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Abstracts of the ICA
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Articles | Volume 1
15 Jul 2019
 | 15 Jul 2019

Evaluating transport time in emergency medical service via GIS: an observational study of Tokyo

Tianqi Xia, Shuzhe Huang, Xuan Song, Ruochen Si, Xiaoya Song, Ryosuke Shibasaki, and Kyoung-Sook Kim

Keywords: Spatial Analysis, Emergency Medical Service, Geostatisics

Abstract. Emergency medical service (EMS) is one kind of medical services which focuses on providing first-time rescue to victims of sudden and life-threatening emergencies. Since a lot of studies have pointed out a close relation between the increase of cost time before the patient accommodated to the hospital and the increased risk of mortality, a reasonable distribution of EMS facilities can shorten the transportation time from the scene to the hospital and is critical to ensure the quality of the EMS system.

With the development of geographic information science and technology, GIS provides the visualization and analysis approaches for the distribution of the EMS cases, ambulance cars and hospitals as well as the ability of measuring road network distance, which results in the popularity of research with GIS based analysis approaches in the field of public health and EMS system. Despite of the prosperity in such kind of studies, most of them focused on evaluating EMS system by observational case analysis while paying less attention on the emergency medical resource distribution.

With the concerns we mentioned above, this research conducts spatial and temporal analysis for evaluating the transportation time via several GIS methods and take the EMS cases in Tokyo 23 wards as a case study. In addition to the observational studies with EMS case data and several spatial and temporal factors, we pay more attention on evaluating the distribution of cases and hospitals from both sides of demand and supplement. In addition, we also check several assumptions that are widely used in accessibility analysis on public health. As far as we know, our work is first research on detailed hospital distribution analysis in Tokyo area based on observations.

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