At Flare Capital Partners, we are deeply passionate about partnering with technical founders who aspire to deliver step-function improvements in patient outcomes and operational efficiency. We are excited to share our investment in Keebler Health, an AI-native platform built for population health.

We are excited to lead an oversubscribed $16M Series A financing with Sands Capital, and Keebler’s existing investors include Freestyle Capital, MBX Capital, and New Stack Ventures. This new round of funding will support continued commercial growth, team expansion, and the infrastructure required to serve value-based care organizations nationwide.

Why Now: Convergence of Technology Readiness and Regulatory Demands

The U.S. healthcare system is undergoing a multi-decade shift toward value-based care, moving financial risk from payers to providers and increasing accountability for outcomes. While nearly 60% of payments are now tied to value, fewer than 20% involve two-sided risk and fully capitated models remain limited — highlighting significant runway. Within this transition, risk adjustment has become mission-critical as the economic engine of value-based care, directly shaping revenue, resource allocation, and patient outcomes, especially amid rising complexity from CMS-HCC Model Version 28.

At the same time, providers face structural data challenges that make accurate risk capture difficult. Roughly 80% of clinical data resides in unstructured formats — PDFs, faxes, and free-text notes — while even structured data under captures chronic conditions less than 60% of the time. This leads to systematic underreporting and reimbursement leakage, compounded by legacy workflows that remain manual, retrospective, and resource-intensive.

Regulatory pressure is also intensifying, particularly in Medicare Advantage. CMS has expanded RADV audits and tightened documentation requirements, demanding that diagnoses be supported by contemporaneous clinical evidence. In parallel, both CMS and private payers increasingly expect providers to act on risk insights through proactive population health management, creating a clear need for modern platforms that enable compliant, real-time risk adjustment and next-best-action workflows.

We believe the convergence of these dynamics — structural tailwinds in value-based care, rising regulatory complexity, and the maturation of AI — creates a compelling opportunity for a new generation of platforms built for risk-bearing healthcare organizations.

Why Keebler Health

Keebler Health is an LLM-native platform purpose-built for value-based care. Keebler’s LLM-native platform and micro-models unlock unstructured clinical data. They support provider groups in a truly patient-centered approach to population health. The platform supports Next Best Diagnostic Action and proactive risk management across an organization’s entire patient population.

From day one, the company has focused on unlocking unstructured clinical data. Its platform ingests and normalizes a wide range of data sources outside of traditional EMRs — including PDFs, XML files, faxes, and claims — reconstructing them into a unified patient view. Leveraging LLM-native models, Keebler identifies previously undetected chronic conditions and surfaces supporting evidence directly from source documentation, along with clear rationale and confidence intervals for provider review.

This emphasis on transparency and explainability has been critical in driving clinical trust and adoption. Rather than operating as a black box, Keebler enables providers to understand not just what the model is suggesting, but why.

Importantly, Keebler is moving beyond retrospective workflows. Its emerging “next-best-action” capabilities translate insights into prioritized clinical and operational interventions, helping providers act at the point of care. In doing so, Keebler is evolving from a risk adjustment tool into a broader clinical intelligence layer for value-based care.

Early results reinforce this differentiation via significant cost reduction. Across its customer base, Keebler has consistently won competitive evaluations against both legacy incumbents and well-funded startups.

We believe these outcomes reflect a durable product advantage — particularly in Keebler’s ability to effectively ingest and reason over unstructured data, combined with its flexible, customer-centric deployment model.

High “Clock Speed” Team with Lived Experience

We were impressed by Keebler Health management team’s hyper-focus on customer centricity and their ability to ship products at a pace that’s rarely seen in healthcare technology.

Isaac Park (CEO and Co-Founder) brings a rare combination of product instinct, technical fluency, and deep empathy for customers. He has consulted for value-based care organizations like Aledade, served as Product Management Director and Adjunct Assistant Professor at Duke’s Pratt School of Engineering, and founded Pathos Ethos, a digital product studio. He’s joined by Andrew Stickney (Co-Founder and President), who brings years of experience in innovation, development, and startups. Before founding Keebler Health, Andrew was the Chief of Staff at H1. Kevin Hill (Co-Founder and CTO) brings over a decade of experience in AI and technology, most recently serving as VP of Machine Learning at H1 and the VP of Startup Creation at Redesign Health. Isaac, Andrew, and Kevin are joined by Dr. Terrell Bacchus (Chief Medical Officer), who was previously the CMO at ConvenientMD and Regional Chief Clinical Officer at ChenMed.

Even before we initiated our due diligence efforts, we have received proactive endorsement from Keebler’s clients about the team’s responsiveness and ability to translate complex problems into practical solutions — attributes that have already proven to be a competitive advantage in the field, as customers have chosen Keebler for its technology and its founders’ hyper-focus on meeting and exceeding customer expectations.

Looking Ahead

The Series A funding will support expansion into adjacent use cases as that vision develops, including compliance and audit workflows, such as AI-enabled RADV audit readiness, as regulatory scrutiny of risk adjustment continues to evolve. Keebler looks to capitalize on the momentum from CMS’s Long-term Enhanced ACO Design (LEAD) model. LEAD’s published intent is to increase participation of higher-risk VBC contracts, for both MSSP and de novo entrants into VBC. This will serve as a significant tailwind for Keebler.

Keebler has demonstrated its ability to analyze large volumes of data and unlock insights from unstructured data by leveraging its LLMs and micro-models at an industry-leading cost. With the risk-adjustment product as a wedge, Keebler’s models can expand into other population health use cases and help its customers better assess standards of care, hospitalization risk, medication adherence, and other high-value clinical outcomes.

As we look ahead, Keebler will accelerate its product innovation and respond to existing clients’ demand across other population health use cases, including care gap management, provider performance optimization, and hospitalization risk prediction. As the platform scales, it has the potential to become an intelligence system for value-based care organizations.

Finally, Keebler will partner with payers and risk-bearing organizations to accelerate the development of RADV workflow solutions.

We believe Keebler is building a foundational platform for the future of value-based care — one that unlocks unstructured clinical data for the next generation of risk adjustment, population health management, and RADV Audit Readiness.

We’re excited to partner with Isaac, Andrew, Kevin, Terrell, and the entire Keebler team on this journey.

To learn more about Keebler Health, reach out to Ian Chiang (ian@flarecapital.com), Uma Veerappan (uma@flarecapital.com), or any member of the Flare Capital Partners team — we would love to hear from you.

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Flare Capital Partners is excited to share our investment in Qualified Health’s $125 million Series B financing. Qualified Health is building the infrastructure layer for AI in healthcare — combining a purpose-built platform, deep healthcare expertise, and close provider partnerships to help health systems safely and systematically unlock the value of AI. This financing is one of the largest Series B financing rounds to date for a technology company focused exclusively on healthcare AI, and we couldn’t be more excited about what lies ahead.

Why We Are Excited

Over the past few years, we have spent a lot of time with health system leaders trying to make sense of the generative AI and agentic AI moment.

The excitement is real. Nearly every healthcare executive we speak with believes AI will fundamentally reshape healthcare operations. The potential is enormous; generative AI and agentic AI could unlock hundreds of billions of dollars in value across administrative efficiency, clinical productivity, patient engagement, quality of care, hospital operations, IT & infrastructure spend, and many other critical areas. But the reality inside most health systems looks very different.

For years, AI adoption in healthcare has been incremental. Isolated teams and departments are experimenting with pilots. Effective pilots are stuck in groups that lack the resources to scale across the systems. Point solutions layered onto existing workflows added additional administrative burdens to staff. Health system leaders are increasingly recognizing that meaningful impact requires deploying AI across clinical and operational workflows at an institutional scale. Very few organizations have figured out how to deploy AI safely and systematically across the enterprise.

As a result, many health systems are stuck in the same place: AI experimentation without enterprise transformation. Scaling AI in healthcare demands unified data, seamless workflows, and rigorous governance. Health system leaders need more than a vendor — they need a strategic partner who can translate that strategy into real deployments that deliver measurable results and bring a purpose-built enterprise AI platform designed for healthcare operations.

That’s exactly what Qualified Health is building.

Flare Capital’s partnership with Qualified began well before this Series B. We had long admired the founders’ leadership across both healthcare and AI, and were fortunate to invest in the company’s first round of institutional financing through our early-stage Flare Scholar Ventures program, as it evolved from a pre-revenue company to serving health system customers. Since then, we have watched the team move with remarkable speed — earning the trust of health systems, building deep partnerships, and delivering measurable impact within months of deployment. We are thrilled to continue that partnership in this oversubscribed round alongside NEA, Transformation Capital, SignalFire, Frist Cressey, and others as Qualified builds what we believe will become a foundational enterprise AI platform for health systems.

The Purpose-Built Team

Healthcare AI is a notoriously difficult category to build in. It requires deep expertise across artificial intelligence, clinical medicine, healthcare operations, and enterprise software — disciplines that rarely coexist within a single founding team. Qualified Health’s founders bring all four.

CEO Justin Norden, MD, MBA, has spent his career at the intersection of AI and healthcare. Justin previously founded Trustworthy AI, a company focused on AI safety that was acquired by Waymo, and has worked for years at the forefront of applied machine learning. At the same time, he has remained deeply connected to healthcare through his role as an Adjunct Professor at Stanford Medicine’s Department of Biomedical Informatics Research, where he studies how AI can be safely deployed in clinical environments. Immediately before founding Qualified Health, he was a Partner at GSV Ventures. Justin is one of the rare founders in our space with both a medical and a computer science background.

If Justin represents the healthcare AI side of the equation, CMO Dr. Kedar Mate brings unparalleled experience in healthcare delivery and system transformation. As the former President and CEO of the Institute for Healthcare Improvement, the world’s leading organization for healthcare quality and patient safety, Kedar has spent his career working alongside health system leaders to redesign care delivery and improve patient outcomes at scale. Few leaders have a deeper understanding of how hospitals actually operate, and what it takes to drive meaningful change inside them. He is also a member of the faculty at Weill Cornell Medicine.

Beau Norgeot, PhD, leads Qualified Health’s AI team. His work has advanced the capabilities of clinical AI applications, personalized medicine, business operations, and ethical AI practices. He was previously the Vice President of AI at Elevance Health (NYSE: ELV). Before Elevance, Beau pioneered the development of human-in-the-loop clinical AI systems as Chief Data Officer at Lucid Lane.

Rounding out the founding team is COO Shantanu Phatakwala, who brings deep expertise in healthcare data infrastructure and operations. Shantanu previously served as Chief Data Science Officer at Haven, CIO at Passport Health Plan, and VP of R&D at Evolent — Shantanu brings firsthand experience scaling technology inside complex healthcare organizations.

