Latest news with #DefineVentures
Forbes
17-07-2025
- Business
- Forbes
AI in Pharma: Startups, VCs And Big Tech Are Reshaping the industry
UKRAINE - 2022/01/10: In this photo illustration, Amazon Pill Pack logo seen displayed on a ... More smartphone and in the background. (Photo Illustration by Igor Golovniov/SOPA Images/LightRocket via Getty Images) Define Ventures' new report provides a clear framework for how enterprise healthcare, particularly Big Pharma, is transitioning from AI exploration to execution. It focuses on core categories, including clinical R&D, digital biomarkers, and AI-powered infrastructure, highlighting the opportunities and bottlenecks that matter most to large-scale industry players. While its lens is focused on enterprise adoption, the broader implications of these shifts extend across the full healthcare ecosystem—from tech-enabled care delivery to next-generation data models. This week, Define Ventures, one of the largest early-stage health tech investors, released its first-ever report about how the pharmaceutical industry is adopting AI. Based on insights from C-suite executives at 16 of the 20 largest pharma companies and tech leaders at AWS, NVIDIA, and OpenAI, the report confirms what many have sensed: pharma is finally moving from pilot projects to platform strategies. Over 85% of surveyed leaders now call AI an 'immediate priority.' AI is no longer a fringe experiment in pharma—it's becoming foundational. Yet its adoption remains uneven. Large pharmaceutical companies are investing in clinical trial optimization, predictive modeling, and intelligent infrastructure. Meanwhile, other parts of the healthcare ecosystem—from digital health platforms to new modalities of drug delivery—are innovating in parallel, often with greater agility. The enterprise lens of Define Ventures' report captures one powerful dimension of the shift, but the ripple effects reach well beyond. Many startups and compounding pharmacies are already using AI to reshape how drugs are developed, personalized, and delivered. As I wrote in A Dose of Disruption, compounders are leveraging AI and regulatory gray space to deliver alternatives to GLP-1 blockbusters at scale. In The $10 Billion Disruption, I outlined how these players are combining compliance, fulfillment, and AI-powered workflows—without waiting for Big Pharma's approval. The Market Is Massive—And Fragmenting The global AI in drug discovery market is projected to reach $11 billion by 2030, with AI in healthcare broadly exceeding $100 billion. Drug development is expensive, slow, and increasingly risky. The Inflation Reduction Act has intensified pricing pressure. GLP-1 drugs, such as Ozempic and Mounjaro, have triggered a scramble for real-world data, faster trials, and differentiated delivery. And consumer expectations are rising. Patients increasingly expect the same speed and personalization from healthcare that they receive from Amazon Prime. While legacy healthcare systems wrestle with integration, Amazon is bypassing them altogether. By acquiring PillPack and One Medical, it's constructing a parallel infrastructure—one that treats fulfillment, primary care, and patient data as a single, streamlined product. From Pilots to Platforms: Pharma's AI Maturity Leap The Define report outlines a significant shift: 93% of pharma leaders consider medical writing a top AI priority, while 80% are focused on reducing the cost of therapeutic discovery. Many companies are evolving from internal builds to hybrid models, balancing proprietary control with the speed of external partners. But enterprise deployment is still deliberate. Success hinges on regulatory alignment, governance, and measurable ROI. Even in low-risk workflows, such as documentation, vendors are expected to integrate seamlessly and demonstrate impact within months, not years. AI's Role in Drug Development: From Hypothesis to Molecule, Faster AI is no longer just an automation tool—it's being deployed to rethink the entire discovery process. According to Define's report, 80% of pharma executives are prioritizing AI to cut therapeutic development costs, and 77% are using it for target identification. Real-world examples include: These investments suggest that AI isn't just improving R&D—it's becoming its infrastructure. The Quiet Revolutionaries While legacy pharma players command headlines, a faster, more capital-efficient transformation is underway at the periphery. Startups are rebuilding pharmaceutical infrastructure from the ground up—deploying AI across diagnostics, prescribing, and fulfillment to create fully integrated, patient-facing platforms. As I detailed in my