TR-Dizin İndeksli Yayınlar Koleksiyonu

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Browsing TR-Dizin İndeksli Yayınlar Koleksiyonu by Subject "Acil Tıp"

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    Mode of Arrival Aware Models for Forecasting Flow of Patient and Length of Stay in Emergency Departments
    (GALENOS PUBL HOUSE, 2022) Mustafa Gokalp Ataman; Gorkem Sariyer; Ataman, Mustafa Gokalp; Sariyer, Gorkem
    Aim: Flow of patients to emergency departments (EDs) and their stays in EDs (ED-LOS) depend significantly on their arrival modes. In this study developing effective models for forecasting patient flow and length of stay (LOS) in EDs by considering arrival modes led better planning of ED operations. Materials and Methods: In this study by categorizing the mode of arrival into two self-arrived in and by ambulance autoregressive integrative moving average (ARIMA) models are applied for forecasting four time series: daily number of patients self arrived/arrived by an ambulance and average LOS of patients self-arrived/arrived by an ambulance. The models are validated with real-life data received from a large-scaled urban ED in Izmir Turkey. Results: While seasonal ARIMA is proper for forecasting the daily number of patients on both modes non-seasonal models are proper for forecasting the average LOS. The mean absolute percentage errors (MAPE) for the models of four time series are 5432% 13085% 9955% and 10.984% respectively. Thus daily arrivals to the EDs show seasonality patterns. Conclusion: By emphasizing the impact of mode of arrival in ED context this study can be used to aid the strategic decision making in the EDs for capacity planning to enable efficient use of the ED resources.
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    Mode of Arrival Aware Models for Forecasting Flow of Patient and\rLength of Stay in Emergency Departments
    (2022) Mustafa Gökalp Ataman; görkem sariyer
    Aim: Flow of patients to emergency departments (EDs) and their stays in EDs (ED-LOS) depend significantly on their arrival modes. In this\rstudy developing effective models for forecasting patient flow and length of stay (LOS) in EDs by considering arrival modes led better planning\rof ED operations.\rMaterials and Methods: In this study by categorizing the mode of arrival into two self-arrived in and by ambulance autoregressive\rintegrative moving average (ARIMA) models are applied for forecasting four time series: daily number of patients self arrived/arrived by an\rambulance and average LOS of patients self-arrived/arrived by an ambulance. The models are validated with real-life data received from a\rlarge-scaled urban ED in İzmir Turkey.\rResults: While seasonal ARIMA is proper for forecasting the daily number of patients on both modes non-seasonal models are proper for\rforecasting the average LOS. The mean absolute percentage errors (MAPE) for the models of four time series are 5 432% 13 085% 9 955% and\r10.984% respectively. Thus daily arrivals to the EDs show seasonality patterns.\rConclusion: By emphasizing the impact of mode of arrival in ED context this study can be used to aid the strategic decision making in the\rEDs for capacity planning to enable efficient use of the ED resources.