Together, these co-founders represent a rare combination: world-class AI expertise, deep clinical credibility, and real operational experience inside healthcare systems. Just as importantly, they share a common vision for how AI should transform healthcare: not as isolated tools, but as an enterprise capability embedded across hospital operations.

It’s this combination of technical depth, healthcare insight, and product ambition that convinced us early on that the Qualified Health team has the potential to build a defining company in healthcare AI.

An Enterprise AI platform for Health Systems

Qualified Health partners directly with health systems to deploy AI across operational workflows. At the center of the company is a purpose-built enterprise AI platform that integrates across a health system’s data environment and enables the rapid deployment of AI-powered applications.

The platform brings together several critical capabilities. First, Qualified Health builds a connected and secure data foundation by integrating data from EHRs, operational systems, and other internal sources, alongside external datasets such as clinical guidelines and payer policies. This information is normalized into a healthcare-specific data layer and an AI-ready schema that supports analytics and AI applications. Second, the platform includes builder tools, allowing users to develop and deploy new solutions tailored to operational needs. Third, Qualified Health deploys AI-powered applications and agents directly into hospital workflows across a variety of use cases.

Finally, and perhaps most importantly, the platform includes a centralized governance, monitoring, and evaluation infrastructure that is designed for healthcare, including auditability, access controls, and decision traceability.

Together, these capabilities allow health systems to move beyond isolated AI pilots and begin deploying AI across the enterprise.

Measurable Impact in Months

One of the things that impressed us most about Qualified Health is how the company works with its customers. Qualified Health leverages forward-deployed product leaders with deep healthcare expertise to partner directly with health system teams. They identify operational challenges, implement solutions quickly, and continuously refine applications based on real-world feedback. This model ensures that solutions are built around real operational problems, not theoretical use cases.

We are incredibly excited that Qualified Health’s early deployments are showing measurable results in months.

At the University of Texas Medical Branch (UTMB), Qualified Health established a secure data foundation, deployed multiple assistants, and automated workflows, generating more than $15 million in measurable run-rate impact. At another health system customer, Qualified Health’s deployed solutions are on track to deliver nearly $30 million in annual value. Vendor ROI can often be elusive in healthcare. Furthermore, health systems often find it difficult to attribute tangible value to specific vendors, especially in a short time frame. We are thrilled to hear the way Qualified’s customers vouch for the company:

“Qualified Health has been an exceptional partner as we build and execute our AI strategy at UTMB,” said Peter McCaffrey, MD, Chief AI & Digital Officer at University of Texas Medical Branch. “Their team brings deep expertise and a real willingness to dive deep alongside our clinicians, operators, and leadership teams. Together, we have been able to focus on the highest-priority opportunities, move quickly from idea to implementation, and stay ahead of the curve as we navigate this period of rapid change. The ROI has already exceeded expectations.”

Qualified is rapidly scaling this impact to a larger cohort of health system partners, some of which include Mercy, University of Rochester Medicine, Emory, Jefferson, and all eight institutions of the University of Texas System. These health systems are partnering with Qualified Health and learning together in real-time as they transform how their institutions operate, sharing lessons on governance, workflow redesign, and operational impact.

What’s Ahead

While the first wave of healthcare AI adoption focused on algorithms and narrow applications, the next wave will be defined by enterprise platforms that enable health systems to deploy AI safely and systematically across their organizations. Qualified Health’s world-class team is meeting this moment with a best-in-class enterprise AI platform.

We are privileged to continue our partnership by investing in Qualified’s Series B financing and look forward to supporting the team as they scale the platform and drive meaningful impact for health systems, and ultimately for the millions of patients those systems serve.

To learn more about Qualified Health, reach out to Ian Chiang (ian@flarecapital.com), Tara Sullivan (tara@flarecapital.com) or any member of the Flare Capital Partners team. We would love to hear from you.

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Read the original announcement here!

The Qualified Health team is growing! Please see open roles here.

At Flare Capital Partners, we are excited to share our lead investment in Daffodil Health, a company building a next-generation, AI-native pricing and payment integrity platform for U.S. payers. Today, Daffodil Health announced $16.3M in Series A funding, bringing its total funds raised to $20.9M.

Why We’re Excited

Medical cost growth continues to outpace wage growth, placing unprecedented pressure on employers, members, and the payers that serve them. The industry has long relied on utilization management, network design and post-pay integrity to control costs, with gradual innovation driving incremental improvements. However, not enough has been done to address the root cause of cost growth, how claims are priced, repriced and adjudicated across complex commercial contracts.

At the same time, payers — particularly third-party administrators (TPAs) and regional plans — are constrained by legacy, opaque vendors that dominate out-of-network repricing through percentage-of-savings models. This multi-billion-dollar market remains largely outsourced, limiting flexibility and preventing payers from retaining the full economic upside of better pricing decisions. Daffodil’s transparent, SaaS-based approach allows payers to bring this function in-house, reclaim control over pricing, and internalize savings.

For decades, payers have been dependent on a fragmented ecosystem of legacy vendors to manage out-of-network (OON) pricing and pre-payment integrity. These solutions are typically rule-based, retrospective, and opaque — operating as black boxes that extract a percentage of savings while offering limited visibility into how decisions are made or value is created. That lack of timely insight contributes to widespread improper payments across the system; for example, 31% of inpatient and 32% of outpatient claims submitted to commercial payers weren’t paid out for more than three months in 2023, underscoring the scale of inefficiency created by legacy approaches. Importantly, the insights they provide are static. As regulatory scrutiny intensifies and employers gain access to detailed pricing data that was not historically available, responsibility is shifting toward plan sponsors to more actively manage healthcare costs — bringing new fiduciary and legal obligations that make opaque, black-box solutions increasingly untenable.

These limitations are a challenge for all payers, but are especially acute for TPAs and regional payers, which collectively serve the majority of commercial lives yet often lack the infrastructure to manage complex pricing and integrity workflows at scale. Daffodil is purpose-built to meet this moment.

Why Now: A Generational Shift in Pricing and Payment Integrity

Several structural forces are converging to create a “why now” moment for Daffodil:

• Employer pressure is intensifying. Premiums and benefit costs are rising at their fastest pace in over a decade, and employers are demanding measurable, auditable control over unit cost — not opaque vendor promises.
• AI turns complex pricing and integrity rules into real-time decisions. By analyzing dense policy logic, pricing benchmarks, and claims data at scale, AI enables more accurate and proactive decisions — shifting payment integrity from retrospective “pay-and-chase” to real-time, pre-pay prevention while reducing administrative friction and provider abrasion.
• Legacy point solutions can’t keep up. Payers are moving toward unified, AI-native platforms that are configurable, transparent, and built to scale, as legacy point solutions struggle to handle increasingly complex pricing logic, regulatory requirements, and real-time decisioning.

Daffodil sits at the intersection of these trends, offering a platform that is not only more transparent and accurate, but also structurally aligned with payer incentives.

Why Daffodil

Daffodil is designing an AI-enabled smart plan platform to replace opaque, legacy repricing and payment integrity systems with a unified, dynamically configurable, and auditable solution.

The company’s initial products focus on out-of-network repricing and pre-pay payment integrity, enabling payers to automatically price, defend, and edit claims in real time. Unlike incumbent vendors, Daffodil gives payers direct, dynamic control over pricing logic with full auditability into every decision. This is powered by an AI-native architecture that learns from claims, pricing decisions, and outcomes over time, which continuously improves accuracy, defensibility, and automation in ways static, rules-based legacy systems simply can’t.

In our diligence, one theme came through clearly: customers genuinely love the product and are realizing meaningful ROI almost immediately, in some cases approaching 10x returns. Payers consistently described Daffodil as “easy to understand and easy to consume,” with a level of modularity “down to the code level” that felt meaningfully differentiated from legacy vendors. Early customers are seeing materially higher margins relative to legacy solutions, alongside faster cycle times and improved provider engagement with several calling the product “very profitable for us.” In a market where “everybody has OON claims problems” and “everybody hates percentage savings models,” customers highlighted Daffodil’s ability to deliver value on significantly shorter implementation timelines than legacy vendors. Most importantly, through the transparency Daffodil offers, payers can understand and configure exactly what is happening, removing the “black box” completely.

Over time, Daffodil aims to become the core pricing and payment infrastructure for payers: a modular, AI-native platform that supports longitudinal pricing, benefit design customization, and real-time adjudication across the claims lifecycle.

Product Built for the Real World

Daffodil’s platform is purpose-built to integrate seamlessly with existing claims systems, support configurable plan designs, and provide granular reporting that helps payers and employers understand exactly how pricing and payment decisions are made.

Its out-of-network repricing engine aggregates multiple reference datasets to generate defensible reimbursement recommendations, complete with a full audit trail. An embedded AI co-pilot assists negotiators in real time, shortening settlement cycles and reducing administrative burden.

Building on this foundation, Daffodil’s pre-pay payment integrity module identifies anomalies and errors before payment occurs — shifting integrity from a reactive to a proactive discipline. Future modules, including dynamic member cost-sharing, extend this pricing intelligence all the way to the member experience.

A Team That Knows the Problem Firsthand

We are especially excited about Daffodil’s founding team, which possesses deep domain expertise across payer operations, enterprise software, and AI.

CEO Navin Nagiah is a three-time technology entrepreneur and former SVP of Product at MultiPlan, where he gained firsthand insight into the structural inefficiencies of legacy repricing systems. CTO Shuo Yang brings world-class engineering experience from Google and Huawei, while Adrienne Cromwell, VP of Customer Operations, adds decades of operational expertise from within the incumbent ecosystem.

This combination of insider knowledge and technical depth has enabled Daffodil to move quickly from concept to commercial traction.

Looking Ahead

Daffodil is already working with leading TPAs and health plans, delivering hard-dollar savings and faster adjudication. As the company expands into additional payment integrity modules and prepares to scale to additional TPAs and payers, we believe it is well positioned to become a foundational platform in payer cost management.

At Flare Capital, we believe the next era of healthcare infrastructure will be defined by transparency, automation, and intelligence. Daffodil Health exemplifies this shift, and we are thrilled to partner with Navin and the team as they build the future of pricing and payment integrity.

To learn more about Daffodil Health: reach out to Parth Desai (parth@flarecapital.com), Uma Veerappan (uma@flarecapital.com), Alyssa Tsenter (alyssa@flarecapital.com) or any member of the Flare Capital Partners team — we would love to hear from you.

