Alternative Methods for Forecasting Variations in Hospital Bed Admission

Abstract

The Malaysian healthcare system is well-being recognized for providing a wide range of access to primary healthcare. The number of hospitals is found to be growing in line with the increase in population. However, over-crowding has become the most common scene that people see in every hospital. The number of  patients being admitted may  somehow  mislead  healthcare planners,  and  thus  causing  them  to  underestimate  the  resources  that  are  required  within  the  hospital. Thus, this study aims to identify better forecasting models for variations in hospital bed admission considering State Space Model (SSM). Data on the admission rate of a state hospital was collected, spanning the period of historical data from 2001 until 2015. The findings indicate that State Space model can outperform common model due to its lower Mean Squared Errors. Female aged between 25 -34 years old are found to be having the highest variation, which could lead to unpredictable in terms of being admitted to hospital.