James Green

Some VC investors believe that brick and mortar - referring to a physical presence of some kind - should be avoided at all costs. Yet, the track record of clinic and hybrid care businesses in health care indicates otherwise. ==> One Medical (sold for $3.9 billion to Amazon), Oak Street (bought by CVS for $10.6 billion), Lifestance (IPO at $7.5 billion market cap), and PE-backed Premise Health (acquired by OMERS Private Equity for $1.1 billion) If a clinic is indicated as the optimal way to deliver care for that patient population, there are creative ways to build and operate them. Full article below with advice and strategies on how to build and operate hybrid clinics from my teammate Anarghya Vardhana from Maveron, Amir Dan Rubin from OneMedical Group, Steven Eidelman from Modern Animal and more!

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What's been buzzing the loudest for me so far at HLTH Europe? AI, AI, and more AI, of course. Tongues have been tripping over those two letters in every conversation I've had today or listened to including these nuggets: Michael Chen of Nuclera: "Generative AI is wonderful because it brings more hypothesis-making power for the drug discovery researcher. But ultimately, we all know that AI isn't enough. You need to have the power at the wet lab benchtop in order to validate experiments and bring hits and needs into the local trials, and the quality of these hits and leads that ultimately will help the success rates in phase one through three clinical trials, which often don't rear their ugly heads until it's too late." Geraldine O'Keeffe of EQT Group: "There's a huge amount of investment opportunity in terms of AI-, SaaS-based models, in big data and machine learning in diagnostics, precision medicine...delivering the care you need better, faster, cheaper." Karen DeSalvo of Google: "The availability of AI, the ubiquity of it, and just how quickly that is moving [has been significant in the evolution of AI in healthcare]. What I'm hoping now is the next generation of this technology allows us to democratise access to health for people all over the world, and that we can start to do that much more quickly and much more equitably." Chiara Bucciarelli-Ducci, MD, PhD and Marlies Schijven on clinicians' take on AI: "To scale up adoption of AI in healthcare, industry needs to demonstrate to clinicians and healthcare providers the cost effectiveness and cost efficiencies that the technology can bring at every level." "More often than not, we find that AI does work and helps to bring efficiencies, but it also carries risk. So it needs to be properly explained." Agree, disagree, or follow-up comments on these soundbites? #genAI #AIeverywhere #medicalAI #healthtech #digitalhealth #AIfordrugdiscovery #techbio #healthAI #clinicalAI

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Generally surprised to not see more hardware vendors in healthcare and life sciences partnering with AI-first companies. When the zone of genius of a company is hardware, it's difficult to imagine that same team achieving parity on an AI product – Apple may be the greatest example of success in this realm, and still has secured many AI capabilities via targeted M&A (Siri, Turi, Xnor.ai, Voysis, Inductiv). Given the importance of leveraged distribution models in the ossified industries of healthcare and life sciences, it seems there is untapped opportunity for more hardware manufacturers to partner and co-develop AI products with startups. Control over the way data is captured and processed is critical for building high quality training data sets, which creates an interesting feedback loop between hardware companies and potential AI-first startup partners. Interested to explore this further with folks in healthcare and life sciences hardware!

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🚀 𝐔𝐧𝐥𝐨𝐜𝐤 𝐒𝐭𝐚𝐫𝐭𝐮𝐩 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐰𝐢𝐭𝐡 𝐔𝐫𝐢 𝐋𝐞𝐯𝐢𝐧𝐞’𝐬 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬! 🚀 Uri Levine, co-founder of Waze and Moovit, shares invaluable lessons in his book “𝘍𝘢𝘭𝘭 𝘪𝘯 𝘓𝘰𝘷𝘦 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘗𝘳𝘰𝘣𝘭𝘦𝘮, 𝘕𝘰𝘵 𝘵𝘩𝘦 𝘚𝘰𝘭𝘶𝘵𝘪𝘰𝘯.” Key takeaways include the importance of passionately embracing the problem you’re solving, the necessity of customer retention as a marker of product-market fit, and the strategic use of pitch deck slides. He also emphasises the significance of hiring and firing correctly and observing user behaviour to refine your product. These principles can transform your startup journey, ensuring resilience and growth. 🌟 #StartupSuccess #Entrepreneurship #ProductMarketFit #Innovation

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One conversation we should be having more, especially in healthcare: is this an AI problem or a policy problem? A big challenge in American healthcare stems from the reality that our healthcare strategy changes with every administration. Throwing AI at problems that are well-addressed by policy innovation (e.g., AI for payer-provider RCM war) feels like a gross misallocation of resources. Solution sets are rarely zero sum – in truth, most problems will require policy, tech, and human interventions, but it's worth asking ourselves whether AI is the best solve when we have a multitude of options.

