Assessing and Planning of the Location of Emergency Medical Facilities Using Spatial Optimization Modeling Approach

Keywords: Spatial Optimization, Medical Geography, Disparity
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 02:00 PM (Eastern Time (US & Canada)) - 2/25/2022 03:20 PM (Eastern Time (US & Canada))
Room: Virtual 26

Abstract

The emergency medical care is one of the critical infrastructures to ensure citizen’s quality of life. However, most of the emergency medical facilities are not evenly located for the potential demands best, which raises the issue of a geographic disparity of health care service access. The main goal of this research is to provide solutions to mitigate the disparity of accessibility of emergency medical care using an improved dispersion problem, named the p-dispersed-median problem (p-DIME), which integrates two classes of location models, the dispersion and median problem. The p-DIME assesses the service area of emergency medical facilities, seeking to identify optimal locations of emergency medical facilities and demand assignment plan for improving the equity of the accessibility. The model is applied to the area of Gyeongsangbuk-do (province), South Korea. Based on computational experiments, the p-DIME model outperforms compared to the traditional maxisum dispersion problem, demonstrating improved service coverages given the same number of emergency medical facilities considered. The result indicates that smaller number of emergency medical facilities is substantially in need to cover the Gyeonsangbuk-do, which suggesting the re-arrangement of existing emergency medical facilities, which is anticipated to curtail current public spending on the designation of the new emergency medical facilities for the local community. The findings also indicate the expandability of the p-DIME to provide realistic optimal solutions to various medical service failure scenarios such as selected relocation of facilities and their reassignment of demand from natural disasters.