An adaptable model for finding a proper path in emergency medical transport system

碩士 === 國立臺灣大學 === 醫療機構管理研究所 === 94 === When people are caused to injury in an accident, one of the most important things of all is to move them to a hospital in time. It is obvious that time is crucial to rescue persons with serious injury. So far, the transportation is often decided by human beings...

Full description

Bibliographic Details
Main Authors: Guan-Hsien Huang, 黃冠賢
Other Authors: 蘇喜
Format: Others
Language:zh-TW
Published: 2005
Online Access:http://ndltd.ncl.edu.tw/handle/81805868724173161601
Description
Summary:碩士 === 國立臺灣大學 === 醫療機構管理研究所 === 94 === When people are caused to injury in an accident, one of the most important things of all is to move them to a hospital in time. It is obvious that time is crucial to rescue persons with serious injury. So far, the transportation is often decided by human beings. Sometimes, according to not familiar with the traffic condition or hospital facilities, such as whether enough beds or being able to offer such supports or not, we often lose the golden hours on emergency rescue. This thesis presents an adaptable model to find a proper path for Emergency Medical evacuation. Geographical Information Systems (GIS) are involved in our design to provide a friendly interface to illustrate the path for evacuation. A system integrated with GIS to offer EMS organization to plan a path from stations to accident places and a path from accident places to hospitals by taking account of the condition of traffic, and avoiding the crowed roads. We consider the time and EMS resource of hospitals as parameters of the program, and put them into database of the system. We also select some cases randomly for simulating in order to evaluate the program, and use SPSS for inferential statistics. The data which were gathered from stations in Taipei were made up by Excel. We selected 202 cases in peak hour, 64 cases in 7:00-9:00, the others in 17:00-20:00. The results of our simulation are compared by using inferential statistics. A significant improvement can be archived if an adaptable path of rescue can be obtained prior to a fluctuated event happened. After comparing by using inferential statistics such as paired t test, we can conclude that it is great helpful to save the time from stations to accident places and from accident places to hospitals.