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Estimating emergency department crowding with stochastic population models

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  • Gil Parnass
  • Osnat Levtzion-Korach
  • Renana Peres
  • Michael Assaf

Abstract

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.

Suggested Citation

  • Gil Parnass & Osnat Levtzion-Korach & Renana Peres & Michael Assaf, 2023. "Estimating emergency department crowding with stochastic population models," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0295130
    DOI: 10.1371/journal.pone.0295130
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    References listed on IDEAS

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    1. Carri W. Chan & Vivek F. Farias & Nicholas Bambos & Gabriel J. Escobar, 2012. "Optimizing Intensive Care Unit Discharge Decisions with Patient Readmissions," Operations Research, INFORMS, vol. 60(6), pages 1323-1341, December.
    2. Cats, Oded & West, Jens & Eliasson, Jonas, 2016. "A dynamic stochastic model for evaluating congestion and crowding effects in transit systems," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 43-57.
    3. Eroglu, Sevgin A. & Machleit, Karen & Barr, Terri Feldman, 2005. "Perceived retail crowding and shopping satisfaction: the role of shopping values," Journal of Business Research, Elsevier, vol. 58(8), pages 1146-1153, August.
    4. Jillian A. Berry Jaeker & Anita L. Tucker, 2017. "Past the Point of Speeding Up: The Negative Effects of Workload Saturation on Efficiency and Patient Severity," Management Science, INFORMS, vol. 63(4), pages 1042-1062, April.
    5. Abo-Hamad, Waleed & Arisha, Amr, 2013. "Simulation-based framework to improve patient experience in an emergency department," European Journal of Operational Research, Elsevier, vol. 224(1), pages 154-166.
    6. Abhyudai Singh & Mohammad Soltani, 2013. "Quantifying Intrinsic and Extrinsic Variability in Stochastic Gene Expression Models," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    7. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Ho, Andrew Fu Wah & Liu, Nan & Ong, Marcus Eng Hock & Cheong, Kang Hao, 2022. "A deep learning architecture for forecasting daily emergency department visits with acuity levels," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    8. Michelle Andrews & Xueming Luo & Zheng Fang & Anindya Ghose, 2016. "Mobile Ad Effectiveness: Hyper-Contextual Targeting with Crowdedness," Marketing Science, INFORMS, vol. 35(2), pages 218-233, March.
    9. Yong-Hong Kuo & Omar Rado & Benedetta Lupia & Janny M. Y. Leung & Colin A. Graham, 2016. "Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 120-147, June.
    10. Ziyou Gao & Yunchao Qu & Xingang Li & Jiancheng Long & Hai-Jun Huang, 2014. "Simulating the Dynamic Escape Process in Large Public Places," Operations Research, INFORMS, vol. 62(6), pages 1344-1357, December.
    11. Knut Haase & Mathias Kasper & Matthes Koch & Sven Müller, 2019. "A Pilgrim Scheduling Approach to Increase Safety During the Hajj," Operations Research, INFORMS, vol. 67(2), pages 376-406, March.
    12. Robert J. Batt & Christian Terwiesch, 2015. "Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department," Management Science, INFORMS, vol. 61(1), pages 39-59, January.
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