Devi Ramanan

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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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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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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Hot off the pre-print server – an important publication authored by the Atropos Health team exploring the performance of LLMs in answering real-world clinical questions. LLMs excel at summarizing and contextualizing existing literature, but cannot generate de novo medical evidence today. The results of the study highlight performance gains attributable to injecting novel, relevant, and up-to-date medical evidence into LLM-powered clinical decision support systems. Brigham Hyde Nigam Shah Saurabh Gombar Read the study here: https://lnkd.in/ecuU25Pq

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Open-source models can be a strong lever for increasing gross margins at AI companies, especially those that have hefty cost structures (e.g., enterprise sales orgs that require significant customer success or implementation resources, as well forward-deployed AI services models common in healthcare and life sciences). One of many reasons to maintain a modular and flexible stack with minimal dependencies when building an AI company. More on the importance of flexibility in the AI stack: https://lnkd.in/eA2XATRk h/t Delip Rao #ai #artificialintelligence #generativeai #healthcare

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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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Excited to announce Frist Cressey Ventures' investment in Qualified Health and privileged to partner w/ SignalFire, Healthier Capital, Town Hall Ventures and others!   Healthcare costs are increasing at a faster rate than ever before, with the US spending nearly twice as other wealthy countries and still ranking bottom in outcomes. It's a story you've all heard - - US healthcare is known for astronomical inefficiencies, waste, workforce supply constraints, fragmented access, appalling patient experiences, and driving patients and businesses into debt.   Yes these problems present opportunities. But opportunities are limited to the system's appetite for change, desire and incentive to try something new and the innovation available. At FCV we believe we've hit the trifecta with Generative AI. Generative AI has given us truly transformational tools in the toolkit and the healthcare ecosystem is demanding to absorb its benefits. This perfect collision of supply and demand means healthcare is poised for big change.   Generative AI is everywhere, it's buzzy, it dangles hope and opportunity. But many things need to be true for the Gen AI transformation in healthcare to take hold, such as but not limited to:   💡 Patient lives and data need to remain safe: We believe in a highly regulated and human life-touching sector such as healthcare we must ensure Gen AI can drive to unrivalled savings and improvements in the quality of care WHILE not putting patient lives or data at risk.   💡 Systems need to own their utilization of Gen AI: We believe systems will use some hybrid of external partners and homegrown solutions but importantly will want full control of data provisioning and utilization as well as the ability to solve an unlimited number of nuanced issues and not cookie cutter algorithms.   💡 Costs need to be looked at on an enterprise level: We believe the speed of innovation in Gen AI means costs will continue decreasing dramatically but at an enterprise level this will still not be a small detail. Systems will want the ability to understand costs and delegate as needed for the outcomes desired at the organizational level.   Enter Qualified.   We knew when we met Justin Norden, MD, MBA, MPhil and team we were standing in front of changemakers. These individuals have lived and breathed the true application and implementation of Generative AI far before we were asking ChatGPT to write our emails. In fact, if you’ve ever driven in a Waymo you’ve benefited from past products this team has built.   Qualified Health is on a mission to enforce governance in Gen AI, allow systems to rapidly build for their needs, monitor and make decisions for all GenAI (homegrown and external) and accelerate the value the sector can glean from these new technologies.   Huge shout out to William T., Tommaso Auerbach and Jamie Kuntz for their hard work!

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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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Anne Wojcicki, CEO of 23andMe, has announced plans to take the genetic testing company private. This strategic move aims to provide more flexibility in navigating regulatory challenges and to accelerate growth opportunities. The decision follows a trend of health tech companies seeking private ownership to focus on long-term objectives. Wojcicki highlighted the importance of this shift to maintain the company's commitment to innovation and customer-centric services. Taking 23andMe private reflects a bold step towards enhancing its position in the rapidly evolving genomics industry. Read more about this exciting development here: https://lnkd.in/gdrC3a64 #genomics #IPO #privatisation #23andMe

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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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Though benchmarks for medical AI are still imperfect, there's no question that AI will continue to march towards diagnostic excellence. These trends will place greater emphasis on physician-scientists – those who advance medical knowledge via closed loop clinic/lab interfaces, which can then feed back into large-scale AI systems that serve entire populations. This was all part of the OG vision for the learning health system. source: https://lnkd.in/eh2RM72s

