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
Qualified Health and Anthropic have partnered to deploy a groundbreaking AI system across the University of Texas health network to address one of the most persistent challenges in healthcare: ensuring that patients who meet evidence-based criteria are consistently identified and provided with appropriate care. The initiative aims to improve patient outcomes for millions of Texans by using advanced artificial intelligence to analyze complex clinical data and highlight gaps in guideline-driven care.
The AI system, powered by Anthropic’s Claude models and integrated with Qualified Health’s governance platform, continuously scans vast datasets from the UT System to identify patients who may benefit from additional clinical evaluation. The system applies validated clinical guidelines and appropriateness criteria to ensure that care teams have a clear, evidence-based view of patients who may need further review or intervention. This approach addresses the operational challenge of modern healthcare, where fragmented records and unstructured data make it difficult to systematically identify patients eligible for guideline-recommended care.
Each year, millions of Americans who qualify for proven treatments are not identified in time, leading to preventable complications and disparities in access. In Texas alone, an estimated four to six million patients may miss appropriate care annually due to the limitations of manual chart review and the complexity of managing population-level data. Qualified Health and Anthropic’s AI deployment is designed to overcome these barriers by automating analysis while maintaining high standards of safety and clinical oversight.
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Eric Kauderer-Abrams, Head of Life Sciences at Anthropic, said, “Healthcare is one of the most demanding areas for artificial intelligence because it requires analyzing enormous amounts of complex and unstructured clinical information while following strict governance standards. Claude can perform this reliably, and when combined with Qualified Health’s platform and a forward-thinking health system like the University of Texas, it allows advanced AI to be applied safely at scale and helps close care gaps for millions of patients.”
Qualified Health’s AI system integrates information from multiple sources, assembling comprehensive patient profiles that combine notes, lab results, imaging, and procedural histories. It evaluates these profiles against guideline-based criteria and surfaces patients who may benefit from additional review directly into clinician workflows. Relevant context is provided to support informed decision-making and ensure care is delivered consistently across large populations.
Justin Norden, MD, MBA, MPhil, CEO of Qualified Health, said, “The challenge is not that we lack knowledge of what works. The difficulty lies in applying decades of evidence and clinical guidelines consistently across large health systems. This AI system is meant to enhance, not replace, clinical judgment. It allows clinicians to make evidence-based decisions at a scale that was previously impossible. By automating the identification of patients who meet criteria for care, we can ensure timely interventions and reduce missed opportunities for treatment.”
The initial deployment has gone live at the University of Texas Medical Branch, focusing on identifying gaps in guideline-recommended cardiology care. The system evaluates patients for appropriate medication doses, guideline-directed therapies, and necessary interventional procedures for conditions such as heart failure and valvular disease. Early results show that previously unrecognized high-risk patients are being accurately surfaced for clinician review, and care pathways for eligible patients are being accelerated. Clinicians reviewing the AI outputs have reported a high level of agreement with the recommendations, supporting trust in the system’s reliability.
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Following UTMB’s initial success, the AI platform will expand across the UT System and extend to other specialties, including primary care, vascular, gastrointestinal, rheumatology, and neurology. By the end of 2026, the deployment is expected to help additional health systems in Texas ensure that patients receive timely and appropriate care, particularly in underserved communities.
Zain Kazmi, Chief Digital and Analytics Officer and Associate Vice Chancellor of Health Affairs at the UT System, said, “Our health institutions serve patients throughout Texas. This initiative is not just layering technology on top of existing systems. We are building a shared foundation across the UT System that allows AI to be deployed consistently, responsibly, and with measurable impact. It enables us to learn collectively and apply intelligence effectively in patient care.”
The deployment is part of the University of Texas Research, Engineering, and Application Laboratory for Healthcare Artificial Intelligence initiative, UT REAL Health AI. The program is focused on expanding access to evidence-based care and establishing a framework for the safe and responsible implementation of AI in clinical practice.
Peter McCaffrey, Chief AI and Digital Officer at UTMB and Chair of the UT REAL Health AI initiative, said, “The goal of UT REAL Health AI is to improve the lives of Texans by using AI to enhance patient care, advance population health, and reduce overall healthcare costs. These AI deployments allow us to provide better care at scale while supporting clinicians and patients across the state.”
This deployment has been highlighted in public forums, including the J.P. Morgan Healthcare Conference, as health systems nationwide explore how AI can extend access to evidence-based care and improve patient outcomes at a population level.
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