly 20% of search traffic, yet users were frustrated, forced into fragmented, sideways searches to assemble answers. We even embedded telemedicine directly into Search. Despite strong teams and deep resources, the product ultimately failed. That experience reinforced a lesson that has guided me ever since: insight and talent aren’t enough. Without the right timing and ecosystem, even the best ideas stall. That lesson shapes how I invest at Lux Capital today. We back founders who aren’t just improving access to information, but expanding access to care itself—reducing friction, earning trust, and driving outcomes where the system is hardest to move. What’s different now is that the excuses are gone. Interoperability is real, with most U.S. hospitals exposing FHIR APIs and major EHRs supporting production integrations at scale. Clinical work is digitized enough for AI to live inside daily workflows. And the economic pressure is acute: administrative overhead still consumes 25–30% of spend, margins remain razor-thin, and clinicians generate nearly twice as much EHR data per patient as they did a decade ago. The result is a clear shift from pilots to purchases. We’re seeing rapid enterprise adoption of platforms like Abridge and OpenEvidence, justified by hard metrics—time returned to clinicians, capacity unlocked, and decisions made faster and better. Health systems aren’t adopting AI because it’s exciting; they’re adopting it because it’s one of the last levers left to keep care delivery viable. Healthcare is complex and regulated for good reason. But what stands out right now is alignment. Builders, clinicians, and operators aren’t chasing AI for its own sake—they want tools that work in the messiness of real care delivery. This feels like a genuine step function in both capability and relevance.

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🔥 This is an interesting read, check out Bessemer Venture Partners report on"The Healthcare AI Adopion Index", where they surveyed more than 400 leaders across Payer, Pharma, and Provider segments to understand how healthcare buyers are approaching GenAI applications 🌐 See link for further details: https://lnkd.in/gf6t5jjH 🤖 AI adoption is accelerating 🏥 60% of execs report AI budgets outpace IT spend Only 30% of AI pilots reach production: due to issues relating to security, data readiness, integration costs, and limited in-house expertise. 🚀 Startups have significant growth opportunities ahead: only 15% of AI projects involve vertical AI applications 🤝 Trust and outcomes matter most—procurement is shifting toward co-development particularly with those startups that show clear and attributable ROI. ⚕️ The state of AI adoption today AI as a strategic priority for healthcare and they’re backing with investment. 📈 Survey: 95% said GenAI will be transformative, with 85% of Provider and 83% of Payer leaders expecting it to reshape clinical decision-making within three to five years. 📊 84% believe AI will impact clinical decisions, 80% expect it to reduce labor costs through automation, and many see the potential for revenue gains. 💊 57% of Pharma executives believe AI will drive most new therapy discoveries in the next decade, reflecting the complexity and long timelines of drug development. 📊 AI Dx Index To help AI startups prioritise healthcare use cases, the report outlines the AI Dx Index, based on survey data. ✨ Opportunity Score: Measures pain and manual effort ✨Adoption Score: Tracks where organizations are on the adoption curve using weighted averages across development stages, from "Not Yet Started" to "Full Rollout." ✨Development Strategy: Shows who’s building solutions—startups, incumbents, or internal teams , it's the case of build, buy or borrow 🚀 The report provide some practical tips to founder, including 1. Pick the right entry point and expand 2. Prove ROI quickly and navigate the POC trap 3. Shift from traditional sales to co-development 4. Reimagine a complex workflow end-to-end 5. Align your business model to the value you create Leave a comment or tag a startup founder you think this might be useful. #artificialintelligence #ai #drugdiscovery #drugdevelopment #lifesciences #biotech #pharmaceutical #innovation #healthcare #investment #founders #startups 👍Like, 🔄Share ⌨Comment and 🔖Tag a friend

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Is your digital health strategy strengthening rural systems—or quietly draining them? The Executive Quick Start Guide outlines how to build rural first regional digital health networks that: ✅ Expand specialty access without exporting patients ✅ Use telehealth, eConsults, and RPM with clear escalation rules ✅ Align multiple organizations around shared outcomes Designed for leaders responsible for regional alignment, not one-off pilots. 📘 Explore the guide https://lnkd.in/gNTPnxMn #RegionalHealth #HealthcareStrategy #RuralHospitals #Telehealth #PopulationHealth #DigitalHealth #RuralHealth #HealthcareLeadership #eConsults #RemotePatientMonitoring #SpecialtyCareAccess

