Kythera Labs is building an AI-native healthcare strategy platform on Databricks that gives any health system access to the expert intelligence they need and can trust.
The meeting ends the way these meetings always end: with a question no one can answer quickly enough. A CEO, CIO, and CFO walk out of a planning session with a mandate: identify how much oncology revenue is leaving the system and where it's going. In a well-resourced health system, that question goes to a consulting firm and comes back six weeks and several hundred thousand dollars later. In most health systems, it goes to an analyst with a BI tool and comes back whenever it does, with whatever confidence the data allows.
The gap between those two experiences is the problem Kythera Labs was founded to close.
Health system executives face a complex set of strategic decisions simultaneously: growing patient volume, optimizing payer contracts, evaluating M&A targets, identifying underserved markets for expansion, and reducing administrative overhead, all with incomplete data. These decisions align with value creation levers, which historically have required expert analytical capacity that correlates almost entirely with institutional budget.
"The market has been served by BI tools sitting on top of claims data," says Jeff McDonald, CEO of Kythera Labs. "BI tools can do a good job of representing what's in the data. They don't do a good job of telling you what's not in the data. That's the antithesis of what the tool is designed to do."
The analysts who can bridge that gap (who understand the missingness and bias in claims data, who can reconstruct a patient journey from fragmented billing records, who know the difference between what a claim says and what actually happened clinically) take years to develop. Large health systems hire them. Smaller organizations do without or spend millions on consulting firms to rent that expertise by the engagement. The strategic intelligence gap in American healthcare is not primarily a data problem. It's an expertise distribution problem.
Kythera Labs is solving it with AI.
Before any agent can answer a strategic question reliably, the data it reasons over has to be reliable. That's a harder problem than it sounds.
Claims data is billing exhaust, generated so providers can be reimbursed, not so executives can make market strategy decisions. Repurposing it requires resolving provider identities across dozens of competing sources, harmonizing procedure codes across 130 standardized medical vocabularies, correcting for systematic missingness, and reconstructing patient journeys as temporal sequences rather than collections of disconnected billing events.
The answer is Kythera’s healthcare data tech, which takes 339 billion medical and prescription drug claims representing over 300 million patients, eight years of history, more than three petabytes of storage and creates something an agent can actually reason over — an event-based structure where a knee replacement isn't a billing code but a surgical event with a pre-operative history, a discharge, and a post-operative care trajectory. All built on Databricks. That translation is the work.
It is also what makes the agent's answers trustworthy. Kythera's operational layer runs on Lakebase, Delta Lake, Delta Sharing, Unity Catalog, and serverless infrastructure — so the transactional data powering real-time workflows shares a single governed foundation with the analytical data the agents reason over. No ETL. No data movement. No seams between the question and the answer.
The proof is in production. A health system in Louisiana signed a contract with Kythera in December 2024 and went live before Christmas. Ten days from contract to first insight with visibility into their patient population that they had never had before:
150% increased visibility into patient encounters,
12% more keepage,
22% less leakage, and
$3.8M in estimated annualized value from retained encounters.
That kind of time to value is only possible because the data foundation was already built.
With Kythera’s Healthcare Strategy Agent built on the Agent Bricks framework, deployed into the health system’s Databricks workspace, the Chief Strategy Officer opens a conversation and asks: “How many cancer patients are being referred to non-affiliated providers for services we offer?”
What follows is not a dashboard refresh. The agent works through the question the way a seasoned analyst would, surfacing 6,800 referred oncology patients, naming the competing providers capturing their volume, identifying the highest-leakage referring physicians by name, and putting $23.1 million in reimbursement opportunity on the table. Specific retention strategies follow. The whole session takes minutes.
Every result in that response represents a query that a human analyst would have had to write, test, validate, and consolidate. The agent runs them in minutes.
"A lot of what we're doing is packaging the expertise around how to work with these datasets into intelligent agents, so that capability isn't limited to a small group of specialists," says Ryan Leurck, Co-founder and Chief Analytics Officer. "Even when you have the right people, answering complex questions can take days or weeks. The idea is to make that expertise more accessible and help people get to answers much faster."
The oncology scenario is one demonstration of a platform built to address more than a dozen strategic question types, from payer mix optimization and demand forecasting to greenfield market sizing and M&A target evaluation. The same platform meets the needs of a CFO asking about payer contract performance, the service line director asking about competitive positioning, and the business development team asking about acquisition targets, each in their own language, each getting answers calibrated to their context. For health system teams that want to explore data outside a structured agent workflow, Databricks Genie can extend this same accessibility — letting any user ask questions of enterprise data in plain language, without requiring a BI team or SQL expertise.
