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Demand Forecast Using Data Analytics for the Preallocation of Ambulances.

Albert Y Chen, Tsung-Yu Lu, Matthew Huei-Ming Ma

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    This study uses geographic information systems (GIS) to forecast prehospital emergency medical services (EMS) demand, improving ambulance allocation. The developed model achieved a reasonable 23.01% daily forecast error, enhancing operational efficiency and patient survival rates.

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    Area of Science:

    • Operations Research
    • Public Health
    • Geographic Information Science

    Background:

    • Prehospital emergency medical services (EMS) aim to minimize response times for improved patient survival.
    • Operational efficiency in EMS is crucial for potentially increasing patient survival rates.
    • Geographic Information Systems (GIS) offer tools for managing and visualizing spatial data, applicable to EMS demand.

    Purpose of the Study:

    • To develop and evaluate a flexible GIS-based model for forecasting prehospital emergency medical demand.
    • To compare the performance of various forecasting methods including moving average, artificial neural network, sinusoidal regression, and support vector regression.
    • To assess the model's applicability for preallocating ambulances and improving EMS operational efficiency.

    Main Methods:

    • Implementation of a flexible GIS model allowing user-defined spatial grid and temporal step sizes for training data.
    • Application of four forecasting techniques: moving average, artificial neural network, sinusoidal regression, and support vector regression.
    • A case study in New Taipei City utilizing three years of prehospital EMS data, with model selection tailored to different areas and input features.

    Main Results:

    • The study achieved a best daily mean absolute percentage error of 23.01% for EMS demand forecasting during testing.
    • Different forecasting models and input features were selected for different areas, indicating a customized approach.
    • The forecast performance was deemed reasonable according to Lewis' definition, demonstrating the model's predictive capability.

    Conclusions:

    • The proposed GIS-based forecasting approach demonstrates acceptable prediction performance for prehospital emergency medical demand.
    • The model has the potential for practical application in current EMS practices, particularly for ambulance preallocation.
    • Improved forecasting accuracy can contribute to enhanced operational efficiency and potentially better patient outcomes in emergency medical services.