We’re excited to be on this journey together!

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Axios coverage here.

Author: Amanda DiTrolio

Introducing the Flare Scholar Class of 2026

We are thrilled to introduce the Flare Scholar Class of 2026 — a group of emerging industry leaders joining the program at a pivotal moment for healthcare. As the industry navigates the transition of AI from experimentation to real-world deployment with increasing operational strain across care delivery, the need for thoughtful, cross-disciplinary leadership has never been greater. The continued pressure to do more with less is real and profound.

This year we welcome 60 new Flare Scholars to the program, bringing our network of Rising Stars to 506 current and alumni Flare Scholars. The Class of 2026 represents a diverse and highly accomplished group of individuals spanning business, medicine, engineering, and technology, all united by a shared interest in transforming the business of healthcare. They join us from leading academic institutions, healthcare and technology organizations across the industry, reflecting both the complexity of today’s challenges and the breadth of perspectives required to solve them.

Below is a snapshot of the Class of 2026:

• Representation from over 17 academic institutions and 29 industry organizations. Academic institutions include Brown University, Cornell Tech, Harvard Business School, MIT Sloan, Stanford University, University of California, Berkley, University of Pennsylvania, and more. Industry organizations include AdventHealth, Apple, Atlantic Health System, Boston Children’s Hospital, Cleveland Clinic, CVS Health, Humana, LabCorp, Memorial Hermann, Novo Holdings, NVIDIA, UnitedHealth Group, and many more.
• A growing cohort of technologists and engineers with backgrounds in AI, data science, and automation — reflecting the sector’s shift from promise to practice.
• >50% female representation and highly diverse class. For the fifth year in a row, this Flare Scholar class achieved gender parity and represents a highly diverse group of rising leaders, reinforcing our belief that inclusive teams are essential to building durable, generational healthcare businesses.

A Class Designed for Where Healthcare Is Going

What distinguishes the Class of 2026 is not only their individual accomplishments, but how well positioned they are for the moment healthcare is in today and where it is headed.

Healthcare transformation does not happen in silos; it requires leaders who can bridge technology, operations, clinical realities, and capital. This year’s Flare Scholars class includes operators navigating real-world system constraints, technologists building and deploying AI in applied healthcare settings, and clinicians working at the intersection of care delivery and innovation. They bring experience from health systems, payers, life sciences organizations, technology companies, and academic research institutions.

Together, this blend of technical depth, operational experience, and clinical insight reflects a defining strength of the Flare Scholar community — and the next generation of builders shaping how healthcare is delivered, financed, and scaled.

About the Flare Scholar Program

For those less familiar, the Flare Scholar program is an annual professional development initiative designed for aspiring healthcare leaders, technologists, and mid-career executives to gain firsthand exposure to healthcare technology and venture capital.

The foundational tenets of the program are three-fold:

1. Provide real-world exposure to early-stage healthcare technology innovation.
2. Enable participants to learn directly from Flare Capital’s investment team and industry partners.
3. Accelerate opportunity and encourage inclusive growth for individuals interested in building and investing in healthcare.

Through hands-on programming, candid conversations with operators and investors, and direct exposure to early-stage innovation, Flare Scholars gain an inside view of how healthcare technology companies are built, scaled, and financed. Just as importantly, they become part of a peer network that often extends far beyond the formal program year.

Each year we receive several hundred applications, making the Flare Scholar program a highly competitive and differentiated opportunity. Interest in the program continues to grow, which excites us as we look to continue to broaden and diversify the backgrounds of our Flare Scholar community.

Why the Flare Scholar Program Matters Now

Talent has always been foundational to success in venture, but in healthcare — an industry defined by patient-centricity, complexity, regulation, and longtime horizons — it is especially critical. At Flare Capital, we believe investing in people is as important as investing in companies.

While our day-to-day work centers on partnering with exceptional founders transforming the business of healthcare, the Flare Scholar program represents our long-term bet on the next generation of leaders across the ecosystem. By identifying, supporting, and connecting rising talents early, we aim to help shape a more thoughtful, resilient, and innovative future for healthcare.

Flare Scholar Alumni: Impact by the Numbers

While the Flare Scholar program is structured as a year-long experience, its impact extends far beyond the formal program. The true strength of the initiative lies in the enduring network it has created — one that continues to grow, evolve, and contribute across the healthcare ecosystem.

Today, Flare Scholar alumni are building and leading healthcare technology companies, shaping product and strategy at scaled organizations, investing across the industry, and advancing care delivery with a technology-first mindset. Their collective impact spans roles, sectors, and geographies, underscoring the long-term value of investing in emerging healthcare leaders. The data below offer a snapshot of that impact to date — highlighting where Flare Scholar alumni are today and how they are helping transform the business of healthcare.

Flare Scholar Community in Action

Throughout the year, Flare Scholars engage with one another and with the broader Flare Capital network through a variety of touchpoints, including:

• Compass Summit 2025: Compass Summit is Flare Capital’s flagship platform convening, hosted this year at Fenway Park, bringing together founders, operators, and industry leaders to accelerate go-to-market excellence and commercial growth in healthcare.
• Flare Capital Community Events: Regional meetups and firm-hosted events designed to foster connection across cohorts in key geographies, including Boston, NYC, SF, Chicago, and Nashville.
• Flare Capital Pickleball & Happy Hour: Flare Scholars from across our ten cohorts joined us in Boston for our annual healthtech community event where we connected over happy hour and some friendly competition in our pickleball tournament.
• Flare Scholar Day: During our Flare Scholar Day, Flare Scholars from across classes led and participated in dynamic panels with our industry partners and guest speakers, exploring the latest themes driving innovation in healthcare.
• Flare Scholar Ventures: An initiative to provide $100k of pre-seed or seed capital to back companies that are founded, advised, or supported directly by Flare Scholars. To date, the firm has invested in 36 companies since 2020 that have gone on to raise $178 million of total capital.

Beyond formal programming, these moments underscore what makes the Flare Scholar community special: Flare Scholars are not just participants — they are contributors, connectors, and collaborators.

Looking Ahead

As we welcome the Flare Scholar Class of 2026, we are energized not only by who these individuals are today, but by the leaders they will become. Healthcare transformation is a long-term endeavor — one that requires patience, collaboration, and a deep understanding of both innovation and operations.

The Flare Scholar program will continue to evolve alongside the healthcare ecosystem itself, grounded in our belief that investing in people is essential to building a durable, future-facing firm.

Congratulations to the Flare Scholar Class of 2026. We are excited to partner with you and to see the impact you’ll make in the years ahead.

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At Flare Capital Partners, we are thrilled to announce our lead investment in the latest financing round for Peer AI, a pioneering agentic AI platform transforming life sciences regulatory documentation. Today, Peer AI announced $12.1 million in total funding, led by Flare Capital Partners and SignalFire. We are also excited to be joined by our friends at Greycroft.

The Team

When investing at the earliest stages, our conviction begins with the people. Peer AI was co-founded by Anita Modi (CEO), Christopher Ceppi (CTO/COO), and Ravi Ramachandran (CSO) — a team with the rare combination of deep industry experience, technical expertise, and firsthand understanding of the regulatory documentation bottlenecks facing life sciences companies.

Anita previously served as Chief Quality and Business Transformation Officer at Science 37, where she led corporate transformation, quality, and compliance initiatives. It was there that she met Christopher, who as Chief Technology Officer built and scaled the company’s technology and R&D functions. Ravi, who had been a customer of Science 37 in his leadership roles at PPD, later joined them as Peer’s Chief Scientific Officer, bringing the perspective of someone who has brought 5+ drugs to market from the biotech and CRO side during his career.

This core team is supported by exceptional engineering talent — including former leaders from Google — as well as regulatory and medical writing professionals embedded directly into the company. They are further guided by a group of world-class advisors, including senior executives from Veeva, Databricks, MosaicML, H1, Formation Bio, and Bristol Myers Squibb. Across diligence, both existing partners and prospective customers consistently cited Peer AI’s unique blend of technical depth and domain fluency as a key differentiator.

The Market Opportunity

Documentation is a consistent and mission-critical workflow across every stage of drug development. Each new drug candidate generates 100,000 to 200,000 pages of regulatory documentation — ranging from toxicology reports and study protocols to clinical study reports and investigator brochures.

The current state of medical writing is manual, slow, and error-prone. Nearly one-third of FDA submissions contain data quality issues, and more than 70% of IND, BLA, and NDA applications are initially rejected due to incomplete or inaccurate data. These inefficiencies cause costly delays, non-compliance risks, and bottlenecks to getting life-saving therapies to patients.

Generative AI is poised to transform this landscape. According to McKinsey, AI could unlock $60B–$110B in annual value across the pharmaceutical industry, with clinical development documentation alone representing a $13B–$25B opportunity. Automating submission content could make regulatory filings 40% faster, 50% more cost-efficient, and twice as accurate. The time is ripe for innovation.

Why Peer AI

Peer AI is building a purpose-built, agentic AI platform that automates the end-to-end process of regulatory documentation — data extraction, authoring, and quality control. The “agent team” is combined with an intuitive AI-powered user interface that allows medical writers to apply their subject matter expertise at critical control points during documentation workflows. The platform integrates seamlessly into existing workflows: medical writers can upload or integrate into systems with structured and unstructured data, apply configurable templates, and generate drafts ready for multi-author review.

Customers have already seen the benefits. Peer AI has worked with large biopharma companies as well as mid-sized innovators, consistently outperforming incumbent CROs and legacy vendors.

Key features of Peer’s approach include seamless integration with existing processes, full traceability to source data for compliance, and the ability to automatically update drafts as new data becomes available.

Looking Ahead

We believe Peer AI is well-positioned to become the category-defining platform, creating an intelligent regulatory workflow that connects documentation, data, and decision-making to speed drug approvals. Its differentiated product, strong early customer validation, and exceptional founding team give us conviction in its ability to scale.

At Flare Capital, we are thrilled to partner with Anita, Chris, Ravi, and the entire Peer team as they transform how life sciences companies bring life-saving therapies to market faster, safer, and more efficiently.

To learn more about Peer AI: reach out to Ian Chiang (ian@flarecapital.com), Uma Veerappan (uma@flarecapital.com), or any member of the Flare Capital team — we would love to hear from you.

Careers: Peer AI is hiring across engineering, commercial, and product. Explore open positions here.

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Axios coverage here

Today we are excited to introduce our latest initiative: the Flare Capital AI Advisory Council. This dynamic new Council convenes senior executives from our strategic partners and leading technology experts to accelerate innovation at the intersection of healthcare and AI.

