Ankit Jain

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Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2.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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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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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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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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A SAFE and convertible note had a baby and Wilson Sonsini Goodrich & Rosati was the midwife- out popped the D-SAFE! 👶🏽🎉 A new #climatetech funding instrument was announced by Elemental Excelerator last week. One of the company’s main goals is to support smaller, up and coming companies during the awkward “Scale Gap” phase between #VC funding and natural growth. It’s called the #DSAFE (Development Simple Agreement for Future Equity) and it aims to aid the process by giving companies a small amount of cash to overcome initial risks and expenses during the developmental stages of a product launch or company. How does it work? D-SAFE has a redemption clause meaning companies can choose to have their payments offered as a loan or convert to equity at the investor’s discretion. Funders and companies will have clear communication and expectations about where the money is going and how it will be applied to either the company or the projects at hand. Financiers and companies will work together with more #transparency, and that’s a great boon for #startups and VC alike. As of this month, Elemental has already invested using D-SAFE in eight different companies, including Dimensional Energy and Origen — both companies investing in innovative and out-of-the-box solutions to environmental issues! Seems like a win-win on the surface. What do you all think about the D-SAFE? Alfredo Coppola Momoka Ueda Tim Wagner Stephen D. Torres Olya Irzak Natsuho Toyama Noriya Tarutani Shin Ogawa

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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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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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What do you think about AI in senior care? Personally, I think we have to be careful with it. When AI presents itself as human, it can be confusing for seniors who may not have strong digital literacy skills. In fact, if they think they're talking to a person and not an AI chatbot, it can be dangerous if they're having a medical emergency or issue. However, there are some helpful use cases. I think Kristen Fischer's exploration of AI here is really interesting. "Artificial intelligence may be a useful communication tool to help older adults with cancer communicate with their doctors and have more of a say in their treatment," she says. AI can provide more context or explanation about certain treatment plans and translate the patient’s needs to clinicians in situations where the patient doesn't feel understood or heard. AI will never replace professionals, but it could help patients and clinicians better understand each other. Feel free to share your thoughts on this—I'd love to know if you think AI would be an effective tool for these situations. #AI #SeniorCare #FutureOfHealthcare

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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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“Over one million new articles are added to PubMed annually—about two per minute. No physician can keep up.” This highlights a challenge that PCPs face daily, managing countless chronic and acute conditions while adapting to ever-evolving clinical guidelines. We can’t expect our clinicians to act like machines. At RhythmX AI (https://rhythmx.ai), we address this by revolutionizing primary care with our generative AI-native precision care platform, developed collaboratively by our physicians, specialists, and AI teams. Our platform integrates advanced AI and longitudinal data to deliver patient-specific, evidence-based recommendations directly within clinical workflows. These hyper-personalized insights help physicians make informed decisions at the point of care, bridging the gap between research and practice. Trust is at our core. Our physicians rigorously validate every recommendation to ensure clinical-grade reliability, empowering clinicians to confidently rely on the insights we provide. #PioneerPrimaryCare #Hyper-PersonalizedCare #PCPBurnOut #HealthAI #ResponsibleAI

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My latest episode is about what you need to know about AI!

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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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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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The most downloaded datasets from the California HHS site? If you follow me on LinkedIn, you've probably guessed it - the Medi-Cal enrolled provider list and Suspended and Ineligible list are the top two, followed by other datasets by a wide margin. The California Health & Human Services Agency (Cal HHS) has a nice data hosting site that resembles data.cms.gov. The site looks slick and provides interesting insights, including visitor statistics and download metrics. It's fascinating to see how people are interested in provider eligibility and exclusion data for Medi-Cal billing. Looking at how organizations use these public datasets, compliance emerges as the dominant use case. This makes sense - when accessing government data portals, users are typically verifying facts related to government programs. The user base spans health plans, hospitals, IPAs, medical groups, and even commercial payers, as Medicaid suspensions often trigger reviews across the healthcare ecosystem. What's interesting is that this pattern extends beyond healthcare. On data.ca.gov, which hosts all kinds of government data, the most downloaded dataset is the directory of certified electricians. This shows a consistent theme - whether it's healthcare providers or electricians, people come to government sites primarily to verify professional credentials and eligibility. This raises an intriguing observation about how government data is used. The way organizations use these datasets - downloading lists to verify credentials and eligibility - is functionally similar to what modern Identity Provider (IdP) systems do, but through a much more manual process. What are your thoughts on the future of government data in healthcare identity verification? #Healthcare #DigitalTransformation #Compliance #HealthTech #GovTech #Identity

