Alternative methods for forecasting variations in hospital bed admission

• Published in: Indonesian Journal of Electrical Engineering and Computer Science
• Main Author: Shariff S.S.R.; Suhaimi M.A.; Zahari S.M.; Derasit Z.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040045618&doi=10.11591%2fijeecs.v9.i2.pp410-416&partnerID=40&md5=8aa31601abc11ce6e61490e3b54f1dc2

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, overcrowding 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. © 2018 Institute of Advanced Engineering and Science. All rights reserved.