Ambulance response time in a Brazilian emergency medical service

Colla, Marcos Schimit; Santos, Gilson Ditzel; Oliveira, Gilson Adamczuk; Vasconcelos, Renata Braga Berenguer de

doi:10.1016/j.seps.2022.101434

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Delay in hospitalization is the most frequent and substantial cause of fatality in traffic accidents [1,[5][6][7]. The injured may pass away before they reach the hospital if there is even a one-minute delay.…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

Noor,

Algrafi,

Alharbi

et al. 2024

Applied Sciences

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Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.

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Section: Introductionmentioning

confidence: 99%

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

Noor,

Algrafi,

Alharbi

et al. 2024

Applied Sciences

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“…Here, the rising population prompts concern among public managers regarding their ability to meet the demand for pre-hospital emergency care. EMS has recently operated through a single centralised telephonic contact in low-income countries such as Bangladesh, Cape Verde, Colombia, and the Dominican Republic 2 . A cross-country comparison revealed lower ambulance response times in Asia (7.3 min), Oceania (8 min), while higher in Ghana (19 min) and Brazil (21–27 min) 3 .…”

Section: Introductionmentioning

confidence: 99%

Examining district-level disparity and determinants of timeliness of emergency medical services in Maharashtra, India

Jana,

Sarkar,

Parmar

et al. 2023

Sci Rep

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The quality of emergency medical services remains a major public health issue in developing countries in terms of access, availability, or timely delivery, owing to high socio-economic and ethnic disparities. Particularly, the timeliness of EMS remains a drawback, leading to higher mortality and morbidity. The aim of the study is to assess the district-level differences and factors that influence ambulance travel time, as there was no study done in the Indian scenario. Sequential Explanatory Design was applied here, which involved a descriptive study and spatial analysis of the call volume and distribution to understand the operational challenges of MEMS, followed by in-depth interviews among medical officers and officials to explore the reasons for the challenges. The data, shared by the Department of Health, Government of Maharashtra, consisted of 38,823 records (emergency: 16,197 and hospital-to-hospital transfer: 22,626), including emergency and hospital-to-hospital transfer calls across 36 districts of Maharashtra for November 2022. Spatial analyses were performed to identify the districts with challenges of timeliness. The average ambulance response time (T) across the districts was reported at 134.5 min for emergency cases and 222.80 min for hospital-to-hospital transfer cases. The total ambulance response time, was classified as preparation time (t1:3.53 min for emergency, 3.69 min for hospital-to-hospital transfer), travel time from base to scene (t2: 23.15 min for emergency, 17.18 min for hospital-to-hospital transfer), time required at scene (t3: 12.12 min for emergency, 14.72 min for hospital-to-hospital transfer), travel time from scene to hospital (t4:39.41 min for emergency, 74.34 min for hospital-to-hospital transfer), patient handover time (t5: 10.85 min for emergency, 13.84 min for hospital-to-hospital transfer), and return from base to hospital (t6: 41.89 min for emergency, 94.72 min for hospital-to-hospital transfer). Multivariate linear regression was conducted to investigate the factors that influence ambulance travel time. The finding identifies that the ambulance travel time increased for the districts with lesser population density, lower road density, fewer hospitals, a higher district area served per ambulance, and a higher population served per ambulance. Additionally, socio-cultural reasons affecting health-seeking behaviour, early closing of healthcare centres, undercapacity and resource-deficit healthcare centres, and overloading of specialised tertiary hospitals were identified as determinants of delay in patient assessment and handover time in qualitative findings. A decisive and multi-sectoral approach is required to address the timeliness of EMS in the Indian context.

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An effective speaker adaption using deep learning for the identification of speakers in emergency situation

Deka,

Kalita

2024

Multimed Tools Appl

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Ambulance response time in a Brazilian emergency medical service

Cited by 5 publications

References 37 publications

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

Examining district-level disparity and determinants of timeliness of emergency medical services in Maharashtra, India

An effective speaker adaption using deep learning for the identification of speakers in emergency situation

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