
Qualified Health
Healthcare · AI
Qualified Health Secures $125M Series B Led by NEA
Raised
$125M
Qualified Health raised $125 million in a Series B round led by NEA to expand its enterprise AI platform for health systems. The funding supports scaling AI deployments amid rising demand for clinical and operational transformation.
Qualified Health, a public benefit corporation based in Palo Alto, completed a $125 million Series B financing round on March 25, 2026, led by New Enterprise Associates (NEA). New investors including Transformation Capital, GreatPoint Ventures, Cathay Innovation, and Menlo Ventures' Anthology Fund participated, alongside existing backers such as SignalFire, Frist Cressey Ventures, Flare Capital Partners, Healthier Capital, Town Hall Ventures, and Intermountain Ventures. The capital will accelerate development of its secure AI platform, which integrates workflow automation, agent development, clinical safeguards, real-time monitoring, and governance for health systems. This brings total funding to $155 million.
The platform addresses key challenges in healthcare, including fragmented data integration, regulatory compliance, and scaling AI beyond pilots. Major partners like Mercy, Emory Healthcare, University of Rochester Medicine, Jefferson Health, and the University of Texas System's eight institutions have deployed the technology, achieving results such as over $15 million in run-rate impact at University of Texas Medical Branch within six months through EHR data unification and automated workflows. These implementations demonstrate revenue gains and cost reductions by redesigning administrative and clinical processes.
Founded by former health system executives, physicians, and engineers, Qualified Health serves over 500,000 users across systems representing 7% of U.S. hospital revenue. The funding underscores investor confidence in its ability to enable enterprise-wide AI adoption, positioning it as a foundational tool akin to enterprise software leaders in other sectors. This move aligns with intensifying pressures on health systems from labor costs, reimbursements, and care complexity, where AI promises operational efficiency and improved outcomes.