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Here is the hard truth about building a health product: skipping early feedback from healthcare insiders and payers can cost you YEARs of time and MILLIONS of $$$. At MDisrupt we are seeing a powerful shift. More and more companies are finally prioritizing feedback from real healthcare stakeholders - a critical step in the rush to scale. This isn't a nice to have, its the difference between products that SCALE and products that FAIL. So what are we doing about it? Today we are helping clients to: 👉🏼 Get feedback from PAYERS early on, crafting a reimbursement strategy that sets the stage for growth. 👉🏼 Consult CLINICIANS ensure products align seamlessly with clinical workflows — no more “great product, but hard to use” feedback. 👉🏼 Engage HEALTH SYSTEM LEADERS to confirm that the product addresses real, pressing clinical needs. We are making it as easy as possible to find these valuable experts and engage them in hourly calls so companies can get feedback quickly. Please talk to me if we can help you with this.

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Movac KiwiNet Deep Tech is difficult because you do not comprehend 95% of the universe using the Standard Model of Particle physics. Either learn that measurement is flawed due to human bias or experience and unbiased state through transcendence experiences corresponding to Christ consciousness. The physics of life is based on LENR and not the Standard Model of Particle physics. Unfortunately, scientific research has been captured by the materialism perspective. This is mutually exclusive to the light observed in consciousness. Physics of life. Deep Tech https://lnkd.in/gKqC3PAR Gateway into the aromatic ring Faraday cage system. Time to see the light 😁.

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Great article from PitchBook on why domain expertise amongst VCs matters. TL;DR -- in verticals like healthcare, having investors with high amount of domain expertise can result in up to 40% higher odds of achieving an IPO, 20% higher chance of being acquired and 30% higher chance of an overall successful exit. Importantly, the marginal value of having investors with domain expertise varies by sector and stage, and is not uniform, so your mileage may vary, and generalist investors still have a very important role to play in the venture ecosystem. https://lnkd.in/dXpi8gzm Curious to hear from founders: Do you have examples of seeing specialist investors be helpful in unique ways that you can share? Or alternatively, do you have an experience with a generalist investor who has been more helpful than the specialists?

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Of note: Your health records are coming to new apps This in an interesting change, and a somewhat Epic change. All puns intended. If you don’t know, the leading medical record storage solution in the US is operated by Epic. A rather critical problem is that if you walk (well drive) between two hospital in the US, the medical records tend not to follow you, unless you carry a large Manila envelope. Having Epic (and others) via TEFCA allow export of records arguably allows you to only carry your phone, which is a very siginificant change. This also (arguably) opens up a huge new innovation sector. Epic is one of the organizations that has been helping the US federal government establish the Trusted Exchange Framework and Common Agreement, or TEFCA. The goal being to iron out both the legal and technical requirements for sharing patients' data at scale. Health-care data in the U.S. has historically been siloed and difficult to move around. Clinics, hospitals and health systems can store their information in a variety of formats across dozens of different vendors, and there hasn't been a trusted nationwide mechanism in place for transporting it securely. This means if a patient moves to a different state or visits a new hospital, their medical records tend not follow. PS. If you want appreciate how profitable the US medical sector is, Epic HQ has a restantant designed to look like Kings Cross from the Harry Potter movies. https://lnkd.in/g8NaWza8

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Is Epic the Apple of electronic health records? You can see some similarities: - dominance among a premier market segment - a "walled garden" style ecosystem with strong control over third-party plugin development - occupies a strategic and brand position focused on user data privacy HTD Health's Interoperability and Data Liquidity Practice Lead Brendan Keeler was quoted today in POLITICO on feisty dispute within the world of health data exchanges. Brendan Keeler, interoperability practice lead at HTD Health, said in the bigger picture, it doesn’t matter whether Epic, Particle and Intregritort are right or wrong. As long as data exchange is limited to treatment but workarounds are available, he said, people will use the workarounds to achieve business goals. “It’s so lucrative to do so today,” Keeler said. Under TEFCA, organizations that sign a data-sharing agreement and adopt the necessary infrastructure can become qualified networks permitted to share patient health data. Keeler said that TEFCA, which is run by the HHS Office of the National Coordinator for Health IT’s nonprofit partner, the Sequoia Project, is supposed to have stronger governance than Carequality and create a unified network pushing data to organizations in addition to groups pulling it from others. Full article here (warning paywall): https://lnkd.in/gkrQK6Fm

