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A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong

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Abstract

Overcrowding at emergency departments in Hong Kong has been a critical issue for hospital managers recently. In this study, we focus on optimizing the medical staff configuration to alleviate overcrowding. According to the service requirements proposed by the Hong Kong government, 90% of urgent patients should receive treatment within 30 min. However, this condition is rarely satisfied in the practical situation. Therefore, we formulate the problem as minimizing the proportion of urgent patients that violate the service requirements while satisfying the service requirements of the other categories and cost constraints, thereby resulting in an optimization problem with a stochastic objective and several stochastic constraints. To solve this problem efficiently, we proposed a multi-fidelity simulation optimization framework containing a low- and a high-fidelity process. We utilize an evolutionary algorithm with violation-constrained handling assisted by a surrogate model as a low-fidelity process to shrink the solution space and generate an elite population. In the high-fidelity process, we exploit the optimal computing budget allocation method to identify the best solution in the elite population based on a data-driven simulation model. A case study is also discussed, and the results demonstrated that with a limited labor cost, there is a 52.05% reduction on average in the waiting time of urgent patients. Meanwhile, our proposed multi-fidelity simulation optimization framework proves to save 98.4% of the simulation time.

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References

  • (2017) Key statistics on service demand of ed in public hospitals. https://today.mims.com/can-rat-help-overcapacity-in-hong-kong-public-hospitals

  • (2019) Hong kong hospital authority. http://www.ha.org.hk/visitor/ha_visitor_index.asp?Content_ID=10051&Lang=ENG&Dimension=100&Parent_ID=10042&Ver=HTML

  • Abo-Hamad W, Arisha A (2013) Simulation-based framework to improve patient experience in an emergency department. Eur J Oper Res 224(1):154–166

    Article  Google Scholar 

  • Ahmed MA, Alkhamis TM (2009) Simulation optimization for an emergency department healthcare unit in kuwait. Eur J Oper Res 198(3):936–942

    Article  Google Scholar 

  • Bertsimas D, Gupta V, Kallus N (2018) Data-driven robust optimization. Math Program 167(2):235–292

    Article  MathSciNet  Google Scholar 

  • Chen CH, Lin J, Yücesan E, Chick SE (2000) Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dyn Syst 10(3):251–270

    Article  MathSciNet  Google Scholar 

  • Chen W, Guo H, Tsui KL (2020) A new medical staff allocation via simulation optimisation for an emergency department in Hong Kong. Int J Prod Res 58:(19):6004–6023

    Article  Google Scholar 

  • England W, Roberts SD (1978) Applications of computer simulation in health care. In: Proceedings of the 10th conference on winter simulation-volume 2, IEEE Computer Society Press, pp 665–677

  • Erhard M, Schoenfelder J, Fügener A, Brunner JO (2018) State of the art in physician scheduling. Eur J Oper Res 265(1):1–18

    Article  MathSciNet  Google Scholar 

  • Feng YY, Wu IC, Chen TL (2017) Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm. Healthc Manag Sci 20(1):55–75

    Article  Google Scholar 

  • Ghanes K, Wargon M, Jouini O, Jemai Z, Diakogiannis A, Hellmann R, Thomas V, Koole G (2015) Simulation-based optimization of staffing levels in an emergency department. Simulation 91(10):942–953

    Article  Google Scholar 

  • Gul M, Guneri AF (2015) A comprehensive review of emergency department simulation applications for normal and disaster conditions. Comput Ind Eng 83:327–344

    Article  Google Scholar 

  • Guo H, Goldsman D, Tsui KL, Zhou Y, Wong SY (2016) Using simulation and optimisation to characterise durations of emergency department service times with incomplete data. Int J Prod Res 54(21):6494–6511

    Article  Google Scholar 

  • Guo H, Gao S, Tsui KL, Niu T (2017) Simulation optimization for medical staff configuration at emergency department in Hong Kong. IEEE Trans Autom Sci Eng 14(4):1655–1665

    Article  Google Scholar 

  • Jean P, Adrian I, Adam C, Rafic Y (2016) A constraint-handling technique for genetic algorithms using a violation factor. J Comput Sci 12:350–362

    Article  Google Scholar 

  • Kleijnen JP, Van Beers W, Van Nieuwenhuyse I (2010) Constrained optimization in expensive simulation: novel approach. Eur J Oper Res 202(1):164–174

    Article  Google Scholar 

  • Kuo YH, Rado O, Lupia B, Leung JM, Graham CA (2016) Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions. Flex Serv Manuf J 28(1–2):120–147

    Article  Google Scholar 

  • Lee LH, Chew EP, Teng S, Goldsman D (2010) Finding the non-dominated pareto set for multi-objective simulation models. IIE Trans 42(9):656–674

    Article  Google Scholar 

  • Lee LH, Pujowidianto NA, Li LW, Chen CH, Yap CM (2012) Approximate simulation budget allocation for selecting the best design in the presence of stochastic constraints. IEEE Trans Autom Control 57(11):2940–2945

    Article  MathSciNet  Google Scholar 

  • Lin RC, Sir MY, Pasupathy KS (2013) Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: specific application to determining optimal resource levels in surgical services. Omega 41(5):881–892

    Article  Google Scholar 

  • Lin Y, Luo H, Wang D, Guo H, Zhu K (2017) An ensemble model based on machine learning methods and data preprocessing for short-term electric load forecasting. Energies 10(8):1186 –

