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Thesis - University Access Only
Master of Science (MS)
Department / School
Civil and Environmental Engineering
This thesis aims at evaluating rural Emergency Medical Service (EMS) system with three proposed objectives: rural EMS needs assessment, evaluation and optimization of EMS station from spatial perspective, and identification of significant factors affecting EMS performance. This study used data collected in South Dakota, including EMS ambulance data, station data, and highway network information. Different statistical methods associated with spatial techniques were adopted in this study. Rural needs assessment including county based analysis and station based analysis were performed to evaluate EMS service demand and performance. In addition, contributing factors affecting service performance were explored, in preparation for regression analysis. Meanwhile, service performance benchmark was established based on the summary statistics of time, distance and speed. En-route time was selected as the EMS performance for the following analysis. Geospatial analysis was carried out to evaluate the locations of EMS stations to accomplish the second objective. Clustering of 911 calls was observed by the geospatial statistics such as Getis Ord G*. The spatial association of 911 calls and EMS stations were confirmed visually and statistically by call clusters and the Cross-K function respectively. In addition, two performance indexes were developed to evaluate the positioning and service quality of each EMS station. xiv In addition to geospatial analysis, methods for optimizing EMS station locations were also developed with two targets: increasing coverage ratio and service equality. Two counties were selected for the case study and optimal solutions were obtained by running the genetic algorithm in the R software. This method serves as a tool for EMS officials to plan new or relocate existing stations strategically to improve service performance based on limited resources. Last, regression analysis was performed to identify significant factors such as case type for service performance. Models with different assumptions and combinations of variables were developed and compared. Results showed that mixed Geographically Weighted Regression (GWR) model was a better option and coefficients of significant variables for each station were estimated. This model approach not only identifies statistically significant factors, but also provides more accurate predictions for the enroute time.
Library of Congress Subject Headings
Emergency medical services -- Evaluation
Rural health services -- Evaluation
Includes bibliographical references (pages 138-144)
Number of Pages
South Dakota State University
He, Zhaoxiang, "Comprehensive Evaluation of Rural EMS and Service Performance Metrics Development" (2015). Electronic Theses and Dissertations. 1840.