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𝗪𝗵𝗮𝘁’𝘀 𝗼𝗻𝗲 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄 𝗻𝗲𝗲𝗱𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁… 𝗯𝘂𝘁 𝘆𝗼𝘂 𝗸𝗲𝗲𝗽 𝗽𝘂𝘀𝗵𝗶𝗻𝗴 𝗱𝗼𝘄𝗻 𝘁𝗵𝗲 𝗹𝗶𝘀𝘁? That “non-urgent” task might be the very thing that sabotages your next milestone. George C. Pappas, and I unpacked how operational risk becomes investor risk—and why HealthTech solutions often crack in real-world systems, not pilot demos. The scary part? Sometimes the team doesn’t even see it coming. Until the breach. Until the downtime. Until the adoption stalls. Drop your biggest ops pain point in the comments👇 𝘓𝘦𝘵’𝘴 𝘵𝘢𝘭𝘬 𝘢𝘣𝘰𝘶𝘵 𝘸𝘩𝘢𝘵’𝘴 𝘶𝘯𝘥𝘦𝘳 𝘵𝘩𝘦 𝘴𝘶𝘳𝘧𝘢𝘤𝘦—𝘢𝘯𝘥 𝘩𝘰𝘸 𝘵𝘰 𝘧𝘪𝘹 𝘪𝘵 𝘣𝘦𝘧𝘰𝘳𝘦 𝘪𝘵 𝘣𝘳𝘦𝘢𝘬𝘴. #DigitalHealth #HealthcareLeadership #FoundersCommunity #RiskToResilience __________________ Liked this post? Want to see more? 🔔 Ring it on my profile Follow hashtag #ProvidersEdge #PulsePointPath 📲 Connect with me

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Can healthcare be an AI early adopter rather than the laggard we tend to expect? These results from 700+ American executives, drawn from a survey by Menlo Ventures and Morning Consult, suggest that healthcare providers are adopting AI at more than twice the rate of other sectors -- and payers / insurance plans even more enthusiastically. Presumably this encompasses any type of technology, at any depth of deployment, but it's a striking signal nonetheless. (Sorry if you've seen this chart previously: it was published just over a month ago, but I'm new to it.) I wonder what a similar survey in the UK would reveal. Perhaps I'd be surprised, but I strongly suspect that healthcare would be a laggard. Now of course there are all sorts of reasons for the difference between the UK and US including, for example, competitive pressures and different financial structures. But I'm pretty sure that attitudes to innovation are as important as these structural differences. As one (British) executive who has worked in a US healthcare system for decades said to me this week: 'Americans are so optimistic they can sound stupid to British ears; whereas the British think it's clever to always be skeptical'. Source: https://lnkd.in/eKj2h7GH

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𝗕𝗶𝗴 𝗧𝗲𝗰𝗵'𝘀 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗛𝗲𝗮𝗹𝘁𝗵 𝗔𝗜 𝗥𝗮𝗰𝗲 𝗜𝘀 𝗡𝗼𝘄 𝗙𝘂𝗹𝗹𝘆 𝗨𝗻𝗱𝗲𝗿𝘄𝗮𝘆 In the first quarter of 2026, every major AI platform launched a healthcare product:  • 𝗢𝗽𝗲𝗻𝗔𝗜: Two-track strategy — ChatGPT Health for consumers, connecting medical records and wellness apps for personalized guidance, and ChatGPT for Healthcare, an enterprise HIPAA-compliant workspace for clinicians and administrators. Cedars-Sinai, HCA and Boston Children's are already adopting the enterprise version.    • 𝗔𝗺𝗮𝘇𝗼𝗻: Expanded its Health AI assistant to its website and app, built on One Medical's clinical foundation, with capabilities including prescription renewal management.    • 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰: Unveiled Claude for Healthcare, enabling access to medical data from HealthEx and Function alongside tools designed to automate repetitive workflows for healthcare professionals.    • 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁: Launched Copilot Health, aggregating records from over 50,000 US hospitals and data from more than 50 wearable devices into a single personalized health companion. The scale of demand is real. Microsoft alone handles over 50 million health questions daily. Each player is competing for the same position: the consumer's first stop for health information and navigation. From a product strategy standpoint, the differentiator is becoming health context: 𝗪𝗵𝗼 𝗼𝘄𝗻𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲, 𝗹𝗼𝗻𝗴𝗶𝘁𝘂𝗱𝗶𝗻𝗮𝗹 𝗽𝗶𝗰𝘁𝘂𝗿𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿'𝘀 𝗵𝗲𝗮𝗹𝘁𝗵 Worth noting: 𝗻𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗮𝗿𝗲 𝗛𝗜𝗣𝗔𝗔-𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝘁. As consumer platforms, they fall outside HIPAA's scope. Data protection is governed by company policy, not by statute. As patient health data flows into these platforms at scale, data governance becomes as important a strategic question as the technology itself. For health systems and payers, this is not just a technology story. 𝗧𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗵𝗮𝘃𝗲 𝗵𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹𝗹𝘆 𝗼𝘄𝗻𝗲𝗱 𝘁𝗵𝗲 𝗽𝗮𝘁𝗶𝗲𝗻𝘁 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗻𝗼𝘄 𝗵𝗮𝘃𝗲 𝗻𝗲𝘄 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻 — 𝗮𝗻𝗱 𝗶𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗵𝗲𝗮𝗹𝘁𝗵 𝘀𝘆𝘀𝘁𝗲𝗺. How are you thinking about the implications for patient loyalty and engagement strategies? 𝘚𝘰𝘶𝘳𝘤𝘦𝘴: 𝘍𝘪𝘦𝘳𝘤𝘦 𝘏𝘦𝘢𝘭𝘵𝘩𝘤𝘢𝘳𝘦 | 𝘔𝘪𝘤𝘳𝘰𝘴𝘰𝘧𝘵 𝘊𝘰𝘱𝘪𝘭𝘰𝘵 𝘏𝘦𝘢𝘭𝘵𝘩 𝘍𝘪𝘦𝘳𝘤𝘦 𝘏𝘦𝘢𝘭𝘵𝘩𝘤𝘢𝘳𝘦 | 𝘈𝘮𝘢𝘻𝘰𝘯 𝘏𝘦𝘢𝘭𝘵𝘩 𝘈𝘐 𝘍𝘰𝘳𝘵𝘶𝘯𝘦 | 𝘖𝘱𝘦𝘯𝘈𝘐 𝘊𝘩𝘢𝘵𝘎𝘗𝘛 𝘏𝘦𝘢𝘭𝘵𝘩 March 16, 2026 #𝘋𝘪𝘨𝘪𝘵𝘢𝘭𝘏𝘦𝘢𝘭𝘵𝘩 #𝘊𝘰𝘯𝘴𝘶𝘮𝘦𝘳𝘏𝘦𝘢𝘭𝘵𝘩 #𝘏𝘦𝘢𝘭𝘵𝘩𝘤𝘢𝘳𝘦𝘈𝘐 #𝘏𝘦𝘢𝘭𝘵𝘩𝘤𝘢𝘳𝘦𝘚𝘵𝘳𝘢𝘵𝘦𝘨𝘺

