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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Probabilistic forecasting of hourly emergency department arrivals
AU - Rostami-Tabar, Bahman
AU - Browell, Jethro
AU - Svetunkov, Ivan
PY - 2023/5/1
Y1 - 2023/5/1
N2 - An accurate forecast of Emergency Department (ED) arrivals by an hour of the day is critical to meet patients’ demand. It enables planners to match ED staff to the number of arrivals, redeploy staff, and reconfigure units. In this study, we develop a model based on Generalised Additive Models and an advanced dynamic model based on exponential smoothing to generate an hourly probabilistic forecast of ED arrivals for a prediction window of 48 hours. We compare the forecast accuracy of these models against appropriate benchmarks, including TBATS, Poisson Regression, Prophet, and simple empirical distribution. We use Root Mean Squared Error to examine the point forecast accuracy and assess the forecast distribution accuracy using Quantile Bias, PinBall Score and Pinball Skill Score. Our results indicate that the proposed models outperform their benchmarks. Our developed models can also be generalised to other services, such as hospitals, ambulances or clinical desk services.
AB - An accurate forecast of Emergency Department (ED) arrivals by an hour of the day is critical to meet patients’ demand. It enables planners to match ED staff to the number of arrivals, redeploy staff, and reconfigure units. In this study, we develop a model based on Generalised Additive Models and an advanced dynamic model based on exponential smoothing to generate an hourly probabilistic forecast of ED arrivals for a prediction window of 48 hours. We compare the forecast accuracy of these models against appropriate benchmarks, including TBATS, Poisson Regression, Prophet, and simple empirical distribution. We use Root Mean Squared Error to examine the point forecast accuracy and assess the forecast distribution accuracy using Quantile Bias, PinBall Score and Pinball Skill Score. Our results indicate that the proposed models outperform their benchmarks. Our developed models can also be generalised to other services, such as hospitals, ambulances or clinical desk services.
KW - Health Informatics
KW - Health Policy
U2 - 10.1080/20476965.2023.2200526
DO - 10.1080/20476965.2023.2200526
M3 - Journal article
JO - Health Systems
JF - Health Systems
SN - 2047-6965
ER -