Alternative Methods for Forecasting Variations in Hospital Bed Admission
| Autor: | Mohd Azuan Suhaimi, S. Sarifah Radiah Shariff, Siti Meriam Zahari, Zuraidah Derasit |
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| Rok vydání: | 2018 |
| Předmět: |
Control and Optimization
Computer Networks and Communications Hospital bed Population Primary health care 01 natural sciences 010104 statistics & probability State Space Model (SSM) Health care Medicine 0101 mathematics Electrical and Electronic Engineering education State hospital Alternative methods education.field_of_study business.industry Admission rate medicine.disease Hardware and Architecture Signal Processing Forecasting Variations Medical emergency business Information Systems Healthcare system |
| Zdroj: | Indonesian Journal of Electrical Engineering and Computer Science. 9:410 |
| ISSN: | 2502-4760 2502-4752 |
| DOI: | 10.11591/ijeecs.v9.i2.pp410-416 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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