We are thrilled to tackle this work with our incredible founding Council members, which include industry leaders from world-class healthcare and technology organizations — including, Advent Health, Amazon Web Services, Amgen, Cincinnati Children’s, CVS Health, GuideWell, Hartford HealthCare, Medical Mutual, MedStar Health, Memorial Hermann, and Microsoft.

Further, we have established a distinguished network of executives from leading technology companies — NVIDIA, Anthropic, Google, OpenAI and Apple — who, together with leading AI researchers like Flare Capital Industry Advisory Board Member, Zak Kohane, MD, PhD, will collaborate with and complement the Council.

We dig into details of the Council and the roadmap below, but first a bit more on why now and how this Council fits into our proprietary Flare Capital strategic engagement model that powers a unique growth engine for the remarkable founders we have the pleasure of partnering with.

How the AI Advisory Council Is Unlocking and Accelerating a New Dimension of Strategic Engagement For Founders and Industry Partners

At Flare Capital, we have spent over a decade partnering with founders to transform the business of healthcare technology. Since 2013 we have backed healthcare technology companies at the earliest stages, supporting them across the entirety of the journey — from product-market fit to identifying initial customers to scaling sustainable growth models, through to successful exits.

Core to the success of our work has been our robust and highly engaged community of strategic industry partners that span the healthcare landscape — from leading health systems and payers to global pharma and enterprise technology organizations. These organizations account for nearly a quarter of healthcare spend annually, and do not just write checks. They go on to become early customers, co-creators, and early champions for the solutions our partner companies bring to market.

Collectively, they have contributed nearly $1 billion in revenue to Flare Capital backed companies — a clear signal that distribution and trusted relationships are often what separate the winners from the rest.

And in today’s rapidly evolving healthcare AI landscape, that edge will be decisive.

“There is a race in healthcare AI where distribution will become what differentiates the winners and losers. That is why we are looking for a strategic partner.”

We have heard this from countless founders that walk through our doors, and we agree: to win, you need to strike the right balance of strong technical talent, a robust product platform, and the right chorus of relationships to find breakout success amongst the competitive and rapidly evolving healthcare AI sector. That is why we are excited to announce the launch of the Flare Capital AI Advisory Council.

A Council for Collective Insight and Practical Action

While foundational technologies continue to rapidly advance, we believe the winners will be those who can integrate, scale, and deliver results across complex healthcare environments.

The AI Advisory Council is a cross-industry initiative designed to bring together senior executives from our strategic partners with leading thinkers and operators in healthcare AI. It is a Council for collaboration — not just conceptually, but operationally. The Council will help our investment team and founders answer key questions: What is working? What is not? Where are the gaps, and how can we close them together?

We officially launched the Council earlier this summer with an energizing kickoff at NVIDIA’s headquarters in Santa Clara, where we convened top healthcare leaders alongside NVIDIA’s senior healthcare AI executives. The discussion spanned real-world AI deployments, implementation bottlenecks, and shared priorities across payers, providers, pharma, and tech. The meeting set the tone for what’s to come: candid conversations, practical insight, and aligned effort to accelerate what is working and further shape the future of the industry.

What the AI Advisory Council Offers

The Council was designed with founders at the center. Its work is meant to accelerate company building while also sharpening our perspective as investors and strategic partners. Our approach is anchored in five value pillars:

1. Founder-Focused Strategic Insight: The Council creates access to unfiltered, real-world perspective from leaders across health systems, payers, pharma, and technology. These insights help entrepreneurs understand how buyers are deploying AI, where budgets are shifting, and how innovation teams are structured — providing a clearer roadmap for scaling.
2. Practical Evaluation of Emerging Tools: Founders benefit from structured, cross-functional feedback on their products and models from executives who evaluate and implement solutions at scale. This reduces wasted cycles and sharpens go-to-market fit. It also offers an early, collective read on which approaches are resonating with the market, informing both investment decisions and post-investment support.
3. Faster Pilots and Adoption Pathways: The Council helps shorten sales cycles by surfacing pilot opportunities and sharing proven playbooks. Founders get access to referenceable use cases that build credibility with the broader market.
4. Market Benchmarking Across Sectors: Founders gain a comparative view of adoption strategies and outcomes across different healthcare verticals, informing product roadmaps and commercialization strategies. The Council helps us understand how sectors are evolving, enabling us to align our investment theses with the realities of adoption on the ground.
5. Early Visibility into Innovation: Founders showcase their companies in front of stakeholders actively seeking new AI solutions, sparking opportunities for partnerships, revenue, and co-development. It offers a front-row seat to what is next in healthcare AI innovation.

Looking Ahead

We are grateful to our partners who join us in shaping the AI Advisory Council and to the founding members who have leaned in with energy and purpose. This initiative reflects a shared belief: that AI in healthcare won’t scale because of a single breakthrough, but through aligned effort, responsible experimentation, and collective insight.

By bringing the right people to the table — at the right time — we can move faster, smarter, and with more impact. We are actively expanding the Council, adding new members from leading healthcare and technology organizations.

Alongside our continued engagement with the Council, we are actively advancing our perspectives on the sector — most recently reflected in the publication of our updated healthcare AI outlook , Billions in Play: Healthcare AI’s Race for Market Dominance.

If you are interested in learning more about our investment work across healthcare AI, reach out to us at flare@flarecapital.com.

About Flare Capital Partners

Flare Capital Partners is the leading healthcare technology venture capital firm advancing innovation-driven companies to improve positive health outcomes, broaden care access, and lower healthcare costs. We partner with exceptional founders solving healthcare’s hardest challenges, supporting each with our deep sector expertise, unparalleled industry resources, and proven access to commercial opportunities. We are investors in several leading healthcare AI companies, including SmarterDx, Cohere Health, Suki AI, Layer Health and Axuall Health among others. Our team of established investors and senior operating executives has invested in 70+ companies and has nearly $1 billion in assets under management. To learn more about Flare Capital Partners, please visit flarecapital.com or follow us on LinkedIn, YouTube, or Spotify.

Table of Contents

1. Introduction
2. Bullish Buying: Healthcare’s Accelerating Rate of AI Adoption
3. A Winner Take All Market: Investor Shift to Selective Value Concentration
4. The Winning Playbook: Key Attributes of Leading Healthcare AI Companies
5. Choppy Seas Ahead: Threats and Opportunities for Leading Companies
6. Conclusion
7. Strategic Imperatives for AI Buyers and Builders

Introduction

Much has changed since we published our perspectives on the healthcare industry’s accelerating adoption of artificial intelligence (AI), last year. Since then, breakthroughs in model architectures and training techniques have dramatically boosted AI performance while reducing compute and data requirements. At the same time, the proliferation of specialized AI hardware, managed cloud services, and open-source model hubs has democratized access, enabling rapid deployment of generative and agentic systems across industries. It’s therefore not surprising that AI usage and adoption are skyrocketing as model performance and user value improve just as dramatically as inference costs fall. Simply put, AI is becoming an indispensable part of our personal and professional lives.

This has profound implications for the future of work in healthcare, where AI is playing an integral role as an equalizer, extender, and enhancer of the workforce. The results so far are significant and tangible, fueling accelerated adoption. Consider the fact that two-thirds of physicians report currently using healthcare AI products, a 78% year-over-year usage increase. This rate of uptake is remarkable, especially compared to the rate of electronic health records (EHR) adoption over the last decade. There is a meaningful level of product-led “pull” driving this cycle of technology adoption, a stark contrast to the “push” that drove many previous adoption cycles.

As a result, healthcare enterprise leaders now view AI as much more than just an enabler of operational productivity. In fact, enterprise AI strategies are informed by recruitment and retainment priorities. In response, healthcare leaders are universally increasing their budgetary allocations to AI, concentrating on a handful of distinct categories of solutions where there is clinician and patient demand as well as proven ROI to rationalize budget expansion. External partnerships are also proving more successful than internal AI builds, creating strong tailwinds particularly for emerging AI solutions.

This has attracted a crowded cohort of emerging AI companies, across categories. Whereas just a year ago, the leading AI companies in each category were raising capital simply to capture market share, today many are seeking to break out of the pack by growing horizontally and bridging categorical boundaries. Meanwhile, incumbents are protecting moats by touting their own, emerging AI capabilities. And quietly, new upstarts are tapping into the forefront of technological progress to develop their own leads. How boundaries shift as leading companies strategically maneuver will shape the next few years of healthcare AI.

In this analysis we:

• Analyze market signals to gauge where we are in the healthcare AI adoption cycle
• Dissect funding data to understand where AI is creating durable value in healthcare
• Share our perspectives on why certain categories of healthcare are currently more primed for AI-led disruption than others
• Study the leading AI categories and companies to highlight key factors driving their success and threats they may face
• Offer suggestions for buyers scaling their AI partnerships, and builders looking to strengthen their moats

Bullish Buying: Healthcare’s Accelerating Rate of AI Adoption

The healthcare sector is emerging as a leading adopter of AI, bucking its longstanding reputation as a laggard of novel technology adoption. The charts below, sourced from the US Census Bureau and included in BOND Capital’s most recent AI Trends report, suggest that the healthcare industry’s rate of AI adoption is the fourth fastest across major sectors of the US economy in 2025. Since we started tracking this metric a year ago, adoption rates in healthcare have doubled.

As mentioned previously, this phenomenon is largely being driven by fundamentally different enterprise buying behavior. Earlier this year, we surveyed over 30 senior executives across our strategic healthcare partner base to understand their technology and AI priorities. The composition of the surveyed group and summary of survey insights are included in Figure 2, below.

On average, leaders across all sectors devote around 10% of their information technology (IT) budget to AI. Over half of the healthcare executives we surveyed allocate an even larger part of their IT budgets to AI, signaling a sustained, rather than fleeting, commitment to the technology. It is worth noting that of the four leaders who signaled that they were devoting more than 25% of their IT budgets to AI, three were hospital executives. As discussed in more detail later, hospitals seem to be outpacing the rest of the healthcare sector in AI adoption.

This pace of AI adoption is expected to persist, given nearly 83% of the healthcare executives we surveyed planned to increase their AI and technology budgets over the next 12 months. Interestingly, 75% of these leaders are not defaulting to incumbent solutions for AI needs, but instead seeking best-of-breed emerging technology solution partners, underscoring a particular enthusiasm about emerging AI products.

