دوره 10، شماره 4 - ( 4-1404 )
جلد 10 شماره 4 صفحات 258-247 |
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Babanezhad M, Khorsha H, Mohajervatan A, Choori A. Estimating the Demand for Ambulances in Traffic Accidents. Health in Emergencies and Disasters Quarterly 2025; 10 (4) :247-258
URL: http://hdq.uswr.ac.ir/article-1-641-fa.html
URL: http://hdq.uswr.ac.ir/article-1-641-fa.html
Estimating the Demand for Ambulances in Traffic Accidents. فصلنامه سلامت در حوادث و بلایا. 1404; 10 (4) :247-258
چکیده: (254 مشاهده)
Background: Effective Emergency medical service (EMS) delivery in road traffic accidents requires accurate resource planning that relies on operational, tactical and strategic demand forecasts. This study aims to estimate the demand for ambulances in traffic accidents using time series modeling techniques.
Materials and Methods: We conducted a retrospective cohort analysis of ambulance demands related to traffic incidents in Golestan Province, Iran. The analysis of individual time series was utilized for demand prediction. Then, we applied statistical methods to present the performance indicators.
Results: This research examined 37409 calls that led to ambulance dispatch from March 2021 to March 2023. According to the examination of traffic collision data, the demand rate is greater during the daytime compared to nighttime. Nonetheless, ambulance responses to deadly accidents take place more frequently at night compared to daytime. Our analysis indicates that demand will vary between 2400 and 800 with a 90% confidence level. Additionally, at an 80% confidence level, the demand range is expected to be between 300 and 2800.
Conclusion: By analyzing the historical data, we have identified a trend and seasonal patterns in the data, which suggests an increase in demand during the summer months. Forecasting the course of service recipients in the prehospital emergency service can increase situational awareness and help manage the challenges caused by overcrowding. By anticipating the surge in demand for services during peak periods, it is possible to plan and allocate resources effectively and minimize delays.
Materials and Methods: We conducted a retrospective cohort analysis of ambulance demands related to traffic incidents in Golestan Province, Iran. The analysis of individual time series was utilized for demand prediction. Then, we applied statistical methods to present the performance indicators.
Results: This research examined 37409 calls that led to ambulance dispatch from March 2021 to March 2023. According to the examination of traffic collision data, the demand rate is greater during the daytime compared to nighttime. Nonetheless, ambulance responses to deadly accidents take place more frequently at night compared to daytime. Our analysis indicates that demand will vary between 2400 and 800 with a 90% confidence level. Additionally, at an 80% confidence level, the demand range is expected to be between 300 and 2800.
Conclusion: By analyzing the historical data, we have identified a trend and seasonal patterns in the data, which suggests an increase in demand during the summer months. Forecasting the course of service recipients in the prehospital emergency service can increase situational awareness and help manage the challenges caused by overcrowding. By anticipating the surge in demand for services during peak periods, it is possible to plan and allocate resources effectively and minimize delays.
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