    Article  Google Scholar 

  • Liu H, Tian H, Li Y (2012) Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl Energy 98:415–424

    Article  Google Scholar 

  • Liu R, Xie X (2018) Physician staffing for emergency departments with time-varying demand. INFORMS J Comput 30(3):588–607

    Article  MathSciNet  Google Scholar 

  • Marcilio I, Hajat S, Gouveia N (2013) Forecasting daily emergency department visits using calendar variables and ambient temperature readings. Acad Emerg Med 20(8):769–777

    Article  Google Scholar 

  • Park C, Kim SH (2015) Penalty function with memory for discrete optimization via simulation with stochastic constraints. Oper Res 63(5):1195–1212

    Article  MathSciNet  Google Scholar 

  • Paul SA, Reddy MC, DeFlitch CJ (2010) A systematic review of simulation studies investigating emergency department overcrowding. Simulation 86(8–9):559–571

    Article  Google Scholar 

  • Peng Y, Xu J, Lee LH, Hu JQ, Chen CH (2018) Efficient simulation sampling allocation using multi-fidelity models. IEEE Trans Autom Control 64(8):3156–3169

    Article  Google Scholar 

  • Prudêncio RB, De Souto MC, Ludermir TB (2011) Selecting machine learning algorithms using the ranking meta-learning approach. In: Meta-learning in computational intelligence. Springer, pp 225–243

  • Qiu Y, Song J, Liu Z (2016) A simulation optimisation on the hierarchical health care delivery system patient flow based on multi-fidelity models. Int J Prod Res 54(21):6478–6493

    Article  Google Scholar 

  • Rath S, Rajaram K, Mahajan A (2017) Integrated anesthesiologist and room scheduling for surgeries: methodology and application. Oper Res 65(6):1460–1478

    Article  MathSciNet  Google Scholar 

  • Rossetti MD, Trzcinski GF, Syverud SA (1999) Emergency department simulation and determination of optimal attending physician staffing schedules. In: Proceedings of the 31st conference on Winter simulation: simulation—a bridge to the future-volume 2. ACM, pp 1532–1540

  • Sinreich D, Jabali O, Dellaert NP (2012) Reducing emergency department waiting times by adjusting work shifts considering patient visits to multiple care providers. IIE Trans 44(3):163–180

    Article  Google Scholar 

  • Sun L, Hong LJ, Hu Z (2014) Balancing exploitation and exploration in discrete optimization via simulation through a Gaussian process-based search. Oper Res 62(6):1416–1438

    Article  MathSciNet  Google Scholar 

  • Whitt W, Zhang X (2017) A data-driven model of an emergency department. Oper Res Healthc 12:1–15

    Google Scholar 

  • Xu J, Zhang S, Huang E, Chen CH, Lee LH, Celik N (2014) Efficient multi-fidelity simulation optimization. In: Proceedings of the 2014 winter simulation conference. IEEE Press, pp 3940–3951

  • Xu J, Huang E, Chen CH, Lee LH (2015) Simulation optimization: a review and exploration in the new era of cloud computing and big data. Asia-Pac J Oper Res 32(03):1550019

    Article  MathSciNet  Google Scholar 

  • Xu Q, Tsui KL, Jiang W, Guo H (2016) A hybrid approach for forecasting patient visits in emergency department. Qual Reliab Eng Int 32(8):2751–2759

    Article  Google Scholar 

  • Yeh JY, Lin WS (2007) Using simulation technique and genetic algorithm to improve the quality care of a hospital emergency department. Expert Syst Appl 32(4):1073–1083

    Article  Google Scholar 

  • Yom-Tov GB, Mandelbaum A (2014) Erlang-R: a time-varying queue with reentrant customers, in support of healthcare staffing. Manuf Serv Oper Manag 16(2):283–299

    Article  Google Scholar 

  • Zeinali F, Mahootchi M, Sepehri MM (2015) Resource planning in the emergency departments: a simulation-based metamodeling approach. Simul Model Pract Theory 53:123–138

    Article  Google Scholar 

  • Zhang Y, Luo L, Yang J, Liu D, Kong R, Feng Y (2019) A hybrid ARIMA-SVR approach for forecasting emergency patient flow. J Ambient Intell Hum Comput 10:3315–3323

    Article  Google Scholar 

  • Zhao Y, Xu X, Li H (2018) Minimizing expected cycle time of stochastic customer orders through bounded multi-fidelity simulations. IEEE Trans Autom Sci Eng 15(4):1797–1809

    Article  Google Scholar 

Download references

Acknowledgements

We thank the anonymous reviewers and the Department Editor for their constructive comments and suggestions, which have greatly improved the exposition of this paper.

Funding

Funding was provided by National Natural Science Foundation of China (Grant Nos. 71701132, 61702336).

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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Hainan Guo, Haobin Gu & Jiaxuan Peng

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Yu Zhou

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  1. Hainan Guo
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  2. Haobin Gu
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  3. Yu Zhou
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  4. Jiaxuan Peng
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Correspondence to Yu Zhou.

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Guo, H., Gu, H., Zhou, Y. et al. A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong. Flex Serv Manuf J 34, 238–262 (2022). https://doi.org/10.1007/s10696-020-09395-3

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