Study IV
Understanding EMS response times
Machine-learning-based analysis of response-time variability and system delay mechanisms in emergency medical services.
Publication
Hill P, Lederman J, Jonsson D, Bolin P, Vicente V. Understanding EMS response times: a machine learning-based analysis. BMC Medical Informatics and Decision Making. 2025;25:143.
Study aim
The study examined EMS response-time variability and explored how system, temporal, geographic, and operational factors contribute to delayed ambulance arrival.
Analytical approach
The study used routinely collected EMS data and machine-learning-based analysis to inspect nonlinear associations and system-level delay patterns. The purpose was to improve interpretability and operational understanding, not to produce an autonomous allocation model.
System delay
Response-time variation reflects multiple interacting system factors rather than a single operational bottleneck.
Operational context
Workload, geography, time of day, priority, and resource availability contribute to response-time patterns.
Governance relevance
The findings support more transparent discussion of performance, capacity, and risk distribution in EMS governance.
