An Integrated Spatio-temporal Analysis of Emergency Medical Service Response Characteristics for Stroke Events Across Alabama
Xiaobing Li (firstname.lastname@example.org), University of AlabamaShow Abstract
Qinglin Hu, University of Alabama
Abbey Gregg, University of Alabama
Stroke treatment must be given within the first few hours of symptom onset, so rapid Emergency Medical Services (EMS) response is essential for positive patient outcomes. Examining EMS response characteristics across temporal and geographical perspectives is critical for improving Alabama’s stroke death rate from its current 49th place in the U.S. We examined EMS delay characteristics for patients with suspected strokes from 2018-2019 across Alabama, with particular attention paid to rural and urban differences. We used EMS call data from the Alabama Department of Public Health and defined possible stroke cases as calls where a stroke scale completed by EMS was positive or indeterminate. Time between EMS dispatch and destination arrival was coded as EMS delay. This study incorporates global and local spatio-temporal weighted ordered logistic regression models to evaluate significant non-stationary correlations of factors with EMS delay by accounting for unobserved heterogeneity. There were 17,088 possible stroke cases, and 74% of these calls had total response times within 60 minutes. Longest EMS delay was observed in rural counties. EMS response characteristics, such as pre-arrival instructions and advanced vehicle navigation were associated with shorter delays, while long travel distance and on-scene/transport times were associated with longer EMS delays. The impact of response characteristics on stroke-event based EMS delay varied significantly across rural and urban counties and time (i.e., between 2018 and 2019). The revealed spatio-temporal correlations are useful for EMS personnel in applying effective localized rural and urban EMS response improvement strategies.
Analysis and Comparison Between Crash- and Health-based Emergency Medical Service Response Across Alabama
Xiaobing Li, University of AlabamaShow Abstract
Qinglin Hu, University of Alabama
Abbey Gregg, University of Alabama
Emergency Medical Services (EMS) can respond to multiple types of urgent requests from patients demanding urgent healthcare. The total time duration of EMS responding to those requests consists of multiple delays at several stages (e.g., response-to-scene, on-scene, transport). Additionally, the corresponding delay at each stage may correlate with different characteristics (e.g., number of crews/patients, signal priority, acuity). Furthermore, those characteristics can have diversified correlations with EMS response for different urgent requests. To capture the diversified correlations for crash-based and health-based EMS requests, this paper collected EMS call data (2018-2019) from the Alabama Department of Public Health and defined crash-based EMS responses as people who are injured in traffic accidents and health-based EMS response as people who either have heart-related health issues or stroke. To account for unobserved heterogeneity due to limited available data, we use random parameter parametric survival models to explore potential varying correlations of the characteristics associated with delays at different stages for three EMS response types. The results indicate that 108,304, 110,262, and 24,421 EMS responses were requested for traffic crashes, heart problems, and stroke, respectively. The modeling results show that at different stages of EMS responses, the associated characteristics are diversified. More importantly, the associations of the same characteristics with delays are significantly distinct for the three studied EMS response types at different response stages. For example, advanced vehicle technologies are found to have positive associations at response-to-scene stage, but negative associations at transport stage for crash-based EMS responses. More implications are discussed in the paper.
Modular technology for Emergency Medical Services
Gaby Joe Hannoun (email@example.com), New York University, Abu DhabiShow Abstract
Monica Menendez, New York University, Abu Dhabi
While advancements in vehicular and wireless communication technologies are shaping the future of our transportation system, emergency medical services (EMS) are not receiving enough research attention. Their operations are still plagued by response delays that can often be life-threatening. Dispatching and redeployment systems identify the best practices regarding the allocation of the resources to emergencies and stations. The existing systems are unfortunately insufficient, and there is a growing need to embrace new technological solutions. This research introduces a smart system for EMS by leveraging the modular vehicular technology initially developed for transit systems. The proposed system relies on the design of vehicular modules that can couple and decouple to transfer patients from a module to another during transport. A fleet of medical transport vehicles is deployed to cooperate with the life support vehicles by providing for example transport and hospital admission tasks, thus allowing life support vehicles to answer pending emergency calls earlier. This is especially useful when there is a large demand for EMS (e.g. under the COVID-19 pandemic). This paper develops a mathematical programming model to determine the optimal assignment decisions. A comparative analysis is executed and results show that reductions in response times and arrival times to hospital can be achieved with the modular technology, with average improvements of up to 17.51% and 40.88% respectively, for the tested scenarios.
Exploring Influencing Factors on Crash-related Emergency Response Time: A Machine Learning Approach
Aryan Hosseinzadeh, University of LouisvilleShow Abstract
Mohamadreza Haghani, University of Louisville
Robert Kluger, University of Louisville
Crashes lead to three million injuries in the United States every year who needs immediate care and swift transfer to the emergency department of hospitals. Reducing ambulance response time can save lives. Therefore, identifying the factors associated with emergency medical services response time can help to shape policy and operational changes. The objective of the current study is an investigation to find what impacts emergency response time in motor vehicle crashes among individual-related and crash-related variables. It followed with a more in-depth investigation of each factor in Jefferson County, Kentucky. The study employed a linked dataset of police-reported crash data, EMS runs from Computer-Aided Dispatch and Patient Care Reports. In this study, EMS response time was modeled and compared using four machine learning approaches, as well as regression analysis. The most successful approach in terms of root mean square error and goodness of fit was chosen to represent contributing factors. The results show EMS travel distance, the discrepancy between police and dispatch location, crash type, time of day, number of injuries and injury location code were important factors in EMS response time. The study outcome can be used to guide practice and help EMS reduce the time to care for motor vehicle crashes.
Freeway Incident Diversion Behavior as a Measure of Transportation Network Resiliency
Ravindra Gudishala, ArcadisShow Abstract
Saurabha Bawankule, Arcadis US Inc
Chester Wilmot, Louisiana State University
Brian Wolshon, Louisiana State University
Disruption caused by an incident reduces the performance of transportation networks. In this case, road users try to redistribute themselves onto alternate serviceable routes. Assuming that no external assistance is provided to the network for recovery, redistribution behavior of the road users helps the network in mitigating performance lost due to incident. The main objective of the study was to propose generic metrics and a formula for measuring resiliency of a transportation network in the context of freeway incident diversionary behavior and the secondary objective was to apply and check its workability on the data collected for LA DOTD’s (Louisiana Department of Transportation and Development) diversionary behavior study. Other objective includes understanding and interpreting the motorist’s adaptive behavior in resiliency context. The findings from the study indicate that resiliency is not a fixed value for facility but varies depending on the severity of the incident, the opportunities in the network for diversion, by the behavior of road users, and actions of network managers.
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