recent reporting, companies like BlueChew, Musely, and Dutch are leading this wave. Each has quietly built a direct-to-consumer, vertically integrated stack that combines telehealth, asynchronous care, and pharmacy fulfillment at scale. Musely, for instance, has surpassed $100 million in annual recurring revenue with under $10 million in outside capital. Dutch, meanwhile, supports over 100,000 pet telehealth visits monthly and recently expanded into AI tooling for veterinary clinics, further embedding itself into provider workflows. These aren't just digital wrappers on existing systems—they're engineered replacements for what traditional pharma has been too slow to build. Their defensibility stems from a combination of regulatory fluency (leveraging 503A/503B compounding exemptions), proprietary care protocols, embedded AI decision support, and deep integration between provider, patient, and pharmacy. Critically, these companies operate with superior margins by compressing CAC through owned channels and increasing LTV via subscription-based formularies and condition-specific treatment plans. Their data advantage compounds with every interaction, feeding back into personalization and fulfillment efficiency. They may not yet appear in JPMorgan presentations or pharma M&A pipelines—but they're reshaping patient expectations and redefining pharma distribution economics. The enterprise curve may lag, but the infrastructure—AI models, telehealth rails, and real-time fulfillment—is already here. For investors, this isn't just a healthtech play. It's a category-defining thesis on the unbundling and replatforming of pharma. One of the clearest case studies in this shift? The GLP-1 boom—where traditional commercialization has met its most agile challengers yet. GLP-1s and the New Commercialization Playbook The GLP-1 gold rush has become a proving ground for new commercialization models, especially among compounding pharmacies operating in the regulatory gray space. With demand for weight loss drugs like semaglutide and tirzepatide outpacing supply, compounders have stepped in to offer alternatives, often leveraging telehealth platforms and vertically integrated fulfillment to bypass traditional GTM barriers. While Big Pharma focuses on payer negotiations and clinical pipelines—often pricing millions out of access in the process—these challengers are capturing market share with speed, convenience, and direct-to-consumer access. The result is a pressure test not just for regulatory enforcement, but for how pharmaceutical products are brought to market in an era where infrastructure—not just IP—defines competitive advantage. The Startup Advantage: Platform Thinking, Real-World Speed Define's report offers a blueprint: lead with a wedge use case, deliver measurable ROI, and build for scale. Growth stage startups like Benchling, Owkin, Arda, and Octant are executing on that playbook. But some of the sharpest momentum is coming from startups solving infrastructure pain points: These are the companies that don't wait for pharma—they design around it. The Blurring Lines: Pharma, Telehealth, and Distribution Converge From Wisp and WellTheory to Dutch and Truepill, VC-backed platforms are now diagnosing patients, writing prescriptions, fulfilling meds, and delivering ongoing care—all inside tech-native stacks. What used to require partnerships across five vendors can now be done under one roof. Amazon's healthcare verticals, powered by Prime, One Medical, and PillPack, only reinforce this model. As silos collapse, AI becomes the connective tissue. And the companies winning aren't necessarily those with the best models—they're the ones who own the full experience. Conclusion: The Future of Pharma AI Is Up for Grabs Define Ventures is right: the next 12 to 24 months will shape how AI is embedded in pharma. C-suite leaders are moving from exploration to enterprise strategy. However, real disruption is already here—from startups building AI-native infrastructure to pharmacy-first platforms that treat compliance, fulfillment, and care as a single system. The next generation of AI leaders in healthcare won't be determined by size, but by execution. They'll be the companies fluent in science, compliance, and ROI, who design systems that pharma will ultimately depend on. For pharma executives, the imperative is to partner boldly and measure relentlessly. For founders, the moment is now to prove value before Big Tech outflanks everyone. For investors, this represents the power shift that healthcare has been slow to address. The AI revolution in the pharmaceutical industry is underway. But who defines it—still remains in play.
