Bharat Welingkar

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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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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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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On the latest Vital Signs, Nikhil Krishnan and I had a ton of fun chatting with Dr. Atul Butte. Dr. Butte is a professor at UCSF, Chief Data Scientist of the UC Health System, and a co-founder of numerous healthcare startups. In our conversation, Dr. Butte shared his takes on healthcare AI opportunities, challenges, hype, and more. A few highlights: ⚡LLM use cases, data, and growth LLMs are already helping to solve the “drudgery” aspects of medicine, such as automatically drafting SOAP notes and patient messages. Dr. Butte is excited about future applications of LLMs that can help with the “cognitive” aspects: with difficult medical problems an LLM might be able to stitch together disparate pieces of information to generate a potentially novel solution. He also highlights that prompt engineering will become the next “coding language” as so much of leveraging LLMs is knowing how to ask them the right questions. Additionally, Dr. Butte discusses how since 2016, UCSF has been working to deidentify its patient notes so that it can query them to answer clinical questions and ultimately enhance patient care. ⚡The need for patient-facing AI As patients are increasingly able to access their own personal health data in nicely structured formats (e.g., EHRs, wearables), they desperately need more tools to help them interpret and make decisions using that data. Dr. Butte understands that these patient-facing solutions are challenging to get funded but he emphasizes that patients need to receive the benefits of AI sooner. ⚡Evaluating healthcare AI solutions In order to evaluate AI solutions, we often put human specialists – such as medical professionals – on the same tasks. Understanding that specialists often disagree with each other, a significant challenge arises in figuring out how we can evaluate AI considering we can’t just “average” all the specialist opinions. Dr. Butte points out that there still remains a massive gap in AI evaluation tools. Additionally, there are so many organizations that want to have a say in how healthcare AI is regulated that the landscape is uncertain. An awesome conversation with an expert on all things AI meets healthcare. Listen to the full episode below: Spotify: https://spoti.fi/3Wr3643 Apple: https://apple.co/4acwgan

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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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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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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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Presenting at the Senate as part of the Coalition for Health AI (CHAI) "Day on the Hill" wasn't on my 2024 bingo card, but I'm feeling energized by the bipartisan discussions on healthcare AI. The numbers tell a compelling story: while nearly 2,000 healthcare AI startups have received funding over the last two decades, only 1 in 20 provider-facing companies reach late-stage maturity. Beyond natural market dynamics, two critical barriers are holding back AI's full potential in healthcare: unsustainable payment models and inadequate evaluation infrastructure – only 5% of research studies on LLMs in medicine utilize real patient care data, and studies advancing our understanding of performance beyond medical licensing exam benchmarks remain anemic at best. At CHAI, we're advocating for essential foundations: • Rigorous technical model performance evaluations • Comprehensive bias assessments across diverse patient populations • Thorough pre-deployment simulations and prospective studies • Robust ongoing local monitoring The path forward demands strong reporting infrastructure, advanced healthcare AI benchmarks, and meaningful public-private partnerships to drive real-world AI deployment in healthcare. Stay tuned for CHAI's upcoming initiatives in 2025 as we expand our engagements with the startup and investment ecosystem. https://lnkd.in/enw-GQcA

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M13 is thrilled to lead the Series A in RadiantGraph which is focused on personalization in healthcare. As we continue to invest in early stage companies it is abundantly clear that the biggest indicator of success is the founder and his or her ability to execute on their vision of the future. In this case, I don't think I have seen much better founder-market fit than we have with founder and CEO Anmol Madan. We believe in his vision: in order to have the best patient outcomes in healthcare we need increased personalization and AI is the breakthrough to get us there. Anmol started one of the pioneering digital health companies Ginger and then was Chief Data Scientist at Livongo and Teledoc. He has done it all: early stage, late stage, public company. And he deeply understands the nuances of the healthcare market from all angles which is critical for success. While there are many players building healthcare-specific AI capabilities, RadiantGraph is focused on outcomes: The platform is already processing personalization models for more than 3.5 million people, despite launching just last year. Excited to partner with my friend Adam D'Augelli and the great True Ventures again and Morgan Blumberg who has been deep in the healthcare market with me for many years. More on our thesis in the blog below. https://lnkd.in/evDDmC3W

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A playbook for managing the biggest risks in early-stage health tech adapted from Wesley Fogel article on Peerr 🚀 👥 People Risk: Engage users, decision-makers, and clinicians early with prototypes and iterative feedback. 🏥 Clinical Risk: Ensure safety through external evaluations and compliance with standards like DTAC and DCB0129. 🔒 Security Risk: Prioritise data security and compliance with HIPAA, ISO27001, and other standards. 🛠️ Technical Risk: Avoid technical debt and optimse your tech stack with an experienced CTO. 🏛️ Governance Risk: Build a diverse advisory team to guide ethical, legal, and regulatory strategies. 🌐 International Risk: Weigh the benefits of early overseas exploration against potential resource strains. 💰 Financial Risk: Focus on vision, team, and go-to-market strategies at different funding stages. 📈 Commercial Risk: Maintain ongoing customer engagement and seek formal partnerships through accelerators. https://lnkd.in/e8BPBnEv

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To all the new or #expecting #parents, Ubenwa Health has developed a unique #AI-powered app designed for those who are still learning how to intuit their infant’s emotions. Through listening/studying to various different baby cries, #NanniAI is here to help you distinguish between their wants and needs, and which decisions is the most likely to soothing them in the meantime. Yes, it is named "Nanni," and yes it is AI powered — however it’s not intended to replace parents, it’s more of a #supplement to help support and encourage good #parenting skills. Nanni AI is the culmination of years of research and data collection in Nigeria, Montreal, Canada and Brazil, which was used to train an algorithm that sorts "thousands and thousands" of cries and tries to categorize your baby's needs accurately. The developer mentioned earlier, Ubenwa Health raised a $2.5 million pre seed financing round led by AI-focused funds. What do you think of this app? If you are a parent, would you consider using Nanni AI?

