Zus Health raises $33 million to build the data layer for AI clinical workflows
Zus Health has secured $33 million in Series B financing to expand the infrastructure it says healthcare AI needs before copilots and agents can be trusted at the bedside. Drive Capital led the round, with participation from Sanofi Ventures, HealthX Ventures, Alumni Ventures, and existing backers Andreessen Horowitz, F-Prime, Maverick Ventures and JAZZ Venture Partners. The financing pushes total capital raised past $100 million and gives the company more room to extend its intelligent patient record platform, national healthcare data network and AI-powered clinical workflows.
The company’s thesis is straightforward: AI applications in healthcare are only as reliable as the patient data beneath them. In a sector still defined by fragmented records, incomplete histories and duplicate documentation, the stronger opportunity may not be the chatbot on top of the workflow, but the connective tissue underneath it. Zus is betting that if it can keep a longitudinal patient record continuously updated across providers, payers and care settings, it can become the operating layer for a growing class of clinical applications.
A national record built for interoperability, not isolated charting
Jonathan Bush, founder and chief executive officer of Zus Health, describes the platform as an “always-on, FHIR-native, API-first medical record.” The company launched in 2021 and built its platform in three layers: a nationwide patient record, a data network that feeds information into that record, and AI-enabled workflows that automate care tasks using a more complete view of the patient. In practical terms, the platform is designed to reduce the time clinicians and care teams spend hunting for records, reconciling conflicts and rebuilding a timeline that should already be available.
EHR systems digitized documentation, but they did not solve portability. A patient’s diagnosis, medications, labs and care transitions often remain trapped in separate systems, forcing each new encounter to start from partial information. At the point of care, that creates administrative drag and clinical risk: histories get repeated, medication changes are missed, and downstream decisions are made with an incomplete view of prior treatment. Zus is positioning its network as the answer to that problem, with a shared record that can be accessed through a standalone app, API, embedded components or direct EHR integration.
The company’s Zus Aggregated Profile, or ZAP, consolidates fragmented clinical information into a longitudinal patient record. It also layers AI-powered clinical intelligence on top of the record so the information can be used more quickly across applications and workflows. The architecture is meant to do more than store data. It is built to make the data usable.
One sentence captures the strategic shift. Zus is no longer selling connectivity alone.
AI workflows now depend on the reliability of the underlying record
Health systems and digital health companies are rapidly exploring AI copilots, documentation aids and agentic tools, but those products falter when the underlying record is incomplete or stale. Zus argues that the competitive moat will sit lower in the stack: at the level of connected patient information, evidence-aware workflows and networked access to records across organizations. That view is consistent with the company’s recent growth across value-based care organizations, digital health companies, provider groups, diagnostics and precision medicine firms.
The scale numbers give a sense of where the platform already sits. In August alone, Zus says its nationwide network supported more than 110 million networked transactions, while its AI-native clinical platform generated 39 million evidence citations year-to-date across its solutions. Those figures suggest a system designed not simply to retrieve information, but to operationalize it at volume. In a care environment where every prior authorization, risk adjustment review or care gap closure can hinge on a single missing lab or medication fill, transaction scale is not a technical footnote. It is the foundation for whether AI can be deployed safely and consistently.
Bush said the company is now focused on the “last and critical piece” of the future medical record: workflows that automate tasks of patient care in a highly tailored way. His argument is that the future of medical records will be national, not local, because care itself has become distributed across multiple settings and specialties. AI changes the calculus only if the information feeding those workflows is trustworthy, current and easy to act on.
Specialty care, especially oncology, is driving demand for connected records
Zus is seeing rapid growth in specialty care, particularly oncology, where patient records are often spread across several providers and institutions. Oncology care depends on assembling a complete picture quickly: prior therapies, pathology reports, molecular testing, referrals, imaging, trial eligibility and evolving treatment plans. In a fragmented EHR environment, those pieces are often scattered, and the administrative burden falls on nurses, analysts and care coordinators who must reconstruct the timeline by hand.
The company says its continuously updated, aggregated data helps with treatment eligibility review, clinical trial screening and genetic testing workflows. Those are not minor conveniences. In oncology, missing one prior therapy or one biomarker result can delay treatment selection or distort trial matching. A system that can assemble the record more efficiently may reduce manual review time and improve the consistency of those decisions, even before any predictive model enters the workflow.
The same logic extends beyond cancer. Population health management, risk adjustment, transitions of care and pre-visit preparation all depend on knowing what happened before the current encounter. AI can accelerate those processes only if the underlying data layer already solves for interoperability. Without that, automation becomes a faster way to reproduce incomplete information.
New capital will fund connectivity, intelligence and workflow automation
Zus says the Series B proceeds will be used to expand its AI-powered clinical intelligence, broaden healthcare data network connectivity and build more automation for population health and care management workflows. The company also plans to enable customers and developers to create AI-powered workflows across value-based care, specialty care and other high-value use cases.
Molly Bonakdarpour, general partner at Drive Capital, said the company has built the foundation needed to address the challenge of reliable connected patient information at scale. Her comments reflect the central bet behind the financing: that healthcare AI will not be won by the most visible application, but by the infrastructure that can make those applications dependable in real-world clinical environments.
The next stage for Zus will be execution. The company has already shown that it can move large volumes of data across a national network and transform that information into usable clinical summaries. The harder test is whether that same infrastructure can support durable workflow automation across more settings, more specialties and more operational use cases without losing fidelity. The funding gives Zus a larger runway to answer that question, while the rest of the market keeps building AI tools that still need a stronger record underneath them.