IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v27y2024i3d10.1007_s10729-024-09678-3.html
   My bibliography  Save this article

Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel

Author

Listed:
  • Marco Boresta

    (Institute for System Analysis and Computer Science “A. Ruberti”, National Research Council of Italy)

  • Tommaso Giovannelli

    (Lehigh University)

  • Massimo Roma

    (SAPIENZA – University of Rome)

Abstract

This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.

Suggested Citation

  • Marco Boresta & Tommaso Giovannelli & Massimo Roma, 2024. "Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel," Health Care Management Science, Springer, vol. 27(3), pages 415-435, September.
    • Handle: RePEc:kap:hcarem:v:27:y:2024:i:3:d:10.1007_s10729-024-09678-3
    DOI: 10.1007/s10729-024-09678-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-024-09678-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10729-024-09678-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    2. Hainan Guo & David Goldsman & Kwok-Leung Tsui & Yu Zhou & Shui-Yee Wong, 2016. "Using simulation and optimisation to characterise durations of emergency department service times with incomplete data," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6494-6511, November.
    3. G. Liuzzi & S. Lucidi & F. Rinaldi, 2012. "Derivative-free methods for bound constrained mixed-integer optimization," Computational Optimization and Applications, Springer, vol. 53(2), pages 505-526, October.
    4. Guilherme F. Coelho & Luiz R. Pinto, 2018. "Kriging-based simulation optimization: An emergency medical system application," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(12), pages 2006-2020, December.
    5. Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2015. "Derivative-Free Methods for Mixed-Integer Constrained Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 164(3), pages 933-965, March.
    6. Yen-Yi Feng & I-Chin Wu & Tzu-Li Chen, 2017. "Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm," Health Care Management Science, Springer, vol. 20(1), pages 55-75, March.
    7. Alberto Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2023. "A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department," Annals of Operations Research, Springer, vol. 320(2), pages 727-756, January.
    8. 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.
    9. Tommaso Giovannelli & Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2022. "Derivative-free methods for mixed-integer nonsmooth constrained optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 293-327, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hainan Guo & Haobin Gu & Yu Zhou & Jiaxuan Peng, 2022. "A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 238-262, June.
    2. Alberto De Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2020. "An optimal non-uniform piecewise constant approximation for the patient arrival rate for a more efficient representation of the Emergency Departments arrival process," DIAG Technical Reports 2020-01, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    3. Zhou, Liping & Geng, Na & Jiang, Zhibin & Wang, Xiuxian, 2018. "Multi-objective capacity allocation of hospital wards combining revenue and equity," Omega, Elsevier, vol. 81(C), pages 220-233.
    4. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
    5. Nikolaos Ploskas & Nikolaos V. Sahinidis, 2022. "Review and comparison of algorithms and software for mixed-integer derivative-free optimization," Journal of Global Optimization, Springer, vol. 82(3), pages 433-462, March.
    6. Alberto Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2023. "A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department," Annals of Operations Research, Springer, vol. 320(2), pages 727-756, January.
    7. Alberto De Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2022. "Determining the optimal piecewise constant approximation for the nonhomogeneous Poisson process rate of Emergency Department patient arrivals," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 979-1012, December.
    8. Noordhoek, Marije & Dullaert, Wout & Lai, David S.W. & de Leeuw, Sander, 2018. "A simulation–optimization approach for a service-constrained multi-echelon distribution network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 292-311.
    9. Ubaldo M. García Palomares, 2023. "Convergence of derivative-free nonmonotone Direct Search Methods for unconstrained and box-constrained mixed-integer optimization," Computational Optimization and Applications, Springer, vol. 85(3), pages 821-856, July.
    10. Roberto Aringhieri & Giuliana Carello & Daniela Morale, 2016. "Supporting decision making to improve the performance of an Italian Emergency Medical Service," Annals of Operations Research, Springer, vol. 236(1), pages 131-148, January.
    11. Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
    12. Ruoyan Sun & David Mendez, 2019. "Finding the optimal mix of smoking initiation and cessation interventions to reduce smoking prevalence," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-12, March.
    13. Warren B. Powell, 2009. "What you should know about approximate dynamic programming," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(3), pages 239-249, April.
    14. Miguel Angel Ortíz-Barrios & Dayana Milena Coba-Blanco & Juan-José Alfaro-Saíz & Daniela Stand-González, 2021. "Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review," IJERPH, MDPI, vol. 18(16), pages 1-31, August.
    15. Jack P. C. Kleijnen & Susan M. Sanchez & Thomas W. Lucas & Thomas M. Cioppa, 2005. "State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 263-289, August.
    16. Tommaso Giovannelli & Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2022. "Derivative-free methods for mixed-integer nonsmooth constrained optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 293-327, June.
    17. Sigrún Andradóttir & Andrei A. Prudius, 2009. "Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 193-208, May.
    18. Sanjay Mehrotra & Hamed Rahimian & Masoud Barah & Fengqiao Luo & Karolina Schantz, 2020. "A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(5), pages 303-320, August.
    19. Stefan Vonolfen & Michael Affenzeller, 2016. "Distribution of waiting time for dynamic pickup and delivery problems," Annals of Operations Research, Springer, vol. 236(2), pages 359-382, January.
    20. Shahab Sadri & Arsalan Paleshi & Lihui Bai & Monica Gentili, 2024. "A Simulation Study for a Safe Reopening and Operation of the Trager Institute Optimal Aging Clinic During the COVID-19 Pandemic," Interfaces, INFORMS, vol. 54(2), pages 188-204, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:hcarem:v:27:y:2024:i:3:d:10.1007_s10729-024-09678-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.