IoT-Integrated Ambulance Allocation-Scheduling for Improved Emergency Response

Optimization of ambulance scheduling is a crucial factor in improving emergency responses to preserve lives. The present study introduces a novel methodology for ambulance scheduling, which incorporates the Internet of Things (IoT) to enhance resource utilization and reduce response times in emergency medical services. We have devised a dual-level program model consisting of two sub-models. The first sub-model is designed to allocate ambulances to different districts based on geographic and traffic data. The second sub-model is responsible for determining the order of visit priorities within each district, aiming to minimize the overall time required to respond to a case. Substantial enhancements in responsiveness and resource utilization were observed in comparison to traditional approaches. The present system, which is enabled by the IoT, exhibits real-time adaptability in crisis management. We present results that demonstrate the revolutionary capacity of IoT applications in Emergency Medical Services (EMS) and offer practical insights for healthcare organization managers.

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Publication Details

Published
2024-12-01
DOI
https://doi.org/10.46254/gc02.20240024
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
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article

IoT-Integrated Ambulance Allocation-Scheduling for Improved Emergency Response

Reza Kia, Ali Zahedi, Amirhossein Salehi-Amiri, Navid Akbarpour
IoT and Edge/Fog Computing
article

IoT-Integrated Ambulance Allocation-Scheduling for Improved Emergency Response

Reza Kia, Ali Zahedi, Amirhossein Salehi-Amiri, Navid Akbarpour
article en

Abstract

Optimization of ambulance scheduling is a crucial factor in improving emergency responses to preserve lives. The present study introduces a novel methodology for ambulance scheduling, which incorporates the Internet of Things (IoT) to enhance resource utilization and reduce response times in emergency medical services. We have devised a dual-level program model consisting of two sub-models. The first sub-model is designed to allocate ambulances to different districts based on geographic and traffic data. The second sub-model is responsible for determining the order of visit priorities within each district, aiming to minimize the overall time required to respond to a case. Substantial enhancements in responsiveness and resource utilization were observed in comparison to traditional approaches. The present system, which is enabled by the IoT, exhibits real-time adaptability in crisis management. We present results that demonstrate the revolutionary capacity of IoT applications in Emergency Medical Services (EMS) and offer practical insights for healthcare organization managers.

Manchester School of Architecture (GB), University of Science and Technology of Mazandaran (IR), Sultan Qaboos University (OM), Tecnológico de Monterrey (MX)
Openalex Percentile: Top 66%
IoT and Edge/Fog Computing
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