Improving Rural Emergency Medical Services (EMS) Through Transportation System Enhancements Phase II

Qin, Xiao; He, Zhaoxiang; Samra, Haifa · 2015 · ROSA P / Mountain Plains Consortium

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Summary

This study addresses the critical challenge of improving rural Emergency Medical Services (EMS) through transportation system enhancements, specifically focusing on South Dakota. The research is motivated by the disproportionately high fatality rates in rural areas, where long travel distances, sparse populations, and underfunded EMS systems hinder timely access to care. The primary objectives were to establish data-driven performance measures for rural EMS, optimize station locations to increase resource utilization, and identify key variables contributing to response times, particularly en route time. The researchers utilized National EMS Information System (NEMSIS) data from South Dakota to develop two specific performance metrics: service coverage and timely service. Service coverage was measured by the ratio of emergency calls within an 8-minute response zone to total calls, while timely service was gauged by the percentage of calls responded to within that 8-minute threshold. To optimize station placement, the study employed a bi-objective covering location model that balanced maximizing ambulance coverage (Maximal Covering Location Problem) with minimizing en route time (Location Set Covering Problem). This optimization was executed using a genetic algorithm. Additionally, the study conducted regression analyses, including Multiple Linear Regression and Geographically Weighted Regression (GWR), to determine factors influencing en route time. Case studies were performed in Todd County (low population, high demand) and Minnehaha County (high population, moderate demand) to demonstrate optimal solutions under varying constraints. The results identified 13 key variables affecting en route time, including call-specific factors (case type, location) and provider-specific factors (volunteer vs. professional status, unit hour utilization, road connectivity). The GWR model provided the best statistical fit, revealing spatial heterogeneity in how these variables influence response times across different regions. The optimization models demonstrated that relocating or augmenting stations could significantly improve service coverage and reduce en route times for uncovered areas. However, the study noted a trade-off where reducing average en route time for uncovered zones could decrease the total coverage ratio, necessitating a balanced approach. While the GWR model improved prediction accuracy, its overall goodness-of-fit remained low, suggesting the need for further spatial modeling techniques. The significance of this work lies in providing a quantitative framework for evaluating and enhancing rural EMS performance, moving beyond anecdotal evidence to data-driven decision-making. By establishing specific metrics and optimization strategies, the study offers actionable insights for policymakers and EMS administrators to improve resource allocation and reduce systemic inequalities in rural healthcare access. The findings highlight the urgent need for better EMS data quality and the potential benefits of linking EMS data with patient outcomes to validate the impact of reduced response times on survival rates.

Key finding

Geographically Weighted Regression provided the best statistical fit for identifying variables affecting en route time, while optimization models demonstrated that strategic station relocation can balance service coverage and response time equity.

Methodology

modeling

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• post crash care delays