TITLE:
EMS Response Time for Patients Critically-Injured from Automobile Accidents Using Regression Analysis
AUTHORS:
Sneha R. Vanga, Phillip M. Ligrani, Mehrnaz Doustmohammadi, Michael Anderson
KEYWORDS:
Automobile Collision, Emergency Medical Services (EMS), EMS Response Time, Geographically Weighted Regression (GWR), Global Regression Analy-sis (GRA)
JOURNAL NAME:
Current Urban Studies,
Vol.9 No.3,
September
22,
2021
ABSTRACT: Background: The ability to quickly and effectively receive
medical treatment in the event of an automobile collision is one of the most
important aspects in emergency medical services (EMS). Emergency medical
service providers are the first to respond and manage cases related to trauma,
emergency surgery, and critically injured patients. Response time for emergency
medical services vehicles is especially
important for areas, where travel distances are often much larger,
compared to more urban areas. The importance of the present data and analysis
procedures are their applicability to multiple environments, including urban
settings. Methods: The present study is focused on optimization of analysis tools, and
understanding the influences of different traffic-related
variables, related to hospital EMS transport times for Pickens County, a county
in west Alabama. Optimization of associated analysis tools is important for
optimal trauma patient survivability, and as such, is directly relevant to the
management of care for severely injured surgical patients. Of particular
interest are the effects of variables, such as travel time, time of the day,
day of the week, weather, lighting conditions, and crash severity, on the EMS
response time (ERT), which are analyzed using two types of advanced regression
analysis: geographically weighted regression (GWR) and global regression analysis (GRA). Results: For GWR analysis, the accuracy of the approach is improved by employing an adaptive bi-square kernel weighting
function. The GWR approach is also unique because geographic location
variations are quantified for local independent variables, as their effects are
included. Magnitudes of variable
coefficients, and variable t-statistic values provide information on the
relative influences and impacts of different variables, and different variable
combinations, as they are considered in pairs, triplets, and different
combinations. Conclusion: The resulting effects and alterations to optimal EMS response time are
provided for a wide range of travel conditions and travel time periods.