The Three Stages of AI Adoption in Healthcare 1
From our experience, there are three categories of AI adoption in healthcare:
Pilot-Ready: AI tools that are technically viable but haven’t been battle-tested in real-world settings. Think of them as early-stage vaccines—promising, but not yet ready for widespread distribution.
Outcome-Ready: These tools perform specific tasks well—summarizing patient visits, flagging missing risk codes—but they are still waiting for a measurable, hard-dollar ROI to justify their place
P&L-Ready: This is the gold standard: AI tools that don’t just work but pay for themselves. This is where AI stops being a science experiment and becomes an essential part of business strategy.
Where does healthcare stand. Let’s break it down.
AI Perception across the Rubicon Portfolio 2
The Healthcare Services P&L
Imagine a doctor’s office. Not the bustling waiting room or the buzzing nurses station, but the back office—the financial nerve center of the operation. Here, the real challenge of modern healthcare plays out. It’s not just about medicine; it’s about math.
At its core, healthcare services is a business of reliable but modest margins, where every dollar spent on human capital—nurses, technicians, administrative staff—directly impacts the bottom line. A CFO looking at the books sees not just patient care, but cost structures.
Now, introduce AI into this equation. A modest 10% reduction in clinical/service costs could drive a 41% jump in EBITDA—not because AI is taking over medicine, but because automation makes the existing system work better 3. AI isn’t replacing doctors. It’s helping them work faster, smarter, and more efficiently.
An Example Healthcare Services P&L 4
So where is AI’s potential financial impact?
The Clinical/Service Line: Small improvements in productivity have the possibility to snowball into major financial gains. Yet, despite clear economic benefits, many healthcare providers hesitate. Why? Because AI in medicine isn’t just a financial decision—it’s a cultural one. Doctors are trained to trust their instincts, not algorithms. The challenge isn’t proving AI works; it’s proving AI can be trusted.
Beyond Healthcare Costs: AI’s biggest financial win might not be in healthcare at all. Legal. Real estate. Operations. These cost centers are potentially ripe for automation, and yet, most healthcare businesses don’t think of AI in these areas. But maybe they should.
The big question isn’t whether AI can improve healthcare margins. It’s who will be savvy enough to achieve the highest ROI projects first.
Revenue Growth and Clinical Service Management are key AI use cases in the Rubicon Portfolio
Revenue: New Product Offerings
AI offers the ability to launch new fully automated or partially automated service offerings from healthcare incumbents. We’ll explore 3 themes we’ve seen across our portfolio including: (1) Long Tail Services, (2) Volume Unlockers, and (3) Algorithmic Identifiers.
Volume Unlockers
AI can help healthcare incumbents identify and service a larger range of customers. Indigo, an AI-enabled professional liability underwriter launched by Rubicon in 2022, uses a proprietary AI-enabled risk score that uses billions of data points to assess physician malpractice risk. This allows the team to avoid the worst risks and then offer a more accurate price to the target physicians in line with their actual risk profile. For lower risk doctors, this means Indigo can offer a more competitive price and win more deals. Using this proprietary model developed in-house over 2+ years, they prescored all the doctors in the U.S., which allows them to directly target the most attractive practices.
“On the back of the risk scoring model, the Indigo team will also be launching real-time quoting this year, unheard of in this space, by leveraging AI throughout the tech stack,” says Jason Foucher, the Chief Product Officer at Indigo.
The Indigo Value Prop: Using AI to better price a traditional health care service offering
Algorithmic Identifiers
Clinical service organizations are increasingly powered by unique algorithms, often focused on patient identification enabling a B2B service model by screening a third party’s patient list and matching them with services.
Cadre Hospice, a value-oriented hospice provider launched by Rubicon in 2024, is an example of this approach. Cadre applies their algorithm to a partner patient list to identify candidates for intervention, ensuring those in need of care are not overlooked and adding speed and fidelity to the process.
“In an era where payors struggle with member engagement, ensuring appropriate end-of-life care remains a critical challenge. The Cadre algorithm serves as a vital safety net, using sophisticated analytics to identify patients at the optimal time for palliative or hospice services - before they fall through the cracks in our healthcare system,” says Sonnie Linebarger, CEO of Cadre Hospice.
Imagine Pediatrics, launched by Rubicon in 2023, also uses an algorithmic model to identify target patients for their fully integrated, multidisciplinary, value-based pediatric care offering for children with special health care needs. In these businesses, the algorithm is woven into the care delivery model.
Like many early adopters, Imagine noted that building trust with users can be a challenge, especially if the algorithm in question is a black-box machine learning model. In those cases, similarly situated clients should consider porting the algorithm into their environment to validate it with their own data and share transparent proof points with clinical leaders.
“Value-based care arrangements require a population selection algorithm that resonates and provides your partners with ease of implementation, shows durability of clinical acuity and patient spend patterns, and avoids regression to the mean dynamics,” says Meghan Haycraft, Co-Founder and Chief Strategy Officer at Imagine Pediatrics.
