Ryan Grattan

Supporting Continuous Improvement in Healthcare with Machine Learning

This project investigates the informatics architecture required to realize a continuous Learning Health System (LHS) in intensive care using offline reinforcement learning. Using mixed shock hemodynamic management as a focal use case, this work aligns organizational needs, algorithmic constraints, and end-user workflows to aid institutional quality improvement. The resulting framework bridges computational feasibility and bedside utility by proposing a human-in-the-loop validation model integrated directly into existing Morbidity and Mortality reviews.