Study and
analysis of the health care systems have become a necessity to
improve its performance over time as it must meet a number of often
conflicting objectives such as providing better and more efficient
patient care while minimizing the cost of health care and resources
(1, 2). Hospital management as an important component of healthcare
systems may face with numerous challenging tasks while achieving
these goals (2-4). In a hospital system, the flow of patients is a
determinant factor that affects the performance of healthcare
delivery processes. The decision problems of a hospital are
directly related to and affected by the month to month changes in
patient flows. The short-term forecasting of patient flows is the
fundamental input of short-term decision making and planning on
hospital and laboratory equipment, staff resources, food and
laundry service demands, and like it. Furthermore, the long-term
forecasts of patient flows are vital to long-term planning
decisions about resources and capital budgeting which its positive
gains, in the long run, will lead to a sequence of capital
expenditure (5). In this context, the knowledge gained from an
accurate prediction of patient flows would provide valuable
information for resource allocation and strategic planning and also
has the potential to minimize patient care delays,
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To the best
knowledge of the authors, use of artificial neural network (ANN)
time series analysis in
healthcare has
been limited and there is a research gap to study the
predictability of ANN time series modeling for patient
flow
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