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🚀 Accelerating the Future of Healthcare with AI 🚀 I recently had the pleasure of tuning into an insightful episode of The Chris Voss Show featuring Lana Feng, Ph.D., CEO and co-founder of Huma.AI. 🗝 Here are some key takeaways that highlight the transformative power of AI in drug development and precision medicine: 🧠 Introduction to Huma.AI: Dr. Feng’s company, Huma.AI, leverages generative AI to automate data analysis, speeding up the development of life-saving drugs. Their mission? To reduce the time and cost involved in bringing these drugs to market. 🌟 💡 Challenges in Drug Development: Traditional drug development is a 10-year, $2.6 billion process plagued by data bottlenecks and regulatory complexities. AI can revolutionize this by handling vast amounts of unstructured data from various sources efficiently. 📊 🤖 The Role of AI: Utilizing advanced AI models like GPT-4, Huma.AI ensures data privacy, accuracy, and transparency. This leads to more reliable insights, faster clinical trial enrollments, and ultimately, better patient outcomes. 🏥 🌍 Real-world Applications: From identifying the right treatments in precision medicine to accelerating clinical trials, AI’s impact on healthcare is profound. It’s especially crucial in areas like cancer treatment and chronic disease management. 🎯 📈 Industry Recognition: Huma.AI has garnered significant recognition, being named a leader in generative AI for life sciences by Gartner. 🏆 🔮 Future Directions: The integration of AI in healthcare requires responsible usage, industry-wide collaboration, and continuous efforts to mitigate risks like data privacy issues and AI hallucination. 🤝 🌟 AI for Good: Dr. Feng emphasizes the importance of using AI responsibly to lower drug prices and improve healthcare outcomes. This approach ensures AI serves as a powerful ally in our quest for better health. ❤️ I’m excited about the potential of AI in transforming healthcare and making life-saving treatments more accessible and affordable. Let's embrace this technology for a healthier future! 🚀 #HealthcareInnovation #AI #PrecisionMedicine #DrugDevelopment #TechForGood #FutureOfHealthcare #ChrisVossShow #HumaAI #Leadership

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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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In our latest publication in Health, Management, Policy, and Innovation – "Why AI Is Good for Our Health but May Hurt Our Wallets," we examine how current regulatory frameworks, reimbursement structures, and business models for AI in healthcare are decoupled, creating an environment in which AI may significantly increase costs without necessarily improving outcomes. We recommend three interconnected reforms to unlock the full potential of healthcare AI: (1) Rigorous pre- and post-deployment evaluation to verify proposed clinical value, (2) Development of assessment standards through shared guidelines, and (3) Strategic alignment of AI deployment modalities with sustainable business models – "modality-business model-market" fit 👉 https://lnkd.in/d8NiczyM Sneha Shah Jain, MD, MBA, Clinical Professor and Kevin Schulman Professor of Medicine at Stanford University, Nigam Shah, Chief Data Scientist, Michael Pfeffer, Chief Information and Digital Officer, and Linda Hoff Chief Financial Officer at Stanford Health Care

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I’ve spoken to 50+ CMIOs/CIOs over the past year. Here are 5 thoughts about AI Scribes I'm hearing that many people are NOT talking about👇 1️⃣ Distribution matters more than Product. Most are just buying Nuance because it’s easier to buy. It’s a known entity. It’s an add-on to the Microsoft contract which means less internal hoops. Key question for who wins long-term: will the startups get distribution faster than the incumbent gets innovation? Will the EHRs eventually just release their own native AI scribes? (my belief is yes… which Oracle/Cerner has already done). 2️⃣ There is a clear “phenotype” for the clinicians who actually use AI Scribes: it’s clinicians who don’t care to have control over how their notes look and sound. But for those who really care about controlling the output? Those folks will keep writing their own notes. 3️⃣ The impact of AI scribes on medical education and trainees is very unclear. Some believe trainees should get exposure to AI scribes early on. Others worry that trainees who don’t learn to write notes are going to miss a critical skill that helps clinicians think. And what happens if there’s a cybersecurity incident and the EHR goes down? Will future clinicians not know how to document manually? 4️⃣ ROI is still hard to truly measure. Few see any improvement in “productivity” (i.e. more patients seen). The real value is in improving clinician well-being. However it’s hard to prove AI scribes are lowering staff churn since that is a lagging indicator many steps away and affected by many other factors. 5️⃣ The per clinician pricing model used by most vendors breaks down at scale. If I have clinicians who would only use the AI scribe for 30% of their patient encounters, am I going to buy a license for them? Probably not. What other less talked about learnings or insights are you hearing behind the scenes?

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Excited for this one. Congrats to Alex Cohen and Hello Patient on their seed round announcement. Hello Patient stands to change the communication paradigm between patients and providers forever. Their generative AI call agent technology is tackling the tremendous admin burden of patient facing comms (scheduling, follow ups, referrals, prescription refills, etc). Massive opportunity and we think Alex and his team are expertly positioned to become market leaders. Max Ventures is thrilled to co-lead the seed round with 8VC and Bling Capital A great quote below from Alex highlighting the concept of "service as a software". Healthcare is a heavily service oriented industry - so the opportunity for step change is even more profound. The startup's approach is part of a paradigm shift where AI companies are offering service as software. "I'm a big believer that the right way to do this is through a managed services business. We're not building SaaS. I believe we can do this services as software where what was traditionally a services business now has software margins because I don't have to go employ a whole bunch of people to do the services under the hood," Cohen noted. https://lnkd.in/ehNN2AmW

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I’m optimistic about the potential of generative AI to reduce provider burnout and improve provider-patient interactions. However, that optimism is tempered by the data that indicates the technology is not quite ready yet. Bright Ventures is interested in digital health founders who understand the realities of provider-patient interactions, and the role technology can play in strengthening clinical outcomes. Which founders and teams are getting this right? Tag them in the comments below.