This explains the frenzied levels of venture capital funding we are seeing in emerging healthcare AI products, shown in Figure 3 below. Through the first half of this year, nearly 58% of healthcare deals were in AI companies, a record pace. Importantly, unlike previous AI hype cycles, it appears that we have also broken out of the typical funding plateau of “testing and learning” that seems to accompany each major AI advancement, and into accelerated adoption. This sharp spike in funding is undoubtedly a function of investors reacting to strong commercial buying signals buried in unprecedented growth rates and pipelines, but also the fact that AI is becoming a standard feature in a broader swath of healthcare product offerings.

A Winner Take-All Market: Investor Shift to Selective Value Concentration

Unsurprisingly, the demand for high-quality healthcare AI companies that can rapidly capture growing budgets is exceeding historical supply, which shows up in valuation data. The charts in Figure 4 below contain valuation data for AI companies selling into hospitals, health plans and life sciences / pharmaceutical companies in 2024 vs. 10-Year Averages.

The average valuation of a healthcare AI company raising capital in 2024 was 50% higher than the healthcare industry average last year. The contrast is most stark in early-stage funding data, but also in companies selling into hospitals, where again, it appears that early-stage AI solutions seem to have taken a faster foothold. This phenomenon and its derivative impact have been referenced in several health insurer earnings calls throughout the year.

Another phenomenon we have been tracking is one of capital concentration. While the charts above seem to imply that later stage AI valuations have dramatically corrected, the underlying data actually shows that a small handful of companies are breaking out as winners in their respective categories. Further, capital and value are concentrating on them.

In fact, at least ten healthcare AI companies have achieved valuations greater than $1 billion over the last year, and at least five multi-billion-dollar healthcare AI exits have been announced so far this year, which is important, because it suggests that there are compelling liquidity opportunities emerging for this first wave of AI products. Firstly, this is a critical signal that AI solutions are creating durable value. Secondly, exits of this magnitude sustain larger capital inflows into the healthcare AI category. But thirdly, the magnitude of these outcomes alone are quite significant, given the average “successful” healthcare and digital health exit size has historically been $100 million or more. Interestingly, five of the at least seven total liquidity events that have been announced this year exceeding $1 billion in transaction value, have been for healthcare AI platforms.

As alluded to earlier, this value creation has been relatively concentrated. We refreshed the analysis that we created last year by incorporating 2024 venture capital funding data and what we continue to observe is that AI is creating outsized value in revenue cycle management and claims operations, patient engagement and contact center and clinical operations categories. Collectively, we refer to these categories as “administrative” in nature.

As highlighted in the chart below, AI companies targeting administrative functions have consistently delivered superior investor returns, commanding valuation premiums (a proxy for commercial revenue) around 30% higher than other categorical segments. But importantly, these categories are also sustaining the first multi-billion-dollar valuations (and liquidity outcomes) in healthcare AI. Six of the highest-profile healthcare AI unicorns minted so far this year, including our portfolio company, Smarter Technologies (fka SmarterDx), primarily address administrative inefficiencies across healthcare. Clearly, these categories also harbor significant appeal to buyers and upstream funding partners.

While administrative operations remain ripe for AI-enabled disruption, most venture capital funding continues to go towards clinical value propositions, a continuation of a 10-year phenomenon that we noted in our report last year. This reflects a shared belief that the largest future opportunity for AI-enabled value creation in healthcare will be in clinical care delivery and clinical decision support.

Parts of this future are steadily becoming a reality. Today, the average physician’s panel size is between 2,300 to 2,900 patients. Some hospital leaders that we have spoken to have developed internal mandates to increase panel sizes to 10,000 patients in the coming years, a feat that would require technology and AI, given existing labor constraints. By streamlining the diagnostic journey and reducing repeat testing, imaging AI platforms like Heartflow seem to have a promising role to play in this future. We are also seeing the emergence of AI-native care delivery models, like Jimini Health or Counsel Health, that promise to extend clinical capacity by sequencing AI and human clinician mediated care. Some of these care delivery models natively integrate AI to automate administrative tasks like billing, calls, and annotation, further boosting clinical productivity.

Despite the promise of clinical care delivery AI models, this sub-category of clinical solutions has a higher threshold of adoption and therefore more left to prove in order to achieve ubiquity. The simple fact that clinical AI products, broadly, continue to exhibit lower graduation rates from seed to later stage financing rounds (compared to administrative AI solutions), highlights this. In fact, clinical AI company valuations, on average, were about 25% lower than companies focused on administrative operations in 2024. An important unlock for many clinical care delivery AI companies is consistent demonstration of quality and safety, at a universally accepted standard. This will gate their ability to achieve payer coverage, at scale. Regulatory complexities, lengthy development timelines, reimbursement variability, and clinical integration complexity are other ongoing hurdles.

Some of the dichotomy between administrative AI uptake rates and clinical AI uptake rates can be explained by the activities that comprise each of these categories. Anthropic recently published an economic index, analyzing the occupations and skills that their Claude.ai model is being used for. In their analysis, below, two things jump out.

Firstly, industries requiring high levels of training, nuanced human tact and correlating to higher wages, like many parts of the healthcare industry, saw a much lower percentage of their jobs being impacted by AI. Secondly, today’s leading models excel at very specific activities like information summarization and reasoning (i.e., clinical chart review, medical necessity research), listening (i.e., scribing and automated speech recognition, call automation), writing and content creation (i.e., appeal and denial letter automation, systems biology modeling), and coding and programming (i.e., medical billing and coding, clinical documentation improvement). However, Claude.ai was more sparsely used for scientific research and technical analysis (i.e., clinical research and decision making), complex human interaction and people management (i.e., live patient care) or physical interventions and activities (i.e., surgical procedures).

This maps fairly consistently to the categories of healthcare where we have historically seen high versus low AI impact. Categories that consist of a higher volume of occupational skills that can easily be automated by AI are easier automation targets. Importantly, large language models (LLMs) are distinguished in their ability to structure previously unstructured data. Therefore, we are seeing the highest incremental gains in performance and automation within previously unstructured parts of the aforementioned workflows.

This skills distribution also suggests interesting potential for a few emerging categories of healthcare AI, most notably, data-enabled clinical decision support activities. This is one sub-category of clinical AI that is less constrained by some of the hurdles mentioned above and therefore has benefitted from higher uptake. Perhaps the best representation of companies harnessing this potential today are OpenEvidence or Layer Health (a Flare Capital portfolio company), which use LLM advancements to convert previously unstructured medical literature or chart data into queryable formats. These products can comprehend new patterns and derive novel clinical and financial inferences from the newly structured data, setting them up to guide fundamentally better clinical decision making. By both streamlining search and recall work at higher accuracy rates,and allowing clinicians to access insights at their disposal (vs. pushing them into live clinical workflows), clinical decision supports products like these are driving unprecedented adoption. There are new personal applications of this functionality beginning to emerge as well.

We believe these are just a few of many emerging opportunities as clinicians and patients develop more comfort with the latest AI tools.

The Winning Playbook: Key Attributes of Leading Healthcare AI Companies

All of these adoption and budgeting patterns are shaping a dynamic of capital concentration. Today, six categories of healthcare seem to be capturing an outsized proportion of current enterprise AI budgets — including, AI physician assistants, billing and coding, care management, member and patient services, network and clinical operations, as well as radiology and imaging solutions.

These categories share several unique market attributes that make them popular targets for AI-enabled transformation:

• First, each of these categories have high technical automation potential given they are comprised of a high volume of manual activities that require the occupational skills discussed above
• Second, because these activities are more manual, they result in higher amounts of data lossiness (i.e., information discussed but not captured on a phone call)
• Third, that lossiness creates friction or erodes value for a core healthcare stakeholder, such as a clinician or patient
• Lastly, these categories support broad product surface areas, meaning there are lots of adjacent inefficiencies that a product can naturally expand to beyond its entry wedge (i.e., physician assistant scribes expanding into billing and coding workflows, or call scheduling solutions expanding into patient throughput activities)

Emerging products in these categories are targeting these inefficiencies by:

1. Automating activities to drive productivity
2. Capturing and structuring lost data, unstructured data or data exhaust to improve the effectiveness of these activities and inform new activities
3. Creating a superior experience for a core healthcare user, usually by saving them time or helping them make faster decisions
4. Evaluating the derivative impact of these activities on adjacent activities, to launch new products that extend their ROI

Naturally, all of the companies in these categories have earned product-market fit by addressing the inefficiencies discussed above. However, this is table stakes. What has distinguished category leaders from their category peers are the technical product characteristics highlighted in the figure below.

Firstly, category leaders exhibit substantially higher workflow automation rates, 75% to 90% in some cases, than their peers. This has allowed category leaders to scale and show value faster with fewer resources and capital, meaning they are able to sustainably undercut pricing. That’s one reason why some category leaders are growing over 5x year-on-year, a remarkable rate considering that top quartile growth for healthcare SaaS companies over the last decade has averaged 100% — 150%. A handful of category leaders have actually crossed the legendary $100M ARR threshold in just 3 years, a notable feat for any technology company, let alone a healthcare technology company.

Growth rates of this magnitude are a major driver of valuations. And category leading companies in each of these categories command upwards of a 5x valuation premium relative to their category peers.

Secondly, each of these companies has invested in core product and business attributes that tightly match enterprise buyer preferences. In our enterprise buyer survey, ROI, integration timelines and operational costs were the most important factors considered by buyers evaluating new AI solutions. These preferences are highlighted in the chart below.

The best healthcare AI companies we are seeing today can both execute a sale and integrate their product into core systems of record within six months or less. We have observed that breakout growth is enabled as much by distribution and implementation innovation as it is by product innovation. Our portfolio company, SmarterDx, for example, chose not to integrate directly with medical record systems but instead have their product accessed via web browser, shaving months off implementation. Counter to its scaled incumbent competitors, it also deliberately relied on a contingency-based business model, ensuring the product is essentially “free” for its customers. These are key factors that have allowed the company to contract over $100M in ARR in 3 years. Similarly, Abridge Health announced a privileged co-development relationship with Epic to streamline both integration and go-to-market efforts in 2023. Growth promptly 10x’d the next year. OpenEvidence’s multi-year partnership with the Journal of the American Medical Association (JAMA), the pre-eminent peer-reviewed medical journal, has helped it attract over 50,000 U.S. clinicians a month to its platform, an unmatched pace of growth.

The best healthcare AI companies we have seen also tend to exhibit unimpeachably attributable ROI out of the gate. Category leading AI companies today are exhibiting 5x or greater ROI and some companies can point to this ROI in one to two months after deployment. This is crucial because it means that enterprise buyers can purchase, integrate and see a financial yield on a partnership in about half of a typical budgeting cycle. This affords them the opportunity to reinvest savings before the next cycle, catalyzing the growth of these relationships.

