Forecasting and Capacity Planning in Prehospital Emergency Medical Services: The Case of Ankara
International Journal of Health Planning and Management, 2026 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1002/hpm.70116
- Dergi Adı: International Journal of Health Planning and Management
- Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, CINAHL, EMBASE, Geobase, MEDLINE, Natural Science Collection (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Business Source Ultimate (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: ambulances, emergency medical services, forecasting, machine learning, prehospital emergency care
- Ankara Üniversitesi Adresli: Evet
Özet
Objective: Time savings in prehospital emergency medical services are vital for human life. Ensuring that these services can meet potential demands is essential for successful delivery. This study aimed to forecast the demand for ambulance services within prehospital emergency medical services and to plan the capacity of ambulance stations. Methods: In this descriptive, cross-sectional, and analytical study, the population consists of 943,412 cases directed by 139 ambulance stations in the capital of Türkiye. The forecasting was carried out at the district level with a particular focus on seven districts. Various forecasting methods such as Holt-Winters, ARIMA, MLP, NNAR, and LSTM were employed. The optimal number of ambulances required at various stations to meet response time targets was identified using Monte Carlo simulations to account for uncertainties in call volumes and response times. Results: A total of 943,412 calls were directed to ambulances. The annual distributions showed slight increases each year. Forecasting performance of various methods was evaluated based on RMSE, MAE, and MAPE metrics, with the ARIMA and NNAR models generally showing the best performance. The study also identified the optimal number of ambulances required at various stations to meet response time targets. Conclusion: The study underscores the importance of accurate demand forecasting and capacity planning for ambulance services in ensuring timely and effective prehospital emergency medical services. By addressing critical response time targets and optimising ambulance capacity, the findings contribute to improving the quality and accessibility of emergency health services, ultimately enhancing human life and health outcomes.