  Patient flow analysis can be studied from a clinical and or operational
perspective using simulation. Traditional statistical methods such as
stochastic distribution methods have been used to construct patient flow
simulation submodels such as patient inflow, Length of Stay (LoS), Cost of
Treatment (CoT) and Clinical Pathway (CP) models. However, patient inflow
demonstrates seasonality, trend and variation over time. LoS, CoT and CP are
significantly determined by attributes of patients and clinical and laboratory
test results. For this reason, patient flow simulation models constructed using
traditional statistical methods are criticized for ignoring heterogeneity and
their contribution to personalized and value based healthcare. On the other
hand, machine learning methods have proven to be efficient to study and predict
admission rate, LoS, CoT, and CP. This paper, hence, describes why coupling
machine learning with patient flow simulation is important and proposes a
conceptual architecture that shows how to integrate machine learning with
patient flow simulation.