Yahoo
02-04-2025
- Business
- Yahoo
Define Ventures Appoints Carolyn Magill as Venture Partner
The former Aetion and Remedy Partners CEO joins Define to further drive innovation across provider, payer, and pharmaceutical sectors SAN FRANCISCO, March 11, 2025 /PRNewswire/ -- Define Ventures, one of the largest venture capital firms focused on early-stage health tech companies, today announced that Carolyn Magill, former CEO of Aetion, has joined the firm as venture partner. Magill, leveraging her 25 years of invaluable experience scaling companies and fostering innovation within payer, provider, and pharmaceutical organizations, will partner with Define founders to scale their companies and become category-defining companies. Carolyn is a seasoned healthcare executive and two-time CEO, having held executive leadership positions across multiple corners of the ecosystem. Prior to joining Define, Carolyn was CEO of Aetion, whose platform transforms real-world data into regulatory-grade evidence for critical healthcare decisions. She also previously served as CEO of Remedy Partners, the premier bundled payments software and services company, and as Executive Vice President of Payer Strategy and Operations at Evolent Health, where she helped drive the company's progression from startup through IPO. She also held several leadership roles at UnitedHealth Group, including Chief Operating Officer of its Community and State plan in New Jersey. "Carolyn's extensive experience across payer, provider, and pharmaceutical sectors makes her uniquely qualified to guide the next generation of health tech innovators," said Lynne Chou O'Keefe, founder and managing partner at Define Ventures. "Having had the privilege of partnering with Carolyn as an advisor for many years, we're confident her deep expertise, particularly in data and AI applications within the pharmaceutical landscape, will be invaluable to our partner companies." "Define represents the best of venture — an incredible founder community and a team of experienced operators who truly understand what it takes to build in healthcare," said Magill. "Throughout my career, I've worked across many corners of healthcare, and joining Define allows me to bring that experience to the earliest stages of innovation, helping founders turn ideas into impact." Carolyn's appointment complements Define Ventures' existing venture partners, Bruce Broussard, former CEO of Humana, and Frank Williams, co-founder and former CEO of Evolent Health, creating a powerful trio of industry leaders with comprehensive experience across payer, provider and pharmaceutical sectors. Together, they bring unparalleled strategic depth to Define's partner companies. Define Ventures has $800 million in assets under management and partners with companies at the seed, series A and series B stages. The firm impacts leading health tech entrepreneurs with its high conviction approach, partnering with over two dozen companies including Hims & Hers (NYSE: HIMS), Unite Us and Cohere learn more about Define Ventures, visit About Define Ventures Define Ventures is one of the largest funds focused on early-stage health tech companies. With $800 million AUM, we take a high conviction approach in partnering with companies in the earliest stages. We believe the future of healthcare will be defined by those who bring together a deep understanding of the healthcare ecosystem paired with a technology-driven mindset. Our team was built to this vision, bringing together founders and investors who built category-defining companies and delivered over $25 billion in exit value, including Livongo (NYSE: LVGO), Evolent (NYSE: EVH), and Hims & Hers (NYSE: HIMS). View original content to download multimedia: SOURCE Define Ventures
Forbes
27-03-2025
- Business
- Forbes
Why Layer Health And Investors Believe It Can Solve Healthcare AI's Scalability Challenge With A Fresh $21 Million
Artificial intelligence (AI) is enjoying its moment as the hottest area of venture investment, with more than $100 billion flowing into the sector last year. In healthcare, AI accounted for 30% of all venture funding in 2024 – and data shows 2025 is off to a strong start. That momentum continues today with the announcement of a $21 million Series A by Layer Health, an ambitious healthcare AI startup aiming to tackle some of the sector's thorniest issues and overcome the industry's biggest barriers to growth. The round was led by Define Ventures, with participation from Flare Capital Partners, GV and MultiCare Capital Partners. They join a cap table that already includes General Catalyst and Inception Health, which suggests credibility in the company's approach. Layer Health is applying large language models (LLMs) to perform data abstraction for medical chart reviews. Seemingly mundane and esoteric to the outsider, chart reviews are a foundational task that underpins a wide range of clinical and administrative workflows within health systems (and for other ecosystem partners). They can entail combing through vast volumes of some of the most fragmented and complex data in any industry – medical records – to