Qualified Health and Anthropic Launch Landmark AI Deployment at University of Texas Institutions to Expand Access to Life-Saving, Evidence-Based Care for Millions of Texans

Population-scale AI system uses Claude to analyze millions of medical records to identify patients who qualify for guideline-recommended care and support timely clinical review

PALO ALTO, Calif., Jan. 21, 2026 /PRNewswire/ -- Qualified Health and Anthropic have launched a landmark AI deployment across the University of Texas System (UT System) to tackle one of healthcare's most persistent challenges: ensuring that patients who meet evidence-based criteria are consistently identified, appropriately evaluated, and considered for high-quality care across large and diverse populations.

Under this initiative, Qualified Health's AI system, powered by Anthropic's Claude AI models, is being deployed across UT System's vast health network in Texas to continuously analyze vast clinical datasets and surface gaps in guideline-recommended care. The system applies validated clinical guidelines and appropriateness criteria to enable care teams across the UT System to consistently identify patients who may benefit from further clinical review and coordinated care planning.

Despite decades of clinical research and well-established guidelines, tens of millions of Americans who meet criteria for evidence-based care are never identified and evaluated in time. In Texas alone, an estimated 4-6 million patients fall through the cracks each year, leading to preventable complications and mortality, inequities in access, and growing strain on clinicians and the healthcare system.

The problem is not a lack of medical knowledge, but the operational reality of modern healthcare. Determining whether patients meet guideline-based and appropriateness criteria often requires intentional and labor-intensive chart review across fragmented systems, unstructured notes, labs, imaging, and historical records. At population scale, across millions of patients and petabytes of complex clinical data, systematic identification of gaps in evidence-based patient care has historically been infeasible, until now.

"Healthcare is one of the most demanding environments for AI because it requires parsing vast amounts of complex, unstructured clinical data while operating safely within strict governance frameworks," said Eric Kauderer-Abrams, Head of Life Sciences at Anthropic. "Claude can do that reliably, and when paired with Qualified Health's governance platform and a visionary health system like the University of Texas System, it creates the conditions to deploy advanced AI safely at scale and ultimately help close care gaps for millions of patients."

Qualified Health's AI system integrates data across sources, parsing complex clinical data, and applying validated clinical guidelines and appropriateness criteria to maintain a continuously updated, population-level view of care gaps. Patients who warrant further consideration are surfaced directly into care team workflows for review, with supporting clinical context automatically assembled to support efficient, high-quality decision-making.

"The challenge isn't that we don't know what works. It's translating decades of evidence and appropriateness guidance into consistent clinical practice at scale," said Justin Norden, MD, MBA, MPhil, CEO of Qualified Health. "The system is designed to augment, not replace, clinical judgment and enable clinicians to apply their expertise at a scale that was previously not possible. Together, the partners are establishing a replicable framework for operationalizing evidence-based medicine at scale. What once required extensive manual chart abstraction and cross-system coordination can now happen continuously, across entire populations."

Following extensive evaluation and testing, the system is now live at the University of Texas Medical Branch (UTMB), the first deployment site within the UT System. Initial deployment focuses on identifying gaps in guideline-recommended cardiology care, ranging from guideline directed medical therapy and appropriate medication dosage to necessary interventional treatments for heart failure and valvular disease. Importantly, appropriateness criteria are also surfaced to ensure high-quality assessment.

Early results from the initial deployment demonstrate population-scale impact:

• Complex clinical data were parsed and assembled into unified patient profiles, integrating notes, laboratory results, imaging, and procedural information
• Unified patient profiles were evaluated against precise, guideline-based criteria across a broad set of cardiology procedures
• Large cohorts of previously unrecognized, high-likelihood candidates were surfaced for clinician review
• Clinician review demonstrated a high level of agreement with system outputs, reinforcing clinical trust
• Care pathways for appropriately eligible patients were accelerated

By ensuring that clinical consideration is applied more uniformly across patient populations and care settings, the approach supports more timely, appropriate care delivery and reduces the likelihood that patients across Texas are overlooked due to operational or access constraints.

Building on UTMB's success, the platform is expanding systemwide and evaluation tools are extending beyond cardiology. By the end of 2026, additional deployments will support identification of eligible patients across primary care, vascular, gastrointestinal, rheumatology, and neurology specialties, helping additional health systems extend access to proven care all across Texas.

"UT health institutions serve patients in every corner of Texas. Rather than laying solutions on top of existing systems, we are building a new shared foundation across the UT System's health enterprise that allows new AI deployments to be introduced with consistency, accountability, and long-term impact," said Zain Kazmi, chief digital & analytics officer and associate vice chancellor of health affairs at the UT System. "This new partnership contributes to our ability to move quickly, learn collectively, and apply intelligence effectively within patient care environments."

As part of the University of Texas Research, Engineering, and Application Laboratory for Healthcare Artificial Intelligence (UT REAL Health AI) initiative, the deployment is anchored around two shared goals: expanding access to evidence-based treatment, particularly for underserved populations, and establishing a new standard for the safe, responsible deployment of AI in clinical environments.

"The UT REAL Health AI Initiative is about improving the lives of Texans," said Peter McCaffrey, chief AI & digital officer at the University of Texas Medical Branch and chair of the UT REAL Health AI initiative. "Through new AI deployments across our health institutions, we can enhance patients' experience of care, advance population health, and reduce the overall cost of care."

The deployment was featured in Anthropic's communications and public forums, including at the J.P. Morgan Healthcare Conference, as health systems nationwide evaluate population-scale AI to systematically extend access to evidence-based care.

About Qualified Health

Qualified Health is the enterprise AI platform and strategic AI partner helping health systems deploy safe and scalable AI to drive measurable clinical and financial outcomes. Reaching over 400,000 users across top health systems nationwide, our platform combines workflow automation, agent development, clinical safeguards, real-time monitoring, and end-to-end governance with deep healthcare and AI expertise, helping healthcare leaders realize value at scale.

Qualified Health is built and led by former health system executives, frontline physicians from leading institutions, clinical transformation experts, and Silicon Valley engineers, bringing the domain depth and operational rigor required to deploy, scale, and govern AI applications safely and responsibly at enterprise scale.

For more information, visit www.qualifiedhealthai.com. 

SOURCE Qualified Health PBC