Modeling EMS Response Time Variability in High-Acuity Incidents: Operational, Temporal, and Sociodemographic Predictors

Timely emergency medical service (EMS) response is critical for patient survival in high-acuity incidents, yet performance often varies across communities. This study examines more than 50,000 EMS calls from East Baton Rouge Parish to model the variance in response times using a multivariate framework. Operational factors such as station proximity, dispatch protocols, and call density are evaluated alongside temporal trends and sociodemographic predictors, including incorporated versus unincorporated service areas. The analysis identifies key drivers of delay, quantifies their relative contributions, and highlights where structural inequities or system inefficiencies contribute to longer response times. Findings provide actionable insights for EMS agencies, healthcare leaders, and policymakers by pinpointing opportunities to optimize station deployment, adjust dispatch strategies, and improve equity in emergency access. By linking operational analytics with community context, this work advances data-driven approaches to enhance responsiveness and outcomes in critical prehospital care.

After attending this Abstract, the attendees will be able to explain how operational, temporal, and sociodemographic factors interact to influence EMS response time variability and describe how these insights can be used to evaluate performance benchmarks and identify systemic inefficiencie

After attending this Abstract, the attendees will be able to apply multivariate modeling concepts and geospatial insights to design practical strategies for optimizing EMS station placement, adjusting dispatch protocols, and reducing inequities between incorporated and unincorporated service areas in their own organizations.