Observational studyPloS one2024
Using machine learning to assess the extent of busy ambulances and its impact on ambulance response times: A retrospective observational study.
Observational study in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Assessment of clinical and logistical contribution in a Norwegian helicopter emergency medical service using integrated data: a retrospective observational study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Observational
- Age and gender patterns in emergency alarms and missions: a cross-sectional observational study.BMC emergency medicine · 2026Observational
- Use of artificial intelligence in the out-of-hospital care settings: a scoping review.BMJ digital health & AI · 2026Article
- Impact of heavy rainfall on ambulance response time in Japan.Scientific reports · 2025Article
- Does Delayed Response Due to Busy Ambulances Impact Risk of Death and Hospital Service Use?: A Cohort Study of 240,000 Medical Emergencies.Epidemiology (Cambridge, Mass.) · 2025Article
- Improving preparedness for time critical prehospital care: a descriptive study of the first responder system in Central Norway.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- A bi-level dynamic emergency route planning system considering signal preemption control using CV technology.PloS one · 2025Article
- Racing against time: Emergency ambulance dispatches and response times, a register-based study in Region Zealand, Denmark, 2013-2022.Scandinavian journal of trauma, resuscitation and emergency medicine · 2024Article
- How do different navigation systems affect emergency response time? A prospective simulation study.BMJ open · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAmbulance response times are considered important. Busy ambulances are common, but little is known about their effect on response times.
objectiveTo assess the extent of busy ambulances in Central Norway and their impact on ambulance response times.
designThis was a retrospective observational study. We used machine learning on data from nearby incidents to assess the probability of up to five different ambulances being candidates to respond to a medical emergency incident. For each incident, the probability of a busy ambulance was estimated by summing the probabilities of candidate ambulances being busy at the time of the incident. The difference in response time that may be attributable to busy ambulances was estimated by comparing groups of nearby incidents with different estimated busy probabilities.
settingMedical emergency incidents with ambulance response in Central Norway from 2013 to 2022.
main outcome measuresPrevalence of busy ambulances and differences in response times associated with busy ambulances.
resultsThe estimated probability of busy ambulances for all 216,787 acute incidents with ambulance response was 26.7% (95% confidence interval (CI) 26.6 to 26.9). Comparing nearby incidents, each 10-percentage point increase in the probability of a busy ambulance was associated with a delay of 0.60 minutes (95% CI 0.58 to 0.62). For incidents in rural and urban areas, the probability of a busy ambulance was 21.6% (95% CI 21.5 to 21.8) and 35.0% (95% CI 34.8 to 35.2), respectively. The delay associated with a 10-percentage point increase in busy probability was 0.81 minutes (95% CI 0.78 to 0.84) and 0.30 minutes (95% CI 0.28 to 0.32), respectively.
conclusionAmbulances were often busy, which was associated with delayed ambulance response times. In rural areas, the probability of busy ambulances was lower, although the potentially longer delays when ambulances were busy made these areas more vulnerable.
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