Imagine leaders also observed the importance of aligning AI tools with commercial opportunity, company values like transparency, and sales cycles:
Algorithmically-Driven Patient ID Tools require some foresight on key questions
Releasing a new AI/ML model
The real opportunity here isn’t just about adopting AI—it’s about shaping it. We believe the next frontier for revenue and volume growth in healthcare services depends on developing smarter, more adaptive AI and machine learning models. And that’s where things get interesting.
Within the development community, the question of how to build these algorithms is an ongoing a source of debate, innovation, and, increasingly, competition. As the AI economy scales, healthcare services ventures find themselves at a crossroads: Do they build a model from scratch? Do they adapt an existing one? Do they leverage the power of open-source tools or align themselves with the big players defining the AI landscape?
For large healthcare organizations, the answer seems obvious—they have in-house IT teams experimenting with custom wrappers around foundational models, fine-tuning algorithms to fit their needs. We have run across all of the major foundational models wrapped in healthcare with Grok being the exception (and more of an emerging player). For smaller ventures, the challenge isn’t just about developing AI; it’s about finding a way to do so efficiently, affordably, and at scale.
Many Options: Companies can adapt their AI/ML solution direction to match their internal level of expertise
Penguin Ai offers a set of pre-built small language models that can be white labeled for a variety of use cases. The Company’s leadership has a background in the healthcare data space across payors, providers and revenue cycle management.
Penguin has emerged as a key member of several workgroups in the Coalition for Health AI (CHAI), which is a collaborative initiative aimed at promoting the responsible development and deployment of artificial intelligence in healthcare. It brings together leaders from academia, industry, government, and healthcare organizations to establish best practices, standards, and guidelines that ensure AI in health is safe, effective, equitable, and transparent.
A speedy option when AI is a key capability, Penguin now offers the CHAI “nutrition facts” labels for each of their models to help drive transparency in their model development methodology.
“As a former buyer, I spent hundreds of millions customizing generic platforms for healthcare. With Gen AI, we can achieve previously impossible outcomes, leveraging our deep understanding of the healthcare ecosystem to solve its most cumbersome problems. A lot of healthcare companies need AI models but don’t have the internal expertise to wrap or fine tune models. Penguin offers those players a rapid option to deploy and rebrand a portfolio of small models made for specific healthcare use cases.”, says Fawad Butt, Founder &CEO, Penguin Ai and former Chief Data Officer, Kaiser Permanente, United Healthcare & Optum.
The Penguin Offering: Off the shelf small-language models for deployment
In our next issue (to be released next week) we’ll cover the Rubicon portfolio experience in Ambient Scribing!
Individuals Interviewed for this Article
Rubicon Portfolio Companies
Jason Foucher, Chief Product Officer, Indigo
Eden Klein, Chief Technology Officer, Imagine Pediatrics
Meghan Haycraft, Co-Founder & Chief Strategy Officer, Imagine Pediatrics
Sonnie Linebarger, CEO, Cadre Hospice
Ron Margalit, Chief Information Officer, Evergreen Nephrology
Tim Pflederer, Chief Medical Officer, Evergreen Nephrology
Joe Lucero, Vice President of Risk Adjustment, Honest Health
Jeffrey Stevens, Chief Medical Information Officer, HarmonyCares
Bartley Bryt, Chief Medical Officer, Privia Health
Irfan Ali, Chief Information Officer, US Heart and Vascular
AI Technology Vendors
Fawad Butt, CEO, Penguin Ai
Michael Ng, Co-Founder and CEO, Ambience Healthcare
Sandeep Gupta, Co-Founder and COO, Innovaccer
Severence MacLaughlin, CEO and Co-Founder, Delorean AI
Punit Singh Soni, Founder and CEO, Suki
Footnotes
The use of AI in healthcare is nascent and evolving rapidly. Views expressed in this paper are based on preliminary findings and analysis. The long-term benefit of the impact of AI in both the healthcare industry and any investments made by Rubicon Founders will be determined over many years or decades. Additionally, any real impacts will likely occur after much research and development time and cost, and not all projects will achieve positive return on investment. ↩︎
Summary based on discussions with key technology players at Rubicon Founders’ portfolio companies. For a list of individuals surveyed, please see the section entitled “List of Individuals Interviewed for this Article”. ↩︎
Projected EBITDA efficiencies are included for illustrative purposes only and constitute forward‐looking statements that reflect the estimates and assumptions of Rubicon concerning the future performance of AI tools, and are subject to significant business, financial, economic, operating, competitive and other risks and uncertainties and contingencies, many of which are difficult to predict and beyond the control of the company. Actual performance can be materially different and there is no guarantee that any investment in AI will be profitable. ↩︎
The P&L summary presented is for representative purposes only and does not represent specific P&L metrics for any company, including any company invested in by Rubicon Founders. ↩︎
