Abstract
Emergency Medical Services (EMS) are acute services provided outside of the hospital.
EMS are crucial in rural environments where hospitals are often far away and difficult
to access. Establishing EMS performance measures is critical in improving a rural
community's access to these services and eliminating systemic inequalities. However,
an absence of data leads to challenges in developing objective and quantifiable service
metrics. EMS data are regularly collected through the National EMS Information System
(NEMSIS), yet the manner of data collection and quality of data vary across agencies.
Moreover, the amount and complexity of information makes data analyses difficult,
subsequently effecting EMS leaderships' ability to identify improvement needs.
This study used NEMSIS data to exemplify approaches for establishing two data-driven
performance measures. The measures used in this study – timely service and service
coverage – are both dependent on the mobility and accessibility of the EMS transportation
network. Two types of spatial models: the spatial econometric model and geographically
weighted regression (GWR) model, were developed and then compared to the linear regression
model to help identify response time factors. GWR performed best in terms of goodness-of-fit
statistics and was chosen to help understand how factors (e.g., weather, transportation)
impact the timely provision of EMS in rural areas. The GWR results provided additional
insights through the particular spatial patterns of the coefficient estimates and
their statistical significance to EMS practitioner for their references to reduce
local response times.
Keywords
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Article Info
Publication History
Published online: November 20, 2018
Accepted:
November 20,
2018
Received in revised form:
October 5,
2018
Received:
June 4,
2018
Identification
Copyright
© 2018 Elsevier Inc. All rights reserved.