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Incredibly excited to share our updated Healthcare AI thesis. This is not a market map – in 4,000 words, we dive deeply into the research, products, business models, and market entry points that are reimagining healthcare with AI. More specifically, we cover: • Translating AI research into real-world products • Mapping modalities (software, copilots, agents, services, diagnostics, therapeutics) to problems in healthcare • Designing AI business models that unlock billion-dollar markets • Closing current gaps in healthcare AI infrastructure • Distinguishing AI platforms from point solutions Let us know what you think ⤵️

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Someone broke down the estimated costs of a $15 Sweetgreen salad. We need these cost graphics for digital health to help future founders. Especially for tech enabled cos, the greatest cost is labor. Much of the "value created" is in curation and matching, which means the gross margins are reliant on labor arbitrage. Pay someone $X, but get paid $X+Y% per visit. The challenge with healthcare is that you often have to pick one of two business models: 1. specialized, low volume, high cost, low gross margin % 2. generalized, high volume, low cost, high gross margin % I'd argue founders should focus on gross dollars over the lifetime of the customer (like Jeff Bezos said) instead of gross margin %'s, but we'll leave that for another time. The challenge is that many tech enabled digital health cos have high cost of goods sold (labor) and moderately high product, design, and engineering budgets. We must build differentiated solutions for patients, clinicians, and clients to be out incumbents, however, we're realizing that companies are ultimately valued on the discounted value of future cash flows. And the high SG&A costs at most organizations might be inbalanced to the unit economics of the business. I'm not sure the math pencils out for everyone unfortunately. But I'm just a guy on the internet, would love to hear the thoughts from those smarter than I (you!). Comment below. --- p.s. I have no idea the accuracy of the graphic and not an investor in Sweetgreen. Cheers. Credit: David Crowther

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The (Healthcare) Ground Beneath Our Feet… This year it is likely that over $10 billion will be invested in the digital health sector – a robust amount, no doubt, but inconsequential when compared to the amount of capital tied up in healthcare real estate assets, which is estimated to be $1.2 trillion by Jones Lang Lasalle. Of that amount, approximately $790 billion is in the hospital sector while the remaining $490 billion is in medical office assets. It is estimated that there are 48.6k facilities with 3.4 billion square feet of healthcare real estate, 42% of which are owned by REITs and another 33% held by other private investment vehicles. Much of the digital health investment is meant to make the physical healthcare infrastructure more productive: greater operating efficiencies, greater patient throughput, more relevant personalized care models with better outcomes. As the healthcare system is being pushed to be more distributed to lower cost sites of care, accelerated now by the rapid proliferation of automation and AI capabilities, the potential impact on healthcare infrastructure assets will be profound. Thoughts on what that might look like... https://lnkd.in/eTfAdUbS #digitalhealth Flare Capital Partners

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💡Benchmarks in the investment industry serve as a standard for measuring the performance of securities, mutual funds, or investment managers. They provide a reference point for analyzing allocation, risk, and return of a portfolio, enabling investors to make informed decisions. Thanks Peter Walker for keeping us all informed!! Can’t wait for this!! Follow and get on the waitlist for this one! #benchmarks #fundraising #Capitalraise #founders #womenshealth #femhealth #medtech #wellness

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This week Mikal Ventures brings you a 4-week breakdown of the US capital market with an emphasis on the Mikal verticals. Over the 4-week period ending April 15, just over 900 new companies were added, with 94,109 companies included in the US Health Tech, Med Tech, Fin Tech, AI Tech, and Ed Tech verticals. AI Tech again saw the greatest percentage of growth in new companies and transactions, weighing in at over double the vertical averages for the period. The US Fin Tech vertical again led the way in SEC Filings and news articles. Mikal Ventures publishes these stats on a rolling 4-week basis to provide a perspective on how the verticals and new startups move through time. We hope you find them informative and thought-provoking. VC Investors are always adjusting their "Play Book" with a variety of options for investing. Mikal Ventures is one of those alternatives, offering curated solutions to real world problems. Reach out if you have questions on how we can be of assistance and bookmark https://mikalventures.com/ for additional insights and information.