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Join me Thursday, May 9 at 9amET, and Microsoft for Startups company founders, Stead Burwell, Lana Feng, Ph.D. Andrew Gostine, MD, MBA and Ankur Teredesai for a lively, *real-world* conversation about applying #AI to improve #healthcare. In addition to hearing these leading #innovators discuss the challenges, value, and future of AI, you'll learn how: 💲 Outbound AI uses AI to improve #RevenueCycleManagement 💊 Huma.AI uses #GenAI to provide meaningful and actionable #insights for #lifesciences companies 🛌Artisight uses #AI and #cognitiveservices to streamline #patientcare and #safety 😷 CueZen uses #AI and #GenAI for hyper-personalized management of #chronicconditions 👉 Registration is below. Stelios Hatzakis Tony Kountz Derek Haynes Bethany Cordes Tom Davis Ted Roduner Microsoft for Healthcare

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I've been experimenting with modelling healthcare opportunities quarterly vs. monthly... Why? - A lot of healthcare activities come in quarterly frequencies (e.g., Rx's are often given in 90 day supply) - Healthcare moves about 3x slower than most industries - Best practice around patient engagement re: tertiary services (from diagnostics to diet) seems to be every 90 days This slightly longer-term viewpoint combined with a razor-sharp focus on weekly growth is a potentially strong recipe. 

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Perfect example of solving for a customer pain point, not innovation for #innovation’s sake. The founder is a physician and hated spending >2 hours daily on summarizing his day’s work, as required by law. This burden is driving people to leave the profession. So, Abridge built an AI app to automate a lot of this admin work and summarization for docs and hospitals. Now, Abridge is blowing up, rightfully so! Their customers (doctors) call the product ‘magical’ and ‘life changing’. A few lessons here: - Starting from real customer pain points is a winning strategy - #AI can add enromous value by solving mundane problems (not every AI application needs to be futuristic) - People with deep experience in an industry can leverage not just their expertise, but their tactical experience with daily hassles, to build great ventures Congrats Shiv and team Abridge, well deserved! 237 Ventures MENA FINTECH ASSOCIATION https://lnkd.in/dwE6nfKJ

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Friends/Fellow Founders - TIMELY– As many of you know, I recently visited lawmakers in #Sacramento to speak out against harmful #AI #legislation. This was an incredible experience, and I was grateful for the opportunity to share my story. Thanks to the advocacy of many AI startups, one bad bill was defeated, but another, SB 1047, is headed to the Governor who has not decided whether to let it become law or veto it. #SB1047 would regulate AI in unprecedented ways. Though the bill says it’s about corporate AI giants, I fear small startups will be hit the hardest. Companies like mine will struggle to overcome the new #regulatory hurdles enforced by this reckless #policy. I’m sure everyone agrees AI abuses and misuses should be prevented and prohibited, but SB 1047 would punish businesses that build technology instead of those that misuse it. The government should regulate misuse and harm, not core technology. Please consider joining other #startup and #smallcompany leaders in signing the letter below urging #Governor #Newsom to #veto SB 1047. Thank you! https://lnkd.in/gAJNDEDJ

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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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Platforms are in, point solutions are out as the healthcare AI category leaders come into focus this year. In conversation with Shelby Livingston at Endpoints News, I shared: 1. Why AI-first companies have the greatest platform potential and heuristics for identifying these opportunities at the earliest stages, 2. How companies are differentiating through co-development and distribution partnerships with leading healthcare institutions, and 3. Strategies for navigating anemic budgets and constrained market sizes through business model innovation such as AI-enabled services and pay for performance models Read more here: https://lnkd.in/dJWqv_zC Bessemer Venture Partners Abridge #healthcare #lifesciences #AI #generativeAI

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🎬 Matt Damon and 🏈 Colin Kaepernick have invested in it, and so has 💰Andreessen Horowitz... Function Health's executive team has "engineered a first-of-its-kind approach that focuses on prevention and empowerment"—very much in line with what we aimed to achieve with Carebook when we started it over six years ago. Could they be the ones to fulfill their lead investors' prophecy (link at the bottom) and Dr. Eric Topol, MD's premonitions in his books "The Patient Will See You Now" and "Deep Medicine"? They have just secured a $50 million grant. Let's hope this is not another Theranos and that we are truly witnessing the rise of the next generation of direct-to-consumer health companies. These companies will be key players in the "transition from reactive to proactive" health care that the system—and we—desperately need. What do you think? Links: 📝 Investment round press release: https://lnkd.in/eUG87qkf 🔗 Andreessen Horowitz's prophecy: https://lnkd.in/eZATCf4f

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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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