The complexity of healthcare delivery frequently requires methodological innovations to methods developed for other settings. The need in healthcare to rapidly model disruptive system changes (e.g., a novel disease) is gaining awareness.  Electronic Medical Records  implementations are increasingly harvested through decision support systems.  Operations Research, Machine Learning, and AI methods are being tailored to better address the unique needs of healthcare.  Supported by practice-based collaborations with several hospitals, we examine advances in personalized bed admissions and placement, including consideration of capacity and other constraints.  Incorporating the prediction and control of unique patient outcomes  following a shock, we present research on adaptive learning and decision making to assign a bed unit type (ICU, PCU, General) with the goal of reducing harmful individual outcomes (i.e., unplanned 30-day hospital readmissions). COVID 19 elevated the need for joint adaptive machine learning and optimization for the allocation of reusable resources in the face of health outcome feedback that is delayed. We present a stratified adaptive learning and optimization approach to balance the management of care under resource constraints in terms of efficiency versus effectiveness.