answer highly specific, context-rich questions. Whether used to support clinical decision-making at the point of care or for administrative functions like clinical documentation improvement (CDI), chart review remains labor-intensive and highly technical. Depending on the use case, it can require scouring both structured and unstructured data – visit records, progress notes, imaging reports, lab results – and interpreting it with clinician-level understanding. At scale, this process becomes expensive and time-consuming, especially since it's currently often performed manually by highly trained professionals. These characteristics make chart review particularly well-suited for AI. LLMs excel at processing, summarizing and interpreting unstructured data with speed and precision. While there have been issues with LLMs 'hallucinating' at times, Layer Health contends that its models, which are trained on longitudinal data, can support its outputs with cited evidence, helping end users trust and verify the information presented. Still, deploying LLMs in real world healthcare settings – especially across disparate clinical environments – is no easy feat. Layer Health, which emphasizes the flexibility of its core AI platform and its ability to mitigate the hallucination problem, is navigating a complex and competitive market. Yet its founding team's deep experience and system-aware approach to the unique challenges of healthcare organizations could help differentiate it. While most high schoolers in the late 1990s were focused on malls, Nintendo 64 consoles or chatting on their Nokia phones, Layer Health co-founders David Sontag and Steven Horng were already discussing how they might one day make an impact on the world. Both were drawn to computer science and shared a strong entrepreneurial drive. Like many teenage friends, they eventually pursued separate paths. Sontag earned a Ph.D. in computer science and held faculty positions at New York University and the Massachusetts Institute of Technology. Horng went on to become a physician, earning additional degrees in computer science and biomedical informatics. He currently serves as an attending emergency physician at Beth Israel Deaconess Medical Center, where he also leads machine-learning initiatives. Both had promising, independent careers, but their desire to collaborate eventually brought them back together. Horng's day-to-day experiences in the ER gave him first-hand insight into the complexity and inefficiency of healthcare workflows and data systems. Starting in the early 2010s, the pair began building test applications – often with Sontag's students – within Beth Israel's (at the time) homegrown EHR. Over time, they explored a range of AI use cases for both clinical and administrative teams, iterating across many early models. 'We originally deployed an algorithm for detecting sepsis but quickly detected that was not where we were going to have a big impact,' said Horng. 'After making that discovery early, we pivoted to clinical workflow.' As LLMs began emerging as a transformative force in AI, the groundwork for Layer Health started to crystalize. One of the first widely cited papers on the use of LLMs in healthcare was co-authored by Sontag and another eventual co-founder, Monica Agrawal, a former MIT student who now is also a professor at Duke. By 2022, the collective experiences of Sontag, Horng, Agrawal and two additional former MIT students, Luke Murray (a software engineer from Google and SpaceX) and Divya Gopinath (a founding engineer at Snowflake-acquired TruEra), led to the formal founding of Layer Health. While medical chart data abstraction is at the heart of Layer Health's AI platform, its modular architecture is key to the company's strategy, according to Sontag and Horng. Each module supports a specific function but also contributes to and builds upon the others, enabling the system to learn and improve across use cases. The company's initial focus is a module that supports clinical registry reporting, which are used to track outcomes over time and support research, quality improvement and public health. The module has been deployed already at Froedtert & the Medical College of Wisconsin health network, where it was used to abstract data for quality reporting. According to Layer Health, its AI reduced the required time by 'more than 65%.' From there, Layer plans to validate one of its next modules: real-time clinical decision support at the point of care. 'The same chart review problem we're solving with our clinical registry module is faced by clinicians at the point of care,' said Sontag. 