Every cardiac surgery program strives to deliver the safest, highest-quality care possible. Some of the most valuable opportunities for improvement occur when healthcare teams review outcomes retrospectively and ask: What can we learn, and how can that knowledge help future patients?
The UT-MOST team's latest artificial intelligence project is exploring the use of AI for the identification altered mental status (AMS) described in unstructured clinical notes.A change in a patient’s mental status can be an important warning sign of sepsis. Yet those observations may sit in different parts of the electronic health record. Signs of altered mental status (AMS) may be documented in physician notes, nursing assessments, emergency department narratives, or progress notes, making it difficult to bring them together when assessing a patient’s risk.
Healthcare quality continues to evolve as new challenges emerge, priorities shift, and innovative approaches develop. What remains constant is the need for organizations to learn from one another and continuously improve the care they deliver. That commitment to shared learning remains at the heart of the Vizient Quality Collaborative. By combining data, expertise, and diverse perspectives, members can learn not only from their own experiences but also from peers facing similar challenges.
Every cardiac surgery program strives to deliver the safest, highest-quality care possible. Some of the most valuable opportunities for improvement occur when healthcare teams review outcomes retrospectively and ask: What can we learn, and how can that knowledge help future patients?