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Hot take: VC backed AI-native medical practice is just rebranded tech-enabled medical practice with an AI assistant marketed as an AI doctor. Why is this the case? Because there’s really only one way to offer direct care services to consumers in the U.S. in all 50 states “legally,” while navigating licensure, billing, CPoM, and employment laws or regulations. We learned this week why tech-enabled care delivery failed, yet here we are again using the same foundational structure with new AI technology expecting different results. 🫠 We also know that these models require employing TONs of expensive engineers, designers, PMs, QA, etc meaning they’ll have to make concessions on clinical talent which is also expensive. So they’ll most likely choose to hire 1099 and we already know the average panel size that these clinicians can manage with a D2C or tech-enabled option. MSO-PC: 1900 panel size for a 1099 (not full-time) contractor - there are STRICT employment laws on this! So that number is very difficult to increase without risking compliance. Full-time: 2500 panel size even with monitoring chat or as the human in the loop for queue systems which can average 40 cases a day. In comparison if we allowed each individual doctor in the U.S. to create and manage their own AI doctor twin the panel size could be ♾️ since it’s not bound by the MSO-PC structure limitations and can be managed locally (safer) where patients still have access to doctors in-person if necessary. tl;dr - AI doctor companies marketing that AI doctors solve the doctor shortage problem is a lie. Doctors still require oversight and governance and in order to provide care delivery services across all 50 states it requires a standard MSO-PC entity structure. MSO-PCs have several limitations with employment, licensure, and compliance which keeps the AI doctor from ever offering services autonomously. #VibeWithCline #LetDoctorsVibe

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From a pure investing standpoint, the future of digital health feels uncertain. Mainly because even our most valuable private companies are small relative to non-health tech. We have no SpaceX, OpenAI, Stripe, Canva, Anduril, Databricks or Wiz-sized pending exits. And getting analyst coverage from the larger investment firms will likely become more difficult for small-cap stocks ($500-$2B market cap). There's lots of reasons to build a healthcare company. I hope the founders are not primarily driven by financial returns, but the investors certainly are (and should be given their jobs). As assets under management (AUM) rises, investors need higher ownership targets and/or higher exit valuations. Nowadays, people aspire to $100B outcomes as the plan. Hence why VCs can invest so much in private rounds. But $100B is higher than the combined market cap of entire digital health industry that is publicly traded. So founders, it may be better to build a business and cap table that works with a $100-500M exit (aka: don't raise too much; and yes I know $100M-$500M is still a massive company, but that's the venture game). Anyways, we need an outlier winner. Tech investors talk about Instacart as a decent / pretty good outcome and that company is worth $12B right now. Where am I missing the forest from the trees?

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It had to be said! 🎯 Epic's stranglehold on healthcare IT has stifled innovation for too long and the writing is on the wall. The consolidation myth needs to die already. We were promised efficiency and lower costs, but what we got was bloated bureaucracies that prioritize profits over patients. The data doesn't lie: smaller, agile providers consistently outperform these healthcare monopolies on both outcomes and patient satisfaction. Time for a major reset in how we think about healthcare delivery.

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This technology is reducing burnout and improving productivity, an important use case for AI adoption in healthcare. The opportunity is large ($11bn Bull Case TAM) but competition is fierce—enhancements in coding and delivering ROI are key. M&A is ripe amid upstream push from revenue cycle firms.

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