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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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mdhub (YC S24) is a clinical AI assistant for mental health clinicians, helping them run their clinics more efficiently. Mental health issues are a growing concern in America, with approximately 50 million Americans currently grappling with a mental health condition— a number increasing by 1.5 million every year. At the same time, the US is experiencing a massive shortage of 30,000 mental health clinicians in 2024, a situation costing the US health system $300 billion annually due to delayed treatment. Yet, clinicians are spending only 50% of their day with patients, while the rest of their time is consumed by admin-heavy work, delaying access to mental health care even further. With mdhub, mental health clinicians become 10x more efficient by automating tasks such as clinical documentation, drafting patient after-visit reports, pre-charting, and treatment planning. Over the last 6 months, mdhub has already successfully saved clinicians more than 100,000 hours in admin tasks. Congrats on the launch, Efrén A. Lamolda and Dominik Middelmann! 🚀 https://lnkd.in/gPCTzfy9

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One of my proudest moments as an investor occurred today as Alaffia Health announced its series A because it shows how the Aperture® Venture Capital vision of multi-level, multi-generational #impactinvesting is succeeding in the marketplace. Here’s the model in its most basic form : ✅As diverse fund managers with meaningful capital to allocate, we are changing the VC landscape every day just by doing our day jobs. ✅As Black/Brown investors with ~40 years experience collectively, Aperture GPs have access to talent /excellence that others do not, so our portfolio *organically* is more inclusive by race, gender and geography even while optimizing for financial outcomes (all about the alpha). ✅Our most successful portco’s are using financial #innovation to solve market problems that impact underrepresented demographics and underserved communities. Alaffia Health is a shining example of the impact portion of our overall fund thesis, and we couldn’t be prouder of TJ Ademiluyi and Adun Akanni, MPH, PMP - the dynamic brother-sister founder duo whose vision we have steadfastly supported on their journey. Congratulations to TJ and Adun from William Crowder and myself, as well as the whole Aperture team- Marjorie King Philip McKenzie Yves Louis-Jacques Tanvi Lal Michelle Dhansinghani Lisha Bell Katie Kelly Amy Chung Cindy Chong, CFA Brian Fernandes-Halloran Monroe France Jayden Pantel Darren Herman Evan Wladis Neal Triplett Thomas Scriven Peter Ammon Irina Bit-Babik Tim Milanich Rob Rahbari

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Instacart is a market leader when it comes to leveraging AI tools to boost productivity at work. I had an amazing conversation with JJ Zhuang, Instacart’s Chief Architect, who has shepherded over 50 AI-driven initiatives to market across the org. We explored what catapulted Instacart to the forefront of AI integration, supporting radically diverse use cases across various functional areas. One striking insight (echoed by Ethan Mollick and others elsewhere) is that you often don't need specialized tools for specialized work. Frontier models like GPT-4 can seamlessly adapt to a wide range of functions and use cases. They're not just for engineers; they're incredibly versatile tools used by marketing, comms, and legal teams. From generating code to proofreading and ideation, GPT-4 empowers every department to leverage AI for enhanced productivity and innovation. By integrating LLM’s like GPT-4 into their workflows, teams can streamline processes, automate repetitive tasks, and unlock bandwidth for new creative possibilities. How can you harness the power of AI in your own work to boost productivity? Share your thoughts below 👇 Want to hear more insights from JJ? Check out the full podcast episode by clicking the link in the comments.

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