Importantly, leading AI companies do not wait for ROI to manifest. Instead, once the growth flywheel starts, they deliberately manufacture ROI by investing heavily in customer success, low friction user experiences and tight product audits / human-in-the-loop. This ensures consistent product performance and that value attribution can scale over time.

Another important characteristic to discuss is team. In a startup journey, chaos is a constant. Success is ultimately moored by leaders who rely on lived intuition to make millions of correct decisions along the journey. This has been true across technology cycles. And in our view, the best healthcare AI leadership teams possess technical AI depth that is grounded in clinical experience. Across the cohort of companies above, most are led by founders with lived healthcare experience who relate to their end-user and the problem in a deeply personal way. Many combine this experience with inherent technical knowledge or find a complement to it within the co-founding team, enabling deep technical and clinical fluency. But that doesn’t necessarily mean the best founders need to be repeat founders. In fact, amongst category leaders above, most founding CEOs happen to be first-time founders.

Choppy Seas Ahead: Threats and Opportunities for Leading Companies

As category leading healthcare AI companies command soaring valuations, the pressure to grow beyond their initial addressable market is increasing. Not all AI companies are able to outgrow this hype. The ubiquity and accessibility of AI mean that competition and price compression are persistent threats. But, what the category leaders in Figure 7 have in common is that they’ve deliberately positioned their products to take advantage of the target market’s broad surface area and expand ROI. This typically includes horizontal expansion into new categorical areas, a privilege unlocked by trust in the entry wedge and cemented in follow-on contracting structures. The race that all of these companies are now currently running is one to establish new product beachheads sooner than existing ones become commodified.

Perhaps the most frequently scrutinized race for horizontal expansion today is in practice management solutions, where multiple categories of products are vying to become the AI assistant for clinical teams and their administrative staff. AI physician assistant scribes (i.e., Abridge, Ambience, Suki) are actively adding billing, coding and care management features to point to a larger financial ROI story. Meanwhile, leading AI billing and coding companies (i.e., Smarter Technologies, Commure, Codametrix) have bought or are considering launching their own AI physician assistant scribing services to get closer to clinical workflows. There are a handful of other emerging startups also entering this race. For example, given physicians often refer to clinical guidelines during a clinical encounter, it isn’t surprising that OpenEvidence is extending its physician assistant services from clinical decision support to transcription via the expected launch of “Visits”. With so many of these companies vying to become a wholistic clinician AI assistant, we expect a landgrab to solve other major clinical workflow pain points. Some areas that we think will be added to roadmaps in the next year are medication + pharmacy management, patient engagement + portal activities, discharge + care planning and utilization + care management activities.

Incumbent threats also abound for emerging startup leaders. OpenEvidence competitor, Doximity, has already announced its own scribe. Meanwhile, Epic (in partnership with Microsoft) recently announced that it will launch its own AI physician assistant with a suite of scribing, care management, coding and patient engagement features. Epic also suggested that its new AI features would be priced distinctly and incrementally, suggesting a higher level of value and performance. Outsourced revenue cycle management providers like R1 RCM and Waystar (who recently acquired AI clinical documentation improvement company, Iodine, for $1.25 billion) are undoubtedly also evaluating their positioning in this market. These announcements are further accelerating the basis of competition away from any single feature and towards longitudinality.

This dynamic isn’t unique to the practice management market. Clinical imaging and decision support technology incumbents are, for example, fiercely competing with emerging AI startups. RadNet is a good example, having launched its AI-powered portfolio, DeepHealth, in 2023. It has followed that up with a number of imaging AI acquisitions, including iCad, and See-Mode earlier this year. Phillips, also a major player in the medical imaging space, announced plans to invest $150 million into a manufacturing and research and development facility for imaging AI products.

Meanwhile, Evolent, one of the most prominent publicly traded care management organizations, recently announced its intention to become an “AI-first” organization, targeting auto-approving 80% of authorization volume. This accompanies their 2024 acquisition of Machinify’s AI-powered utilization management product. Like many other incumbents, Evolent is signaling that it intends to evolve its technology stack as leading AI startups like Innovaccer and Cohere Health (a Flare Capital portfolio company) reinvent the care management market.

Flush with cash, technological advantages and brand recognition, many emerging startup category leaders are primed to continue scaling with new products. How quickly and effectively they scale clinician and patient stickiness while growing pricing power through extended ROI, will dictate the pace of scale.

Conclusion

AI in healthcare has moved from experimentation to execution. Adoption is broadening, budgets are expanding, and value is concentrating in companies that pair high automation with fast, verifiable ROI. The past year delivered three reinforcing signals:

1. Sustained enterprise demand — with many hospitals leading adoption
2. Breakaway company performance — some crossing $100M ARR within three years and posting 5x+ year-on-year growth
3. Credible liquidity — multiple multi-billion-dollar outcomes validating durable value creation.

At the same time, boundaries between categories are blurring as leading companies expand horizontally, especially across administrative operations where AI’s near-term impact remains most tangible.

Over the next 12–24 months, we expect continued budget expansion, accelerated consolidation, and a clearer separation of winners. Administrative workflows will keep compounding value as products capture and structure “lost” operational data, while the most promising clinical AI will center on decision support built on newly structured clinical and literature corpora — deployed adjacent to live workflows to minimize friction and maximize clinician trust. Distribution will be the decisive moat: privileged partnerships, implementation speed, and pricing models that de-risk adoption will determine who scales.

Strategic Imperatives for AI Buyers and Builders

For providers, payers, and life sciences buyers

• Buy for time-to-value. Invest in internal capabilities that can speed up partnership timelines and facilitate contracting-to-go-live cycles of ≤6 months.
• Establish measurable outcomes. Create infrastructure to enable transparent and traceable ROI attribution between internal efforts and partners.
• Build longitudinally reliable automations. Prioritize partnerships with auditable quality controls and human-in-the-loop guardrails.
• Invest in data exhaust. Favor products that capture, structure, and return operational and clinical exhaust to upstream systems, in order to compound value across adjacent workflows.
• Plan for expansion. Choose platforms with credible roadmaps beyond the entry wedge and align expansion incentives through innovative contracting.

For founders and operators

• Win on distribution and implementation. Treat integration time, change-management, and pricing model design (e.g., contingency-based but only where appropriate) as core product features.
• Instrument ROI from day one. Bake in telemetry, unit-level economics, customer success and clear before/after baselines to drive consistent product usage and expansion.
• Be model-agnostic, data-centric. Build pipelines that normalize heterogeneous data, support rapid model swaps, and enforce provenance, security, and auditability.
• Expand horizontally with intent. Sequence adjacencies that reuse the same data and decisions; resist one-off features that increase integration debt.
• Operationalize trust. Maintain clinical safety reviews, tight QA (including human-in-the-loop), and transparent error reporting.

About Flare Capital Partners

Flare Capital Partners is the leading healthcare technology venture capital firm advancing innovation-driven companies to improve positive health outcomes, broaden care access, and lower healthcare costs. We partner with exceptional founders solving healthcare’s hardest challenges, supporting each with our deep sector expertise, unparalleled industry resources, and proven access to commercial opportunities. We are investors in several leading healthcare AI companies, including SmarterDx, Cohere Health, Suki AI, Layer Health and Axuall Health among others. Our team of established investors and senior operating executives has invested in 70+ companies and has nearly $1 billion in assets under management. To learn more about Flare Capital Partners, please visit flarecapital.com or follow us on LinkedIn, YouTube, or Spotify.

We are thrilled to share that Flare Capital Partners has deepened our partnership with Cascala Health, an AI-enabled care transitions software platform transforming how patients move between care settings. Today, Cascala announced the close of its $8.6 million Seed financing, co-led by Flare Capital Partners and Eniac Ventures, and welcomed additional new investors Omega Healthcare Investors, Ziegler Link-Age Fund, Tau Ventures, and Digital Health Venture Partners.

This marks the third time we’ve partnered with CEO and Co-Founder, Matt Murphy. Matt previously served in key leadership roles at Circulation and Cohere Health (both Flare portfolio companies) where he proved himself as a thoughtful operator with a deep understanding of care delivery workflows. He is joined by CTO Himanshu Chaudhary (former Chief Architect at Wondr Health and CTO of eMindful) and Chief Medical Officer Dr. Joseph Jasser (healthcare veteran and executive from Cigna, Dignity Health, Humana and Signify Health), along with a talented team of advisors from across the healthcare continuum.

Why We Invested

Care transitions from hospital to home, between specialists, or across post-acute settings, remain one of the most fragmented and risk-prone moments in the patient journey. In the U.S., 30-day hospital readmissions cost the healthcare system billions annually, while incomplete handoffs and siloed clinical data place patients at unnecessary risk and create duplicative work for providers. This has been a persistent problem in healthcare for decades, and a clear use case for AI. Prior to joining Flare Capital, Vic led product strategy at Curaspan, a leading post-acute care transitions platform that was acquired by naviHealth in 2016, and later by Optum and Wellsky. At that time, companies like Curaspan digitized workflows that had previously been done via fax machines. The leap from fax to HL7/API platforms was vast. The leap from the prior generation of software platforms to modern LLM solutions, like Cascala Health, promises to be the next significant shift in streamlining the administrative burden of care transitions.

Cascala’s platform addresses these challenges with a clinically informed knowledge stack powering large language models (LLMs) to produce enhanced discharge summaries, dynamic risk scores, and condition-specific care plans. Its early product offerings include Clarity™ for AI-driven data curation, Continuity™ for longitudinal patient views, and Copilot for automated in-workflow activation.

By surfacing relevant information sooner and streamlining communication between care settings, Cascala enables providers to be better prepared for a new patient visit, minimize failures of care coordination, inform next best clinical actions, and strengthen value-based care performance.

Strategic Partnerships Powering Cascala’s Next Growth Chapter

Cascala’s initial focus is on risk-bearing providers and post-acute care organizations where improved care transitions directly translate into better quality scores, shared savings, and patient outcomes.

In early 2025, Cascala launched its first partnership with one of the largest nationally recognized, physician-owned ACOs. The partnership is demonstrating the platform’s ability to deliver high-quality, timely transition reports and integrate seamlessly into provider workflows. Physicians and care teams have already reported meaningful improvements in efficiency and care coordination, including a more than 50% reduction in manual effort.

The addition of Omega Healthcare Investors and Ziegler Link-Age Fund as strategic partners positions Cascala to accelerate its entry into post-acute care. The combined relationship represents nearly 1,000 skilled nursing and assisted living facilities and over 150 senior living operators, offering direct access to a broad range of post-acute providers.

