HCUPnet., Healthcare Cost and Utilization Project. Agency for Healthcare Research and Quality, Rockville, MD. https://datatools.ahrq.gov/hcupnet.

AHA., American Hospital Association, Hospital Statistics (annual), 2024. https://www.aha.org/data-insights/hospitals-and-systems.

Rachuba, S., Reuter-Oppermann, M., and Thielen, C., Integrated planning in hospitals: a review. OR Spectrum. 2024. https://doi.org/10.1007/s00291-024-00797-5.

Article  Google Scholar 

He, L., Chalil Madathil, S., Oberoi, A., Servis, G., and Khasawneh, M.T., A systematic review of research design and modeling techniques in inpatient bed management. Comput. Ind. Eng. 127:451–466, 2019. https://doi.org/10.1016/j.cie.2018.10.033.

Article  Google Scholar 

Baru, R.A., Cudney, E.A., Guardiola, I.G., Warner, D.L., and Phillips, R.E., Systematic Review of Operations Research and Simulation Methods for Bed Management, 2015. https://api.semanticscholar.org/CorpusID:115817970.

Bhattacharjee, P., and Ray, P.K., Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections. Comput. Ind. Eng. 78:299–312, 2014. https://doi.org/10.1016/j.cie.2014.04.016.

Jacobson, S.H., Hall, S.N., and Swisher, J.R., In: Hall, R.W. (ed.), Discrete-Event Simulation of Health Care Systems. Boston, MA: Springer US, pp. 211–252, 2006. https://doi.org/10.1007/978-0-387-33636-7_8.

Barnes, S., Golden, B., and Price, S., In: Denton, B.T. (ed.), Applications of Agent-Based Modeling and Simulation to Healthcare Operations Management. New York, NY: Springer New York, pp. 45–74, 2013. https://doi.org/10.1007/978-1-4614-5885-2_3.

Thompson, S.M., Day, R., and Garfinkel, R., In: Denton, B.T. (ed.), Improving the Flow of Patients Through Healthcare Organizations. New York, NY: Springer New York, pp. 183–204, 2013. https://doi.org/10.1007/978-1-4614-5885-2_7.

Zhang, X., Application of discrete event simulation in health care: a systematic review. BMC Health Serv. Res. 18(1):687, 2018 . https://doi.org/10.1186/s12913-018-3456-4.

Article  PubMed  PubMed Central  Google Scholar 

Rutberg, M.H., Wenczel, S., Devaney, J., Goldlust, E.J., and Day, T.E., Incorporating Discrete Event Simulation Into Quality Improvement Efforts in Health Care Systems. Am. J. Med. Qual. 30(1):31–35, 2015. https://doi.org/10.1177/1062860613512863.

Tyler, J.M., Murch, B.J., Vasilakis, C., and Wood, R.M., Improving uptake of simulation in healthcare: User-driven development of an open-source tool for modelling patient flow. J. Simul. 17(6):765–782, 2023.https://doi.org/10.1080/17477778.2022.2081521.

Ordu, M., Demir, E., Tofallis, C., and Gunal, M.M., A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation. Emergency 4:100248, 2023. https://doi.org/10.1016/j.health.2023.100248.

Hassanzadeh, H., Khanna, S., Boyle, J., Jensen, F., and Murdoch, A., New bed configurations and discharge timing policies: a hospital-wide simulation. Emerg. Med. Austral. 35(3):434–441, 2023. https://doi.org/10.1111/1742-6723.14135.

Article  Google Scholar 

Cochran, J.K., and Bharti, A., Stochastic bed balancing of an obstetrics hospital. Health Care Manag. Sci. 9(1):31–45, 2006. https://doi.org/10.1007/s10729-006-6278-6.

Article  PubMed  Google Scholar 

Bertsimas, D., and Pauphilet, J., Hospital-wide inpatient flow optimization. Manag. Sci. 70(7):4893–4911, 2023. https://doi.org/10.1287/mnsc.2023.4933.

Article  Google Scholar 

Yu, H., Shen, T., and Zhong, L., Optimizing hospital bed allocation for coordinated medical efficiency and quality improvement. J. Combinator. Optim. 48(4):32, 2024. https://doi.org/10.1007/s10878-024-01210-1.

Bahalkeh, E., Chiam, T.C., and Yih, Y., An interpretable clustering classification approach for assessing and adjusting hospital service lines. Healthcare Anal. 4:100255, 2023. https://doi.org/10.1016/j.health.2023.100255.

