  The quality of service in healthcare is constantly challenged by outlier
events such as pandemics (i.e. Covid-19) and natural disasters (such as
hurricanes and earthquakes). In most cases, such events lead to critical
uncertainties in decision making, as well as in multiple medical and economic
aspects at a hospital. External (geographic) or internal factors (medical and
managerial), lead to shifts in planning and budgeting, but most importantly,
reduces confidence in conventional processes. In some cases, support from other
hospitals proves necessary, which exacerbates the planning aspect. This
manuscript presents three data-driven methods that provide data-driven
indicators to help healthcare managers organize their economics and identify
the most optimum plan for resources allocation and sharing. Conventional
decision-making methods fall short in recommending validated policies for
managers. Using reinforcement learning, genetic algorithms, traveling salesman,
and clustering, we experimented with different healthcare variables and
presented tools and outcomes that could be applied at health institutes.
Experiments are performed; the results are recorded, evaluated, and presented.