'For example, one of our next modules will focus on real-time clinical decision support to help automate clinical care pathways, leading to more reliable, high-quality care. This will not only improve patient outcomes, but will also naturally lead to more timely and accurate revenue capture, quality improvement and research.' Additional modules under development aim to support hospital operations and revenue cycle management by enhancing CDI and medical coding processes. The broader vision is to offer an enterprise-level solution – a foundational AI 'layer,' as the name implies – that spans departments and delivers cumulative ROI over time. Chart review isn't just essential for providers. Life sciences companies and clinical research organizations also rely on it to answer highly specific, nuanced questions, especially when evaluating patients for clinical trial eligibility. Manually reviewing charts to assess thousands of patients against inclusion and exclusion criteria is slow and costly, making it another ripe area for automation. Layer Health recently signed a multi-year agreement with the American Cancer Society (ACS), which will use its platform to extract clinical data from thousands of patient records tied to research studies, including the Cancer Prevention Study-3. The deal followed a successful pilot in which the AI accurately abstracted real-world data in a fraction of the time. Despite promising early traction, Layer Health faces a significant battle in a competitive market within an industry that's notoriously difficult to scale. Health systems often struggle with people- and process-related challenges that can't be solved by technology alone. Even within the same organization, different departments may have unique configurations, workflows and legacy systems that complicate implementation. The idea of a transferable, enterprise-wide AI solution is appealing, but in practice, significant barriers remain. Layer Health acknowledges these complexities and believes its platform is designed to meet them head-on. 'While many of healthcare's challenges are universal, some are uniquely local. Our enterprise platform also makes it possible for hospitals to easily configure, evaluate and deploy AI for chart review for their specific, local problems. It directly integrates with a hospital's electronic medical record and existing business intelligence platforms, easily extending a hospital's existing workforce to use AI chart review in a no-code / low-code way. The self-service SaaS platform is already in use by our life science customers,' said Sontag. Investors share this belief. Lynne Chou O'Keefe, founder and managing partner at Define Ventures, sees Layer's architecture as a key differentiator. 'Layer Health is designed to be a foundational AI platform, rather than a single-use AI tool. Many AI solutions in healthcare are highly specific to a single workflow or require extensive customization for each customer,' O'Keefe said. 'In contrast, Layer Health has built a generalizable LLM-based system that can interpret complex clinical data across multiple use cases. Its AI reasons across an entire patient chart, allowing health systems to derive clinician-level insights with minimal configuration. This ability to scale across different health system environments without excessive customization is a key differentiator.' Define Ventures, which previously announced $460 million across two new funds, saw Layer as a natural fit for its investment thesis. 'We believe the most successful AI companies will be those that solve deep, system-wide inefficiencies rather than offering surface-level automation. Layer Health embodies this thesis by addressing the immense problem of clinical data abstraction and chart review, a historically manual, error-prone, and resource-intensive process. By creating a generalizable AI infrastructure for clinical inference, Layer has the potential to become the foundational AI layer for healthcare organizations, making it a natural fit for our investment approach,' explained O'Keefe. Flare Capital Partners also sees value in Layer's low-friction deployment model and revenue-generating potential for health systems operating on tight margins. Parth Desai is Partner at Flare Capital @enidarvelo 'Layer Health's AI platform uncovers powerful revenue-generating insights for health systems, through a unique ability to unify clinical chart data with outcomes. Powered by breakthroughs in AI, Layer Health can also deliver these insights in real-time, with minimal integration and at a fraction of current costs,' said Flare Capital Partners Partner Parth Desai. 'This has made David and team a foundational and trusted partner to all healthcare organizations deploying AI.' Layer Health's goal to become the connective AI tissue across clinical, operational and research domains is ambitious. With early traction in clinical registry reporting and expanding partnerships across the provider and life sciences sectors, the company is positioning itself as more than a single-use solution. However, the path to widespread adoption in healthcare will demand not just technical strength, but also adaptability to deeply rooted workflows and fragmented infrastructure. Backed by $21 million in fresh capital and investors betting on foundational impact, Layer Health now faces its next challenge: demonstrating that its platform can scale, deliver meaningful ROI and adapt to healthcare's complex realities. If successful, the company may not only set itself apart in a crowded AI landscape—it could help define how large language models are integrated into the future of healthcare.
