A deep learning architecture for forecasting daily emergency department visits with acuity levels

Zhao, Xinxing; Li, Kainan; Ang, Candice Ke En; Ho, Andrew Fu Wah; Liu, Nan; Ong, Marcus Eng Hock; Cheong, Kang Hao

doi:10.1016/j.chaos.2022.112777

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…Modeling crowding has been a major challenge in disciplines such as operations research, marketing, transportation research, psychology, and healthcare management [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…ED crowding has been intensively studied [10][11][12][13]15,16,[18][19][20][21][22]28,29], exploring a variety of operational metrics [27], and using variety of modeling techniques (see Hu, Barnes and Golden 2018 [20], Vanbrabant et al 2019 [27], and Sinreich and Marmor 2005 [25] for review). Our model offers a fourfold contribution: First, it captures average behavior, as well as spikes and events of overcrowding, which received less research attention [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating emergency department crowding with stochastic population models

Parnass,

Levtzion-Korach,

Peres

et al. 2023

PLoS ONE

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Environments such as shopping malls, airports, or hospital emergency-departments often experience crowding, with many people simultaneously requesting service. Crowding highly fluctuates, with sudden overcrowding "spikes". Past research has either focused on average behavior, used context-specific models with a large number of parameters, or machine-learning models that are hard to interpret. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding. We test the model using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) cuts the probability of severe overcrowding events by 50%. Such forecasting is critical in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many systems in nature.

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