Parameter estimation for optimised number of beds in hospital planning design
Designing hospital buildings are a complex process which has to cater for different needs and requirements. The healthcare industry is expanding rapidly as the population increase in both public and private sector. Other than population, the other variables which can affect the number of hospital be...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/102813/1/YahayaHassanPSBME2022.pdf.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Designing hospital buildings are a complex process which has to cater for different needs and requirements. The healthcare industry is expanding rapidly as the population increase in both public and private sector. Other than population, the other variables which can affect the number of hospital beds requirement are (1) admission rate, (2) average length of stay and (3) bed occupancy rate. Various models and methods for determining the number of hospital beds were developed to cater for regional and hospital level requirements. The factors for planning hospital capacity are based on the population of the region, the frequency of admission, occupancy of bed as well as length of stay. This thesis proposes a new predictive formula to determine the number of hospital beds by replacing the variable population of region and frequency of admission, with number of outpatients within the region and conversion rate of outpatient to inpatient respectively. Further discussion has been focused on the initial stage of hospital planning which incorporate nine design parameters in five design processes and the relationship between these parameters in each design processes as to determine the correct bed capacity. Data was analysed using Microsoft Excel – Data Analysis method of Regression Analysis during the research. This method was used to establish and predict the dependent variable (number of beds) using one or more independent variables. It was also predict the design bed capacity with a given suitable data’s that have been extracted from management and operation inputs. Outputs from regression analysis successfully produced the trend in graphical form that predict the bed capacity. Using 8 private hospitals data from KPJ Hospitals and Health Facts from Ministry of Health Malaysia, results show a correlation between number of bed (dependent parameter) and other independent parameters which are, No. of Clinics, No. of Outpatients, No. of Inpatients, Bed Occupancy Rate and Length of Stay. Based on these 5 independent variables, the formula managed to predict the optimum number of beds required at hospital levels. Therefore, these parameters can be used to calculate the gap between formulated number of beds and the available bed to determine the present/future bed requirement. |
|---|