Estimating future demand for hospital emergency services at the regional level

Mielczarek, Bożena

doi:10.1109/wsc.2013.6721613

Cited by 7 publications

(1 citation statement)

References 18 publications

(25 reference statements)

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“…Mielczarek [27] has studied the influence of changes in population size and structure on the volume of emergency service needs exhibited by patients arriving at hospital emergency departments in the area. He uses a Monte Carlo simulation model to examine demographic trends at the regional level, formulates forecasts for population changes, and extrapolated the simulated patterns of the demand for acute services.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Emergency Calls With a Poisson Neural Network-Based Assemble Model

et al. 2019

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Forecasting emergency calls are of great importance in practice. By forecasting the occurrence of unfortunate events, we can learn from these events and further prevent their occurrence in the future. However, because of the uncertainty of event occurrences, it is hard to guarantee their prediction accuracy. In this paper, a combined model, which consists of two parts, is proposed. The first part is a Poisson neural network model (PNN). It is responsible for basic forecasting, and its initial weights and thresholds are trained by applying a genetic algorithm. The second part consists of multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), and multivariate gray (GM), which are responsible for estimating residual errors. The basic prediction result adjusted by the residual error is used as the final forecasting result. The proposed model fully takes the advantages of PNN, MLR, ARIMA, and GM, and thus improves forecasting performance. Our method has been applied to the emergency calls of Ningbo, China. The experimental results show that the proposed model has advantages over some existing forecasting models, such as a neural network model, Poisson regression, and stochastic configuration networks in terms of mean absolute percentage error. INDEX TERMS Accident forecasting, combined model, Poisson distribution, Poisson neural network, residual model.

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Section: Related Workmentioning

confidence: 99%

Forecasting Emergency Calls With a Poisson Neural Network-Based Assemble Model

et al. 2019

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Forecasting Patient Flows into Emergency Services

Afilal¹,

Yalaoui²,

Dugardin³

2019

Hospital Logistics and E‐Management

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Forecasting the Emergency Department Patients Flow

Afilal¹,

Dugardin²,

Yalaoui³

et al. 2016

J Med Syst

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Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods.

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Estimating future demand for hospital emergency services at the regional level

Cited by 7 publications

References 18 publications

Forecasting Emergency Calls With a Poisson Neural Network-Based Assemble Model

Forecasting Emergency Calls With a Poisson Neural Network-Based Assemble Model

Forecasting Patient Flows into Emergency Services

Forecasting the Emergency Department Patients Flow

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