Andersen, A.R., Nielsen, B.F., and Plesner, A.L., An approximation of the inpatient distribution in hospitals with patient relocation using Markov chains. Healthcare Anal. 3:100145, 2023. https://doi.org/10.1016/j.health.2023.100145.

Bekker, R., uit het Broek, M., and Koole, G., Modeling COVID-19 hospital admissions and occupancy in the Netherlands. Eur. J. Oper. Res. 304(1):207–218, 2023. https://doi.org/10.1016/j.ejor.2021.12.044. The role of Operational Research in future epidemics/ pandemics

Dijkstra, S., Baas, S., Braaksma, A., and Boucherie, R.J., Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy. Omega 116:102801, 2023. https://doi.org/10.1016/j.omega.2022.102801.

Devapriya, P., Strömblad, C.T.B., Bailey, M.D., Frazier, S., Bulger, J., Kemberling, S.T., et al., StratBAM: a discrete-event simulation model to support strategic hospital bed capacity decisions. J. Med. Syst. 39(10):130, 2015. https://doi.org/10.1007/s10916-015-0325-0.

Article  PubMed  Google Scholar 

Eren Demir, M.M.G., and Southern, D., Demand and capacity modelling for acute services using discrete event simulation. Health Syst. 6(1):33–40, 2017. https://doi.org/10.1057/hs.2016.1.

Article  Google Scholar 

Rodrigues, F.F., Zaric, G.S., and Stanford, D.A., Discrete event simulation model for planning Level 2 “step-down” bed needs using NEMS. Oper. Res. Health Care 17:42–54, 2018. https://doi.org/10.1016/j.orhc.2017.10.001. Operational Research Applied to Health Services 2016

Hu, Y., Dong, J., Perry, O., Cyrus, R.M., Gravenor, S., and Schmidt, M.J., Use of a novel patient-flow model to optimize hospital bed capacity for medical patients. Joint Comm. J. Qual. Patient Safety 47(6):354–363, 2021. https://doi.org/10.1016/j.jcjq.2021.02.008.

Article  Google Scholar 

Latruwe, T., Van der Wee, M., Vanleenhove, P., Devriese, J., Verbrugge, S., and Colle, D., A long-term forecasting and simulation model for strategic planning of hospital bed capacity. Oper. Res. Health Care 36:100375, 2023. https://doi.org/10.1016/j.orhc.2022.100375.

Oakley, D., Onggo, B.S., and Worthington, D., Symbiotic simulation for the operational management of inpatient beds: model development and validation using \(\Delta \)-method. Health Care Manag. Sci. 23(1):153–169, 2020. https://doi.org/10.1007/s10729-019-09485-1.

Article  PubMed  Google Scholar 

Holm, L.B., Lurås, H., and Dahl, F.A., Improving hospital bed utilisation through simulation and optimisation: With application to a 40% increase in patient volume in a Norwegian general hospital. Int. J. Med. Inf. 82(2):80–89, 2013. https://doi.org/10.1016/j.ijmedinf.2012.05.006.

Article  Google Scholar 

Muhammed Ordu, C.T., Demir, E., and Gunal, M.M., A novel healthcare resource allocation decision support tool: a forecasting-simulation-optimization approach. J. Oper. Res. Soc. 72(3):485–500, 2021. https://doi.org/10.1080/01605682.2019.1700186.

Gong, X., Wang, X., Zhou, L., and Geng, N., Managing hospital inpatient beds under clustered overflow configuration. Comput. Oper. Res. 148:106021, 2022. https://doi.org/10.1016/j.cor.2022.106021.

Tello, M., Reich, E.S., Puckey, J., Maff, R., Garcia-Arce, A., Bhattacharya, B.S., et al., Machine learning based forecast for the prediction of inpatient bed demand. BMC Med. Inf. Decis. Making 22(1):55, 2022 . https://doi.org/10.1186/s12911-022-01787-9.

Article  Google Scholar 

Alexopoulos, C., Goldsman, D., Fontanesi, J., Kopald, D., and Wilson, J.R., Modeling patient arrivals in community clinics. Omega 36(1):33–43, 2008. https://doi.org/10.1016/j.omega.2005.07.013. . Special Issue Section: Papers presented at the INFORMS conference, Atlanta, 2003

Tafazzoli, A., and Wilson, J.R., Skart: a skewness- and autoregression-adjusted batch-means procedure for simulation analysis. IIE Trans. 43(2):110–128, 2010. https://doi.org/10.1080/0740817X.2010.504688.

Article  Google Scholar