Patient profile and outcomes of traumatic injury: The impact of mode of arrival to the emergency department

Crilly, Julia; Bartlett, David L.; Sladdin, Ishtar; Pellatt, Richard; Young, Jesse T; Ham, Wietske H. W.; Porter, Louise

doi:10.1016/j.colegn.2022.03.002

Cited by 3 publications

(1 citation statement)

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“…While each 911 call and EMS arrival to the ED is considered an independent event, the cumulative data reveals potential patterns that can be forecasted. Accurate forecasting of ED patient arrivals, especially ambulance arrivals, plays a crucial role in effective resource allocation and delivering prompt, high-quality patient care (Mohammad 2008;Crilly et al 2022). However, despite the importance of forecasting in healthcare, there are still challenges and gaps in our understanding of ambulance arrivals and their variations across different periods or shifts.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Forecasting of Ambulance Arrivals: Insights for Shift-Based ED Resource Allocation

Larot,

Nhu Nguyen,

Yelamanchili

et al. 2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Understanding the specific timings of ambulance arrivals during different shifts presents unique opportunities for emergency department (ED) throughput optimization. This research aims to determine whether there are significant differences in ambulance arrivals across various periods (i.e., morning, afternoon, evening, and night) and to use machine learning to forecast daily emergency medical service (EMS) arrivals. We performed a cohort study of patients presenting to an urban, academic ED between January 1, 2021 and April 18, 2022. Data comprised daily patient arrivals, demographic information, mode of arrival (either by walk-in or ambulance), chief complaints, and ED disposition, focusing exclusively on patients who arrived via ambulance. A Light Gradient Boosting Machine (LGBM) model was developed to forecast daily EMS arrivals based on different periods. The findings indicate distinct ambulance arrival patterns, with higher patient influx during the night shift. The LGBM model achieved moderate to high accuracy, particularly during the evening and night periods, with mean absolute percentage errors of approximately 21% and 19%, respectively. This study provides insights into ambulance arrival patterns, emphasizing the need for specialized care across different periods. These findings can inform the development of decision support systems that leverage advanced analytics to optimize resource allocation, enhance preparedness, and improve patient outcomes in EDs and health systems.

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Section: Introductionmentioning

confidence: 99%