Looking Ahead

Given the scale of the challenge, the strength of Cascala’s technology, and the experience of its leadership team, we believe the company is uniquely positioned to become the leader in AI-enabled care transitions, helping providers deliver better outcomes, more efficiently, at scale.

We are proud to partner with the Cascala team and Matt once again as they tackle one of healthcare’s most pressing and solvable challenges.

To learn more about Cascala Health: reach out to Vic Lanio (vic@flarecapital.com), Tara Sullivan (tara@flarecapital.com), or any member of the Flare Capital team, we would love to hear from you.

Careers: Cascala Health is hiring across engineering and operations roles. Explore their open positions here.

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Neurological disease remains a leading cause of illness and disability worldwide. In the United States alone, the annual economic burden of Alzheimer’s disease and related dementias is now estimated at $780 billion and rising as the baby‑boomer generation ages. Yet, more than nine in ten cases of mild cognitive impairment go undiagnosed and over 20 states have so-few dementia specialists that public health experts are warning of emerging “dementia neurology deserts.”

The consequences are stark: patients miss the window to benefit from disease‑modifying therapies and clinical‑trial enrollment, and caregivers in turn are shouldering an unsustainable load. As providers and payers seek to better manage these patients, powerful tailwinds are emerging to reduce this burden (GUIDE, HCC V28) — yet critical gaps persist. The combination of these clinical, financing, and pharmaceutical dynamics has created an opening for a neurologist‑led virtual neurology platform to expand access to timely diagnosis and evidence‑based, guideline‑level treatment that links patients to research.

Today, Flare Capital Partners is thrilled to lead Isaac Health’s $10.5 million Series A as the company develops a category‑leading, end‑to‑end virtual neurology platform. We are excited to be joined by investors Meridian Street Capital, B Capital, Industry Ventures, Primetime Partners, and Black Opal Ventures.

The team boasts robust scientific depth with clinical and operational expertise from institutions including the University of Cambridge, NYU Langone Health, McKinsey & Company, and Mass General Brigham. Co‑Founder and CEO Dr. Julius Bruch, MD, PhD, earned his doctorate in neurodegeneration and is a Cambridge‑trained physician. CMO Dr. Joel Salinas, MD, MBA, MSc, FAAN, is a behavioral neurologist at NYU Langone Health’s Memory Center, a renowned clinician‑scientist, former Clinical Director at Mass General’s McCance Center for Brain Health, and a revered author who has contributed to eight books on neurology.

Market Opportunity

Isaac Health is capitalizing on powerful tailwinds that make this the right moment to build a category‑defining virtual neurology platform. Policy momentum continues to push the industry toward cost‑efficient care: CMS has introduced the eight‑year GUIDE dementia model, and HCC V28 now rewards payers and risk‑bearing providers that proactively diagnose and manage cognitive impairment.

Therapeutic breakthroughs continue to advance. A new class of anti‑amyloid therapies (AATs) mark a scientific milestone, with technologies on the horizon (e.g., Brainshuttle by Roche) that show promise. This wave of therapeutic intervention coincides with a >$60 billion pipeline of global neurodegenerative drug candidates that depend on timely, accurate diagnosis.

Isaac Health’s Evidence‑Based Care Model

Isaac Health’s breakthrough lies in its cutting‑edge approach to care delivery which combines human expertise with AI‑powered diagnostics and workflows, pioneering an industry‑leading clinical model inspired by evidence-based models developed at institutions like UCLA and UCSF. Isaac Health tackles core gaps in traditional neurology care via a neurologist‑led model that spans the full continuum of care. The company starts with AI‑powered identification of at‑risk members, then delivers longitudinal disease management and caregiver support.

Proprietary screening algorithms surface high‑risk individuals across claims and EHR datasets and drive evidence‑based diagnostic pathways. Caregiver‑centric support is delivered virtually and, when needed, through in‑home partners. Isaac Health is now licensed in 50 states and covers 400,000+ Medicare lives and 20,000+ Medicaid lives, with over 5,000 members screened via their proprietary screening tool. Isaac Health’s flexible “plug‑and‑play” model for care lets health plans and risk‑bearing provider groups choose the mix of virtual and in‑person services that best fits their needs. Robust evaluations of Isaac Health’s enrolled patients confirm improvements in cognitive skills, higher engagement, and significant medical cost savings — helping health plans achieve a greater than 4:1 ROI.

Isaac Health demonstrated clinical efficacy and financial efficiency in a matched case‑control study independently validated by the Validation Institute. Nearly 73% of managed patients showed improved neurocognitive function after six months, and 92% met cognitive goals. The platform can also deliver meaningful cost reductions, fewer ED visits, and shorter hospital stays while boosting Medicare Advantage (MA) revenue by closing dementia‑related coding gaps. Commercial traction is strong: Isaac Health has partnered with national and regional health plans, risk‑bearing MSOs and medical groups, and multiple pharmacy home‑management vendors.

Further, Isaac Health is the first virtual provider delivering safe, active treatment of AAT, which serves as a differentiator for the platform that will service the life sciences sector long-term. A testament to the Isaac Health care team.

The Leadership Behind It All

Great companies are built by great people. Co‑founder & CEO Dr. Julius Bruch, MD, PhD, is a Cambridge‑trained clinician‑scientist with a doctorate in neurodegeneration and seven years at McKinsey advising global healthcare organizations. Co‑founder and CMO Dr. Joel Salinas, MD, MBA, MSc, FAAN, is a nationally recognized behavioral neurologist at NYU Langone Health’s Memory Center and the former clinical director at Mass General’s McCance Center for Brain Health. Julius and Joel have assembled a purpose‑built team of operators and engineers, whose experience spans Kyruus Health, Mass General Brigham, IBM, Harvard, and more — a rare blend of scientific depth and execution prowess.

Our team has known Julius and Joel since their earliest prototyping days and has watched them translate vision into a highly capital‑efficient business that aligns perfectly with our thesis of enabling value through next‑generation specialty care.

Looking Forward

As more partners and patients come onboard, we expect Isaac Health to continue driving strong ROI (>4:1) via disease‑modifying therapies and patient-centered care. Longer-term, Isaac Health’s future roadmap goes well beyond dementia. The same clinical engine can address adjacent neurological conditions, power real‑world‑evidence (RWE) collaborations with pharmaceutical companies and helping health plans and risk‑bearing providers to thrive in value‑based arrangements.

We’re excited to partner with Isaac Health’s leadership and support their growth as they build the trusted, category‑defining virtual neurology clinic.

To learn more about Isaac Health: reach out to Ian Chiang (ian@flarecapital.com), Jon George (jon@flarecapital.com), or any member of the Flare Capital team — we would love to hear from you.

Careers: Isaac Health is hiring across clinical, engineering, and operations roles. Explore open positions here.

Stay in touch with Flare Capital:

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• Follow us on LinkedIn, X (Twitter), and our website.

Axios coverage here: https://www.axios.com/pro/health-tech-deals/2025/08/14/isaac-health-dementia-care-series-a

Healthcare artificial intelligence sits at an inflection point. The pace of change and innovation for AI is moving at a rate we have never seen before, and as advanced decision‑making becomes more rapidly accessible, healthcare organizations are mobilizing to adopt the necessary infrastructure and cutting-edge tools to move their workforce towards a highly efficient, AI-powered workplace.

For many in the market, it is unclear how the best companies are building winning healthcare AI products. Large models adapt by the week, and AI labs like OpenAI, Anthropic, and Google seem to be displacing entire startup categories every month. We find ourselves in an ambiguous moment for product building, as no doubt AI in 2026 and 2027 will look starkly different from the applications that we’re seeing today. This transformation is creating a new guard for how companies build and deploy products, as well as how they fundamentally operate, think, and act when solving their most pressing problems.

With so many companies (both public and private) moving to adopt AI and build their own AI-native products, we’ve gathered experts leading the charge in this regard to learn from them and understand how they think about building winning AI products in healthcare.

A Recap…

Last year, we saw healthcare technology funding rise above the $10 billion mark with an estimated 42% of that capital going directly to AI investments. With an even higher estimated percentage of funding going towards AI through H1 than last year, enterprises and investors continue to show a renewed focus on administrative workflows, a trend that we anticipate will continuing throughout 2025.

As we have seen across several Flare Capital portfolio companies (highlighted in the chart above), the core business principles for AI-based administrative solutions have not fundamentally changed, even as the underlying AI technology evolves. Winning teams still lead with a clear ROI narrative, target <12-month payback periods, build durable product moats, and integrate tightly (and seamlessly) into existing clinical and back-office workflows.

Organizations are pursuing a healthcare AI profit pool that McKinsey pegs at roughly $360 billion in annual value, as deep-learning breakthroughs and transformer-based LLMs give way to autonomous reasoning engines and agent-based workflows. Early-stage VC dollars and enterprise pilots are already shifting toward these next-gen capabilities (with more detail forthcoming in Flare Capital’s future AI reports).

Yet many health systems, payers, and life sciences companies remain stuck in a maze of sub-scale pilots launched primarily to maintain a competitive edge. Interviews with our executives across the sector have revealed that AI adoption may actually be advancing more slowly than anticipated, with less enterprise-wide impact than initially promised. Reasons for this include large data silos, integration headaches, and a persistent trust gap that continue to prevent the jump from proof-of-concept to production.

Adding to these challenges is an increasingly uncertain regulatory climate. Looming federal action (i.e., the anticipated “Big Beautiful Bill”) plus state-level regulatory rules and medical boards have prompted organizations to adopt more of a “wait-and-see” stance. Even still, capital keeps flowing: private-market investors are rewarding startups that can pair differentiated technology with seasoned/tech-forward teams. The result: category leaders are beginning to surface in key niches within the AI market.

Amid these changes, one question was fundamental to our discussion: how should companies design and scale world-class healthcare AI products to thrive in this environment?

Navigating the Dynamic AI Landscape

This year we sat down with experts from across the healthcare AI industry as part of our Expert Roundtable series on Building Winning Healthcare AI Products. We heard directly from the experts who shared their thoughts and insights on what it takes to build winning AI products in the healthcare space, and where the industry might be headed.

The discussion saw an impressive panel of founders and operators debate live topics, such as technological moat, how to properly integrate into clinical workflows, and how best to navigate product builds for startups.

The first panel saw a group of three founding CEOs of early-stage healthcare startups. The second panel included two seasoned healthcare AI veterans, with direct oversight of AI implementation and evaluation within large organizations.

