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已发表论文
中国城市自动体外除颤器（AED）配置的分析与优化：以北京市东城区为例
Received 15 May 2025
Accepted for publication 27 September 2025
Published 12 October 2025 Volume 2025:18 Pages 3281—3295
DOI https://doi.org/10.2147/RMHP.S502335
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Gulsum Kaya
Yunke Shi,1,2 Ning Zhang1
1School of Public Health, Capital Medical University, Beijing, People’s Republic of China; 2Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
Correspondence: Ning Zhang, School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing, 100069, People’s Republic of China, Email cufestat@163.com
Purpose: To explore strategies for optimizing Automated External Defibrillator (AED) configuration in urban areas of China and improving the treatment conditions for out-of-hospital cardiac arrest (OHCA) patients.
Material and Methods: Taking Dongcheng District, Beijing as the research object, spatial data such as administrative divisions, transportation road networks, AED configuration points, and points of interest in key public places, as well as non-spatial data such as population statistics, were collected. Service area analysis and location-allocation models were used to analyze the current status of AED configuration and explore optimization strategies for AED deployment.
Results: As of September 2024, a total of 86 AEDs had been configured in Dongcheng District, and their service area covered 13.74% of the district. The combined service area of AEDs and hospitals covered 82.62% of the district. After achieving the goal of full AED coverage in key public places in the future, AED service area will cover 34.80% of the district, and the combined service area of AEDs and hospitals will cover 85.92% of the district. According to the optimization plan proposed in this study, an additional 218 AEDs are needed in Dongcheng District, bringing the total number of AEDs to 519. At this point, AED service area will cover 50.62% of the district, and the combined service area of AEDs and hospitals will cover 97.28%.
Conclusion: The AED configuration optimization strategy proposed in this study is highly reasonable, and relevant government agencies can refer to this framework to optimize the AED deployment in urban areas. Additionally, technologies such as the Internet of Things and drones can be leveraged to establish urban AED search and delivery platforms, further enhancing the accessibility and utilization rate of AED to achieve optimal treatment outcomes for OHCA patients and save more lives.
Keywords: AED, ArcGIS, configuration optimization, service area analysis, location-allocation model
1School of Public Health, Capital Medical University, Beijing, People’s Republic of China; 2Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
Correspondence: Ning Zhang, School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing, 100069, People’s Republic of China, Email cufestat@163.com
Purpose: To explore strategies for optimizing Automated External Defibrillator (AED) configuration in urban areas of China and improving the treatment conditions for out-of-hospital cardiac arrest (OHCA) patients.
Material and Methods: Taking Dongcheng District, Beijing as the research object, spatial data such as administrative divisions, transportation road networks, AED configuration points, and points of interest in key public places, as well as non-spatial data such as population statistics, were collected. Service area analysis and location-allocation models were used to analyze the current status of AED configuration and explore optimization strategies for AED deployment.
Results: As of September 2024, a total of 86 AEDs had been configured in Dongcheng District, and their service area covered 13.74% of the district. The combined service area of AEDs and hospitals covered 82.62% of the district. After achieving the goal of full AED coverage in key public places in the future, AED service area will cover 34.80% of the district, and the combined service area of AEDs and hospitals will cover 85.92% of the district. According to the optimization plan proposed in this study, an additional 218 AEDs are needed in Dongcheng District, bringing the total number of AEDs to 519. At this point, AED service area will cover 50.62% of the district, and the combined service area of AEDs and hospitals will cover 97.28%.
Conclusion: The AED configuration optimization strategy proposed in this study is highly reasonable, and relevant government agencies can refer to this framework to optimize the AED deployment in urban areas. Additionally, technologies such as the Internet of Things and drones can be leveraged to establish urban AED search and delivery platforms, further enhancing the accessibility and utilization rate of AED to achieve optimal treatment outcomes for OHCA patients and save more lives.
Keywords: AED, ArcGIS, configuration optimization, service area analysis, location-allocation model