Measuring and optimizing accessibility to emergency medical services

Luo, Weicong (2022) Measuring and optimizing accessibility to emergency medical services. PhD thesis, University of Glasgow.

Abstract

Emergency medical services (EMSs) undertake the responsibility of providing rapid medical care to patients suffering from unexpected illnesses or injuries and transferring them to definitive care facilities. This research concerns several research gaps that are associated with different EMS trips, real-time traffic conditions, improving EMS efficiency and equalities. This research aims to develop GIS-based spatial optimization methods to improve service efficiency and equality in EMS systems. Specifically, the research intends to achieve the following goals: (1) to measure spatiotemporal accessibility to EMS; (2) to improve EMS efficiency and provision through spatial optimization approaches; (3) to reduce urban-rural inequalities in EMS accessibility and coverage using spatial optimization approaches. The proposed approaches are applied in three empirical studies in Wuhan, China.

To achieve the first objective, the proximity and the enhanced two-step floating catchment method (E-2SFCA) are adopted to evaluate spatiotemporal accessibility. First, the EMS travel time is estimated for the two related trips as an overall EMS journey: one is from the nearest EMS station to the scene (Trip 1), and the other is from the scene to the nearest emergency hospital (Trip 2). Then, the E-2SFCA method is employed to calculate the accessibility score that integrates both geographic accessibility and availability of EMS. Travel time is estimated by using both static road network with standard speed limits and online map service considering real-time traffic.

To achieve the second objective, two facility location models are proposed to improve EMS service coverages for two-related trips (Trips 1 and 2). The first model maximizes the amount of demand covered by both ambulance coverage (EMS station – demand) and hospital coverage (demand – hospital). The second model maximizes the amount of demand that can be served by both ambulance coverage and overall coverage (EMS station – demand – hospital).

To achieve the third objective, two bi-objective optimization models are developed. The two models have the same primary objective to maximize the total covered demand by ambulance. The second objective is to minimize one of the two inequality measures: one focuses on accessibility of uncovered rural people, and the other concerns the urban-rural inequality in service coverage.

For the first empirical study with respect to spatiotemporal access to EMS, different spatial patterns are found for the three trips (two partial trips and the overall trip). Good accessibility to one trip cannot guarantee good accessibility to another trip. In addition, urban-rural inequalities in EMS accessibility and coverage are observed. Finally, it is observed that real-time traffic conditions greatly affect EMS accessibility, particularly in urban districts. Specifically, the accessibility of EMS becomes poor during the morning (7-9 am) and evening peak periods (5-7 pm).

For the second empirical study in relation to EMS optimization involving two related trips, the results find that the first proposed model can guarantee that more demand to be covered by both ambulance and hospital coverages than the Maximum Coverage Location Problem (MCLP). The second proposed model can ensure that as many people as possible to be served by both ambulance and overall coverage than the work by ReVelle et al. (1976).

For the third empirical study attempting to reduce urban-rural inequality in EMS, the results show that the first bi-objective model can improve EMS accessibility of uncovered rural demand, and the second model can reduce EMS service coverages between urban and rural areas. However, the improvement EMS inequalities between urban and rural areas leads to a cost of a decrease in the total covered population, especially in urban areas.

Regarding policy implications, this research suggests that different EMS trips and traffic conditions should be considered when measuring spatial accessibility to EMS. Spatial optimization research can help improving service efficiency and reduce regional equalities in EMS systems. The work presented in this thesis can aid the planning practice of public services like EMS and provide decision support for policymakers.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: GIS, spatial optimization, accessibility, service coverage, EMS.
• Colleges/Schools: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Supervisor's Name: Yao, Dr. Jing and Mitchell, Professor Richard
• Date of Award: 2022
• Depositing User: Theses Team
• Unique ID: glathesis:2022-83279
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 25 Nov 2022 14:10
• Last Modified: 13 Dec 2022 12:28
• Thesis DOI: 10.5525/gla.thesis.83279
• URI: https://theses.gla.ac.uk/id/eprint/83279

Related URLs

• Article DOI
• Article DOI
• Article DOI