Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado, Dionicio; Escobar, John Willmer; McClean, Sally

doi:10.3390/ijgi11020109

Cited by 17 publications

(8 citation statements)

References 153 publications

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“…OR technologies can be used to analyze historical data on demand for EMS services in different locations to determine the optimal number of ambulances to deploy to each area. This can help to prevent resource waste and ensure that there are enough ambulances available to meet demand [12,13].…”

Section: Determine the Optimal Number Of Ambulances To Deploy To A Ce...mentioning

confidence: 99%

Addressing Challenges in EMS Department Operations: A Comprehensive Analysis of Key Issues and Solution

Basnawi

2023

Emergency Care and Medicine

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Background: Emergency medical services (EMS) are essential in providing timely medical attention and transportation to people in need during times of crisis. Effective EMS operations are crucial for delivering prompt and effective healthcare. However, the landscape of EMS operations is constantly evolving, posing a number of challenges that require rigorous research and innovative solutions. Objectives: To highlight the key challenges facing EMS departments in their daily operations and discuss potential solutions. Methods: A narrative literature review was conducted. Relevant studies were identified by searching electronic databases, such as PubMed, MEDLINE, CINAHL, and Google Scholar. Results: The main challenges facing EMS departments include an increasing demand for services, limited resources, aging infrastructure, technological advancements, and regulatory compliance. Operational-level solutions that can be used to address these challenges include investing in technology, cross-training personnel, developing contingency plans, and partnering with other organizations. Conclusion: EMS departments today face a variety of challenges. However, there are a number of approaches that can be taken to address these challenges. By investing in technology, cross-training personnel, developing contingency plans, and partnering with other organizations, EMS departments can improve their operations and provide the best possible care to patients.

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Section: Determine the Optimal Number Of Ambulances To Deploy To A Ce...mentioning

confidence: 99%

Addressing Challenges in EMS Department Operations: A Comprehensive Analysis of Key Issues and Solution

Basnawi

2023

Emergency Care and Medicine

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“…However, all the cases described view EMS location as a static problem, meaning EMVs have immovable location sites and fixed numbers across the timeline. The perceived quality of EMSs would be degraded as a result [1,19].…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Data-Driven Multiperiod Relocation Optimization of Emergency Medical Services: Maximum Equality Objective

Zhou

Chen

et al. 2023

IJGI

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As a kind of first aid healthcare service, emergency medical services (EMSs) present high spatiotemporal sensitivity due to significant changes in the time-dependent urban environment. Taking full advantage of big spatiotemporal data to realize multiperiod relocation optimization of EMSs can reduce idle resources and improve service utilization efficiency and fairness. First, we established the dynamic time-dependent accessibility and equality model to formulate the multiperiod maximization objective of global equality. Second, we proposed a capacitated integer evolution algorithm that relocates emergency medical vehicles to optimize the scheduling scheme. Based on multiperiod mobile phone records and multiperiod online route planner data, the equality of EMSs in the research metropolis, Nanjing, China, rose by 41.5% on average, which has an incentivizing effect on alleviating the tension of prehospital service and minimizes accessibility disparities without constructing more infrastructure. We also created maps to visualize the changes in equality patterns over time. This relocation optimization approach can be regarded as a trade-off approach to dispatch time-dependent sensitive services and provide a practical tool for healthcare decision-makers to evaluate public healthcare systems and improve strategic urban service planning.

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“…The proper detection of ambulance vehicles will help emergency services to keep up with evolving emergency calls [16,17]. To develop a proper ambulance vehicle detection system that can detect ambulances and distinguish them from other objects, the work is heavily dependent on data collection and training the model to detect the required objects through the camera [1,[18][19][20].…”

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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Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Cited by 17 publications

References 153 publications

Addressing Challenges in EMS Department Operations: A Comprehensive Analysis of Key Issues and Solution

Addressing Challenges in EMS Department Operations: A Comprehensive Analysis of Key Issues and Solution

Spatiotemporal Data-Driven Multiperiod Relocation Optimization of Emergency Medical Services: Maximum Equality Objective

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

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