None of this matters if health system executives can't trust what the agent tells them, or if the CIO can't defend the deployment to a compliance team.
Trust in a healthcare AI platform has a specific meaning. It means a financial analyst asking about payer contract performance can't accidentally surface data from a different service line. It means an agent query leaves an auditable trail that satisfies HIPAA requirements. It means the data the agent reasons over has been validated, quality-checked, and governance-stamped before any model ever touches it. In a regulated industry where a single data access violation can trigger significant legal and reputational consequences, these aren't nice-to-haves — they are the condition under which deployment is possible at all.
Kythera's answer is Databricks Unity Catalog, implemented as the governing layer across every customer deployment. Unity Catalog provides a single control plane for data access, security, and lineage across the entire platform, meaning the same policies that govern a SQL query govern an agent query, and the same access controls that apply to a BI report apply to an AI-generated recommendation. A service line director sees her market. A CFO sees his financial data. A strategy executive sees the full picture she's authorized to see. No one sees more than they should, and every access event is logged.
Jeff McDonald, who has spent 32 years in healthcare, is direct about what this represents in practice: “It's a complex problem to solve, and Databricks brings the pieces together in one place. Unity Catalog allows us to deploy across all three clouds in one day, and Delta Sharing enables near-instant access to petabyte-scale data. That removes friction and helps us execute faster.”
In a traditional enterprise architecture, governance requires layers of custom tooling, separate access management systems, and significant ongoing engineering. Unity Catalog collapses that into configuration: policies set once, enforced everywhere. For a company serving 55-plus health systems in a heavily regulated industry, that is the difference between a platform that can scale and one that can't.
The Chief Strategy Officer now has her oncology analysis. She knows which physicians to call, which competitors to respond to, and what the revenue opportunity looks like. But the question every insight eventually surfaces is the same: now what?
That question marks the beginning of Kythera's longer-term roadmap and where the difference between better analytics and genuine decisioning comes into focus. The arc of AI in healthcare data has followed a familiar progression: from static dashboards that told you what happened, to predictive tools that told you what might happen next, to today's guided analytics that let any leader in the organization ask complex questions in plain language and get answers calibrated to their role and context. Kythera has made that third stage real, at production scale, on governed infrastructure.
But the company's ambition doesn't stop at answering questions faster. In Kythera's own published thinking, the future of healthcare analytics is not just descriptive, it is predictive, prescriptive, and increasingly autonomous. The next stage of Wayfinder is a platform where an insight doesn't just inform a decision; it initiates a workflow. Where the referral leakage analysis triggers an outreach sequence to the highest-impact referring physicians. Where the market gap analysis routes directly to the real estate team. Where the full arc from strategic question to operational action runs on a single governed platform, without the data ever leaving the lakehouse.
"No one wants data," McDonald says. "People want answers. And why do they want answers? So they can go do something for someone. That's the workflow adjacent to the answer."
This is the transition from guided BI to compound decisioning, systems that don't just surface intelligence, but act on it, within the guardrails of a governed architecture that healthcare's regulatory environment demands. Achieving that at scale requires the same ingredient that made the analytics layer possible in the first place: data that is patient-mastered, harmonized, and governed before any agent ever touches it.
As Kythera's engineering team has observed, an agent that is properly trained and validated can explore data more deeply and thoroughly than a human, remain constantly aware of interpretive context, and do so without fatigue, qualities that become more valuable, not less, as the system moves from answering questions to taking action.
The implications reach beyond any single health system. A 200-bed regional system asking the same strategic questions as a major integrated delivery network and eventually acting on the answers with the same speed with the same quality of expert intelligence, without a consulting retainer. That is a different kind of healthcare market. It is the market Kythera is building toward, one health system at a time, on Databricks.
And if enough health systems can make better decisions about where to deploy capital, where to expand access, and where to reduce administrative waste, and then act on those decisions faster, the founding ambition starts to feel within reach: that the cost of healthcare might actually come down.
To explore Kythera's healthcare strategy platform and request a demo of the Healthcare Strategy Agent, visit kytheralabs.com. To learn more about the Databricks technologies that make it governed, scalable, and ready for the next stage of autonomous decisioning — Genie, Agent Bricks, Unity Catalog, and Lakebase — or to explore the Built On partner program, start here.
Kythera Labs is a Databricks Built On partner. Learn more at kytheralabs.com.
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