One of the greatest challenges in acute care is the ability to hire and retain nurses. With high turnover and a limited candidate pool, how do you build a highly reliable organization with fewer nurses? This advanced care setting calls for Bachelor-level nurses, but those can be hard to come by, and contract labor is expensive and unsustainable in the long term. IHQSE graduate, Stephanie Elston, MSN, and her team have built a model that optimizes support to the front-line staff, helping to relieve the disproportionate reliance on RNs.

In-basket burden is frequently cited as a contributor to burnout. In a research letter published in JAMA Network Open, IHQSE graduate, Dr. Eden English, discusses her recent study of the use of large language models (LLMs) for drafting replies to patient messages within a large health system.

IHQSE graduate Dr. Daniel Lam is lead author on this publication in Pediatric Quality & Safety which discusses the implementation and outcomes of a fast-track pathway called "Supertrack" in a pediatric emergency department. Fast-track models decrease patient crowding in emergency departments (EDs) by redirecting low-acuity patients to an expedited care pathway. This intervention resulted in improved flow of "Supertrack" patients in addition to decreased length of stays and increased timely discharges.

IHQSE faculty member Dr. Sandra Spencer and a team quality improvement experts at Children’s Hospital Colorado developed a novel fast-track model, typically seen in emergency departments, and implemented it in urgent care. The initiative resulted in a 74% reduction in door-to-provider time and 36% reduction in urgent care length of stay. The fast-track process also continued to function and reduce door-to-provider and urgent care length of stay times during a large volume surge. Improvements in these metrics for higher acuity patients in the co-located emergency department were also observed. The results of this QI project suggest that a FT model can improve efficiency in an appropriately selected UC setting.