Emergency versus standard response: time efficacy of London’s Air Ambulance rapid response vehicle

Rehn, Marius; Davies, Gareth; Smith, Paul H.; Lockey, David

doi:10.1136/emermed-2017-206663

Cited by 12 publications

(14 citation statements)

References 18 publications

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“…Rehn and colleagues have demonstrated that typical traffic conditions result in substantially slower transport to hospital 4. We have found that the online controlling of traffic lights is a feasible system that can decrease ambulance drive times to hospitals.…”

mentioning

confidence: 80%

Time is brain: An online controlling of traffic lights can save lives

Amiri

Vafaeinezhad

Azarpazhooh³

2017

Emerg Med J

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mentioning

confidence: 80%

Time is brain: An online controlling of traffic lights can save lives

Amiri

Vafaeinezhad

Azarpazhooh³

2017

Emerg Med J

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“…The first responders’ arrival time varies around the world and is influenced by geographical isolation. Countries with a developed ambulance service report a response time of less than 15 min [[9], [10], [11]]. Time from ambulance response to hospital admission is also an important consideration when trying to replicate a real-world scenario.…”

Section: Methods Detailsmentioning

confidence: 99%

A new model for standardising and treating thermal injury in the rat

2019

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“…Insignificant variables were dropped during the model estimations. The ambulance drivers, usually violating the traffic rule for the sake of time constraints [25], determined the severity of the injury the most. It appeared that male drivers or injured ambulance drivers were involved more in crashes with the worse injuries.…”

Section: Ol Regression Modelmentioning

confidence: 99%

Fail to Yield? An Analysis of Ambulance Crashes in Taiwan

Jou

Chao

2021

Sustainability

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Introduction—Medical emergency vehicles help patients get to the hospital quickly. However, there were more and more ambulance crashes on the road in Taiwan during the last decade. This study investigated the characteristics of medical emergency vehicle crashes in Taiwan from January 2003 to December 2016. Methods—The ordered logit (OL) model, multinominal logit (MNL) model, and partial proportional odds (PPO) model were applied to investigate the relationship between the severity of ambulance crash injuries and its risk factors. Results—We found the various factors have different effects on the overall severity of ambulance crashes, such as ambulance drivers’ characteristics and road and weather conditions. When another car was involved in ambulance crashes, there was a disproportionate effect on the different overall severity, as found by the PPO model. Conclusions—The results showed that male ambulance drivers and car drivers who failed to yield to an ambulance had a higher risk of severe injury from ambulance crashes. Ambulance crashes are an emerging issue and need further policies and public education regarding Taiwan’s ambulance transportation safety.

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Emergency versus standard response: time efficacy of London’s Air Ambulance rapid response vehicle

Abstract: The current study found RRVs to be significantly quicker when responding with lights, sirens and traffic rule exemptions compared with a response being compliant with all traffic signals, road signs and speed limits.

Cited by 12 publications

References 18 publications

Time is brain: An online controlling of traffic lights can save lives

Time is brain: An online controlling of traffic lights can save lives

A new model for standardising and treating thermal injury in the rat

Fail to Yield? An Analysis of Ambulance Crashes in Taiwan

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