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I'm going to try Forward's business model in 2025, but the budget will be $5,000 and most things will play in the opposite direction. First opposite idea: How do we make AI NEVER replace medical providers? At DeepCura, we want to play a contrarian approach in the market, and try to create systems that make the human connection obligatory even in the next 10 years. Here are some ideas but the feedback is more than appreciated. Starting with Popular issues, big tech usually wants to: -Sell the collected data to third parties in pursuit of profit. -Eliminate jobs seeking automation. I was thinking on the following: 1.How can we make the data work for the user while keeping it private? 2.How can we create some point of leverage long term, so that medical providers can never be replaced by AI? Then I arrived at the following strategies: What if we created 2 concepts: 1.An AI Medical Marketplace for patients where only the first interaction is with AI. 2.A natural selection routing system based on a leaderboard from our own users? I was thinking on this flow: 1.DeepCura User opt ins by clicking on settings "Allow to be discovered in the DeepCura Marketplace" 2.A patient feels sick and goes to google and finds the DeepCura marketplace, and gets screened by our AI Nurse copilot and based on that screening, it selects 3 DeepCura Medical candidates based on their own DeepCura data and their experience with conditions the doctor has already treated in the platform, all of this analyzing his own data. In this model, the data works FOR the DeepCura user, and sends patients to the best fit based on natural selection concepts. And then the leaderboard or score is something that should provide trust to the patient, giving leverage to the medical doctor with a scientific system based on clinical meritocracy. I'm thinking now here as a patient, it would be great to see a doctor score based on their AI proficiency + their experience that is relevant to my current conditions. We are bouncing ideas with our own users about this, but this is for 2025. Basically DeepCura sending patients to our own medical providers. We could also do some booths in some malls like Forward, but in all honesty, as a patient I have never thought of going to the mall to treat my health. Who knows, things can change, but a mall booth, with an employee and an Ipad for sure is less than $10k for this experiement.

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Hot take on a lot of early-stage digital health companies out there: You're not a tech company. Most tech-enabled care companies I've seen over the past two years are only tech-enabled in the sense that they use telehealth as their delivery mechanism for care. There are exceptions to this, of course - companies who build computer vision and applied AI tools to better the doctor-patient experience, companies who build tooling around risk scoring and stratification to enable more personalized medicine, companies who build truly novel intakes to better route patients to the appropriate care, etc. These companies have real technology chops and, if they sell it right to the market, can get credit for this from investors. But if you're truly a telehealth provider, you need to realize now that the goal post has shifted dramatically from 2021 and you're going to be valued at maturity at something like 3x EBITDA. That's profit. Not revenue. Throw a revenue multiple out the window. That's not the world we live in if you're a virtual doctor's office. Think of how that impacts venture math. If you're raising a seed round at $15M post-money (or $12M pre-money), it means you'll have to generate something like $150-200M in annual EBITDA to be a fund returner for your seed investors. Maybe even more with dilution, since telehealth businesses are not traditionally capital efficient and will likely need to raise material additional capital to reach that scale. You need to be ridiculously profitable (probably on $800M-$1B in annual revenue since you're likely going to be a 40% gross margin business). Here's the good news: you can set yourself up well (and probably be a contrarian founder relative to your peers), by talking about EBITDA growth and how you can grow into a fund-returning outcome based on realistic mature market comps. This will stand out for the investors you're pitching, even at the early stage. This is how you can beat the malaise of the tech-enabled care market. Focus on the right metrics and the right type of growth. You don't have to have all the answers (no founder does), but you need to make sure the ship is headed in the right direction. A focus on profitability for "tech-enabled care" (*ahem* telehealth) businesses is a good first step.

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Suppose you are a VC, Early investor, or CIO trying to keep your eye on the industry trends. In that case, you’ve probably heard already about hallucinations, context shifts, and accuracy measurement problems in LLMs. These scary concepts kill motivation to invest in LLM-based projects and build RnD departments. Are they really so scary? Yes, but just for now, you have a good strategy to cope with them. Is this strategy already known? Yes, old but still one of the most effective approaches- knowledge graphs in combination with fresh new RAGs will allow you to keep your models free of hallucinations, stable in accuracy, and easily adaptable for context change. Btw, do you know that knowledge graphs are seeing as one of the most impactful AI enables during this year?🙌🏻 How big companies like Deutsche Telekom and Microsoft are seeing the future of LLMs adoption you can find here https://lnkd.in/d4B3t56z

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