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Harnessing Proprietary Information for Strategic Healthcare Investments Healthcare investments offer a unique combination of financial returns and the potential for significant social impact. For family offices and discerning investors, navigating this complex landscape requires more than just capital—it demands strategic insights fueled by proprietary information. The Foundation of Strategic Investment: Proprietary Information In the investment world, knowledge is power. This is particularly true in the healthcare sector, where regulatory landscapes, technological advancements, and policy changes can drastically impact the viability and potential of investment opportunities. Access to proprietary information gives investors a critical edge, enabling them to: - Anticipate Market Movements: With insights into healthcare policies and emerging trends, investors can position their portfolios to capitalize on upcoming shifts in the market. - Enhance Due Diligence: Tailored underwriting and due diligence tools grounded in exclusive data can highlight risks and reveal hidden potentials in investment prospects. - Maximize Value Creation: By understanding the intricacies of the US healthcare economy, investors can devise strategies for long-term value creation, rather than short-term gains. Empowering Family Offices Family offices often seek investments that offer both competitive financial returns and the opportunity to create a positive legacy. By refining investment platforms to address the specific needs of these entities, such as instant diversification and upside capture, they can achieve their financial and legacy-driven goals. These platforms can provide: - Market Insights: Curated intelligence that focuses on the most promising healthcare segments. - Educational Resources: In-depth content to deepen the understanding of healthcare technology trends and investment strategies. Building Relationships and Trust Investing is not just about the numbers; it's also about people. To attract and retain high-potential family office prospects, investment platforms must foster empathy and engagement. This involves: - Offering Exclusive Rights: Providing access to follow-on deals and direct investment opportunities with board or advisory roles. - Understanding Emotions: Addressing fears of missing out on prime opportunities due to geographical limitations and dissatisfaction with average investment options. - Creating Community: Facilitating a network of like-minded investors focused on collaborative, high-impact healthcare investments. Harnessing proprietary information is critical for making strategic healthcare investments that offer significant financial returns and contribute to the betterment of society. If you like to learn more about strategic healthcare investments or discuss your specific investment goals and how proprietary information can be leveraged to achieve them? Please feel free to reach out or leave a comment below.

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Thrilled to announce the MVP launch of Nephrolytics in our first customer: A revolutionary AI-powered platform designed to tackle some of the most pressing challenges in nephrology. Our flagship AI-Agents, Saya™ and Lumi™, are set to redefine how nephrology professionals deliver care, improve outcomes, and manage financial performance. Addressing Physician Burnout: A National Crisis. Physician burnout has reached alarming levels: • 63% of physicians report feeling burned out, according to the AMA, with administrative burdens being a top driver. (American Medical Association, 2024) • Burnout is estimated to cost the U.S. healthcare system $4.6 billion annually in lost productivity, reduced clinical hours, and turnover. (Annals of Internal Medicine, 2023) How Generative AI Agents Can Help: AI has proven to be a game-changer in addressing these challenges: • Automating routine documentation tasks can save physicians up to 2.5 hours per day, giving them more time for patient care. • Real-time decision support improves diagnostic accuracy, reduces errors, and supports clinicians in managing complex patient cases more effectively. • AI tools like ours can cut down administrative overhead by 40%, allowing #nephrologists to focus on what matters most: their patients. Nephrology: A Specialty at a Crossroads The nephrology field faces unique challenges: • The disease burden of chronic kidney disease (CKD) continues to grow, affecting 37 million Americans. • 45% of nephrology fellowship positions remain unfilled, highlighting the critical shortage of specialists needed to manage this growing patient population. Improving Clinical Outcomes with AI Our tools empower nephrologists to enhance patient outcomes while optimizing workflows: • Saya™, the clinician’s co-pilot, provides real-time insights and risk stratification, enabling earlier interventions and improved patient care. • Lumi™ delivers predictive analytics and actionable financial insights, ensuring practices thrive while maintaining clinical excellence. • AI-powered predictive models can reduce hospital readmissions for kidney-related conditions by up to 30%, directly improving patient health and lowering costs. Join Us in Transforming Kidney Care At Nephrolytics, we believe that technology should empower healthcare providers, not burden them. Our AI-driven solutions are built to: • Combat burnout and improve provider satisfaction. • Enhance decision-making and clinical efficiency. • Address specialist shortages in nephrology and beyond. We invite healthcare executives, C-Suite leaders, and venture capitalists to explore Nephrolytics and join us in shaping the future of kidney care. Together, we can elevate patient outcomes and redefine what’s possible in nephrology. Visit our site to learn more and schedule a demo. Or reach out to our leadership Tina Gill Naeem Rahim Alice Wei, M.D. Ali Nadeem Hasan Ayub Jennie (Chaves) Atkinson

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