Study 04 · BMC Medical Informatics and Decision Making
Understanding EMS response times
How priority, workload, geography, weather and operational intervals shape variability and tail delays.
How priority, workload, geography, weather and operational intervals shape variability and tail delays.
Retrospective analysis of 1,144,754 EMS missions in Stockholm, 2017–2022, integrating operational, geographic and weather variables with regression and machine-learning analyses.
Method
Retrospective analysis of 1,144,754 EMS missions in Stockholm, 2017–2022, integrating operational, geographic and weather variables with regression and machine-learning analyses.
Key findings
Priority was the strongest overall feature, but workload, call reason, geography, precipitation, temperature and operational intervals interacted. Lower priorities had markedly long-tailed response-time distributions.
Operational relevance
Management should monitor distributions and extreme delays across all priorities, not only a median for the highest priority. System strain can be redistributed rather than removed.
Interpretation boundaries
Traffic information was unavailable, the study was conducted in one region and predictive performance does not make the models ready for real-time deployment.