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Journals / Eurasian Journal of Emergency Medicine / 2022 / Cilt: 21 - Sayı: 1

Mode of Arrival Aware Models for Forecasting Flow of Patient and Length of Stay in Emergency Departments

Mustafa Gökalp ATAMAN
Görkem SARIYER
Pages
34–44
Publication Date
2022-10-11
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: Flow of patients to emergency departments (EDs) and their stays in EDs (ED-LOS) depend significantly on their arrival modes.
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Key Findings & Data:The mean absolute percentage errors (MAPE) for the models of four time series are 5,432%, 13,085%, 9,955% and 10.984%, respectively.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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 İzmir, 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 5,432%, 13,085%, 9,955% 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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Cite This Article

ATAMAN, M. G., SARIYER, G. (2022). Mode of Arrival Aware Models for Forecasting Flow of Patient and Length of Stay in Emergency Departments. Eurasian Journal of Emergency Medicine, Cilt: 21 - Sayı: 1, 34–44. https://ajindex.com/articles/507551

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