Combined forecasting of patient arrivals and doctor rostering simulation modelling for hospital emergency department

Lin, W. D.; Chia, Linda

doi:10.1109/ieem.2017.8290320

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…Indeed, Mousavi Isfahani et al, (2019), Habidin et al, (2015), and Ahmed et al, (2013) reviewed the literature related to LSS applications in EDs and concluded that this method has significantly helped ED managers to reduce costs and prevent wastes of time. Specifically, Furterer reported significant reductions in waiting times as well as increased patient satisfaction in an ED after a 3-month project (Furterer, 2018 (Gartner and Padman, 2019), ARIMA (Lin and Chia, 2018), Six sigma (Hussein et al, 2017;Mandahawi et al, 2017), and Data Envelopment Analysis (Aminuddin and Ismail, 2016) to provide more robust results and cover aspects that have not been considered in previous studies (e.g. identification of significant factors, demand forecasting, optimization of resources, etc.).…”

Section: Emergency Care Network: Related Studiesmentioning

confidence: 99%

Redesigning the Barranquilla's public emergency care network to improve the patient waiting time

Barrios¹,

Ángel²

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Waiting time is one of the most critical measures in the satisfaction of patients admitted within emergency departments. Therefore, hospitals and governmental organizations should jointly aim to provide timely attention at reasonable costs. In the case of Barranquilla's Pubic Emergency Service Network, composed by 8 Points of care (POCs) and 2 hospitals, the trend evidences a continuous growing of the waiting time with a rate of 3,08 min/semester and a 93,13% likelihood of serving patients after waiting for more than 30 minutes. This is an unmistakable symptom of 12 average with a standard deviation of 81,6 min before being served by the emergency care unit. On the other hand, based on the reported literature, it is highly suggested to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for addressing this problem. In this sense, a methodology based on collateral payment models, Discrete-event simulation, and Lean Six Sigma was proposed and validated resulting in a redesigned network whose average waiting time may diminish between 6,71 min and 9,08 min with an average profit US$29,980/node. Lately, a model comprising of 8 criteria and 35 sub-criteria was designed for evaluating the overall performance of emergency departments. The model outcomes revealed the critical role of Infrastructure (Global weight = 21,5%) in ED performance and the interactive nature of Patient Safety (C + R = 12,771).

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