In our first panel, a few key paradigms emerged from the discussion:

When it comes to differentiation, our experts shared ways in which they see companies differentiating amidst the competitor-rich environment:

1. Integration of multimodal data: combining structured EHR fields with unstructured notes for robust reasoning
2. Constrain & optimize LLMs: for speed, cost, and precision
3. Embed deeply in workflow: driving high clinician adoption (90%+) through human‑AI teaming
4. Shortening path from prototype to production*: via Cloud‑native, HIPAA‑ready MLOps stacks
5. Establishing proven governance playbooks*: embed safety, risk controls, and ROI measurements at the executive level — available day one — into the product
6. Readiness to test (and adopt) new foundation models*: those pre-trained on multimodal clinical or claims data (e.g., Stanford’s CLMBR-T-Base) while benchmarking against commercially available baselines (which may outperform clinical models).

The Flare Capital team expanded on certain expert insights above*

“Many health data problems can be solved with today’s LLM models. Scaling across a health system, seamlessly integrating into workflows, and building long-term safe AI deployments — that’s where real defensibility lies.”
David Sontag, Co-Founder & CEO, Layer Health

As for how a product can differentiate, true defensibility in healthcare AI comes from mastering the “last-mile” problems that generic models leave unsolved. That means building pipelines that ingest messy clinical data (e.g., EHR, imaging, claims) and normalizing it to standards (i.e., FHIR). By layering rigorous validation into the process of product build, every prediction can then meet clinical safety and regulatory thresholds. Additionally, the latency, uptime, and privacy safeguards must be engineered into the tech stack such that care teams can rely on the system in “real” workflows. If an insight arrives too late, or can’t be audited by a healthcare professional, it’s useless.

At the same time, our panelists shared how platforms themselves must stay model-agnostic to some degree. Foundation models are advancing on a monthly and quarterly cadence, so the architecture should be able to support hot-swapping new checkpoints, A/B testing them against gold-standard datasets, and then rolling back if the metrics regress.

In practice, this calls for abstraction layers (e.g., model routers, feature stores, and evaluations) to decouple product logic from any single vendor. The winners here will be teams that treat data quality and clinical validation (along with continuous model governance) as core competencies rather than one-off tasks.

“With foundation models improving every few months, your platform must seamlessly switch, test, and validate new models, rather than cling to a single provider or version.”
Dr. Justin Norden, Co-Founder & CEO, Qualified Health

Generative AI has fundamentally shifted our day-to-day operations, pushing healthcare toward a new baseline in which clinicians and algorithms can collaborate/co-manage care. We already see ambient clinical-documentation copilots drafting visit notes in real-time, freeing physicians from keyboards; radiology suites where generative models can auto-compose preliminary imaging reads; and revenue-cycle models that can assemble prior-authorization packets and slash administrative lag.

Yet adoption still collides with the challenge of siloed data, regulations, legacy EHR transitions, and a lack of trust around “black box” outputs from different AI companies. Most decisively, the incentives in healthcare remain fractured between IT budgets, clinical needs, and payer contracts each pulling in different directions, which can cause pilots to stall when the value doesn’t accrue evenly. Breaking through this requires governance models that share upside across departments and technical architectures that make high-quality data, compliance, and explainability the “default” rather than the exception.

Translating Data into Insights, and Insights into Action

Leading startups can turn their relationships with stakeholders in the organizations from use case or SOW-driven into longer-term, actionable insights, which ultimately extend well beyond their initial scope of work. Startups must begin developing a trust-based relationships before parlaying the opportunity to solve the organization’s next, ideally larger, hair-on-fire problem.

When asked about tracking and measuring success, our experts shared some great insights below:

1. Success metrics should be simple, and then expand: Layer Health delivering 65% faster registry abstraction; Bayesian Health’s cut mortality by up to 30% through sepsis alerts
2. Granular attribution: Use counterfactual, patient‑level analytics rather than aggregate pre/post
3. Speedy A/B trials & phased pilots: Launch MVPs with clear hypotheses and measurable outcomes
4. Rapid iteration: Switch models, update prompts, refine workflows, then remeasure
5. Adoption monitoring: 0% usage = 0% ROI. Track every interaction

As we look outside of healthcare, product leaders across OpenAI and Anthropic for example are echoing similar guidance for startups building in this dynamic environment. Taking tangible steps — such as preparing teams to write increasingly more effective AI evaluation tests — will become the de-facto skills for product builders as teams build, improve, and trust AI systems not to make mistakes, especially with autonomous workflows.

Our roundtable experts noted a similar shift and described ways in which they’re developing smaller clusters of specialized models to work either in tandem or finetuning model to keep compute cost low without sacrificing quality output. An example of this in action includes Microsoft’s healthcare agent orchestrator, piloting with Stanford Health Care to enable data scientists and developers building AI agents for oncology tumor boards. Despite this vision, examples of this work are still early.

As we pan out, the fact is, today’s best AI model is the worst we’ll be using going forward.

“Skate to where the puck is going and anticipate next year’s challenges, not last year’s headlines.”
Suchi Saria, Founder & CEO, Bayesian Health

Product teams must continue to build products with this in mind. As we heard from one of our panelists, Suchi Saria, who draws on experience across Bayesian Health, Coalition for Healthcare AI (“CHAI”), and universities Johns Hopkins and Stanford; Suchi highlighted how solutions should emphasize product design such that the infrastructure is ahead of the model that exists today, without over‑promising features to the buyers, and expect that the pace of model innovation will continue to climb.

Core Capabilities for Safe, Scalable AI Products

In our second panel, we were joined by two senior executives from Flare Capital’s strategic Limited Partners, Lana Bender, VP AI and Behavioral Science at GuideWell and Dr. Barry Stein, VP and Chief Medical Informatics and Clinical Innovation Officer at Hartford HealthCare. Both drew on their experience deploying AI solutions across payer and provider organizations for 20+ years.

Barry and Lana shared how their organizations are thinking about building solutions, and the often-under looked aspects that startups miss (or must get right) when integrating across large enterprises.

Insights included:

1. Develop Strong Governance Frameworks — mirroring key FDA processes
2. Allow for On‑prem GPU Infrastructure — for cost‑effective, secure model customization
3. Establish Centers of Excellence — to pilot, refine, and scale solutions across the enterprise
4. Maintain Robust Education of Technology — from CIOs/CMOs to front‑line staff
5. Provider Meaningful Ecosystem Partnerships — with leading orgs (e.g., MIT, Google)

“Accelerating AI models from bench to bedside safely is our obligation. We approach AI deployments like we do new drugs and/or new medical devices: rigorous governance, real-world data, risk mitigation strategies, relevant clinical and operational training, and a commitment to ongoing monitoring.”
Dr. Barry Stein, VP, Chief Clinical Innovation Officer, and Leader of the “Center for AI Innovation in Healthcare”, Hartford HealthCare

Above all, perhaps the “ecosystem partnerships” piece is the more ambiguous task to achieve for early-stage startups, especially since these startups may be spinning out of great organizations or already have tech talent, making it seem like an unnecessary endeavor. Meanwhile, we’ve seen large organizations developing their own direct partnerships with larger enterprises (i.e., NVIDIA, OpenAI, Anthropic) and expanding on existing relationships (i.e., Microsoft, Salesforce). When startups fail to recognize the importance of scale through channel partnerships, it can cause that organization to get left behind.

To capitalize on these capabilities, startups must design engagement models that align incentives across payers, providers, and technology vendors from day one. That means being able to structure contracts that share upside on clearly defined clinical and financial outcomes — and embedding co-development milestones to give the enterprise partners real “skin in the game.” Furthermore, it’s important that startups select channel partners whose data assets and distribution help shorten time-to-impact. In practice, what this looks like could be coupling a rapid-cycle pilot (e.g., a 60-day deployment) with a pre-negotiated expansion path, contingent on hitting adoption and outcome thresholds.

By pairing an evidence-based commercial model with the pillars outlined above, startups can shift AI projects from isolated proofs-of-concept to more enterprise-wide programs that deliver compounding value.

Observations

If healthcare is going to keep pace with the broader AI ecosystem, the next eighteen months should be treated as an execution window for startups and organizations alike. We expect AI budgets will continue to rise, but capital alone will not be enough to bridge the gap between breakthrough model performance and organizational impact. Success is going to hinge on an organization’s ability to pair the larger spend commitments with disciplined governance, data interoperability, and incentive-aligned business models that translate AI-efficiency into measured outcomes — both clinically and financially.

Startups and organizations that can institutionalize these frameworks (rapid-cycle product evaluation, partnerships to extend reach, and hard-wired trust and transparency into workflows), as shared by our experts, are the ones that will successfully convert pilot bets into enterprise-wide value. Those that delay will face a widening performance divide as model capabilities and company expectations continue to accelerate.

Summary

• Defensibility lives in the “last-mile”: Competitive advantage comes from ingesting messy clinical data, validating performance, and guaranteeing low latency, audit ready outputs — not from the base model alone
• Governance = go live: Treat every deployment like it were an FDA-class device; build safety nets, audit trails, and monitoring before the first clinician or user touchpoint
• Incentives beat enthusiasm: Align upside across IT, clinical, and finance teams up front; mis alignment kills adoption
• Metrics start granular: Use patient level or claims-level counterfactuals (not aggregate pre/post) so wins are irrefutable and expansion becomes automatic
• ROI must fit on a slide: Enterprises buy 12 month paybacks and clear cost avoidance or revenue lift stories; anything longer invites death by pilot
• Control the data: Own end‑to‑end pipelines and normalization (e.g., FHIR) to ensure data quality, provenance, and faster iteration cycles
• Build for swap‑ability: Hot‑swap hooks and documented switching costs keep you vendor‑agnostic and future‑proof
• Pilot with speed and evidence: Best-in-class target 60‑day MVPs with A/B testing baked in to prove value quickly and decisively
• Make adoption undeniable: Target >70% clinician or end‑user utilization so impact scales beyond the pilot ward
• Align upside in contracts: Include outcome‑based expansion triggers that automatically convert wins into enterprise roll‑outs
• Accelerate through partnerships: Leverage strategic channel or ecosystem allies to shorten sales and integration cycles
• Stay regulation‑ready: Track emerging policies (e.g., the “Big Beautiful Bill”) and design documentation to breeze through future audits
• Today’s best model is tomorrow’s baseline: Build infrastructure that assumes relentless model improvement and lower inference costs

What did we miss? We’d love to hear from you, the teams and AI product builders, working on these solutions. Reach out to Jon George or any member of the Flare Capital team to continue the discussion.

Be sure to join us for our Flare Capital Expert Roundtable Series each quarter as we discuss the important topics impacting our healthcare system.

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