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Hospital Bed Management Modelling: A Conceptual Framework

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Decision Sciences (DSA ISC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14779))

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Abstract

Faced with the variability inherent in healthcare, hospitals prioritize operational adaptability and resilience by adjusting their strategies and processes to the current situation. This approach is crucial to mitigate the impact of such uncertainties and deliver seamless and efficient patient care. Hospital bed management (BM), essential for managing patient flow and optimizing resources, is at the heart of these efforts. However, the diversity of BM processes in different organizations makes it difficult to assess their effectiveness.

This study explores the various strategies (including processes) employed in BM. BM involves handling admissions, discharges, and the configuration of beds. To identify such strategies (and build the conceptual framework), we map the physical operations (patient admissions, unit stays, and discharges) and build the logical system (decision-making processes related to these operations). Key practices include blocking, deferring, and diverting admissions; expanding or re-configuring beds through ward reservations and flexibility to adjust bed capacity to demand; and expediting discharges.

Our study emphasizes that the strategies used in BM, show limitations of BM adaptability. We believe that designing models that integrate these adaptive strategies is an unavoidable future line of research. An example of this would be the development of models that ensure responsiveness to varying levels of demand by integrating dynamic responses into bed management strategies.

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References

  1. Akcali, E., Côté, M.J., Lin, C.: A network flow approach to optimizing hospital bed capacity decisions. Health Care Manag. Sci. 9(4), 391–404 (2006). https://doi.org/10.1007/s10729-006-0002-4

    Article  MATH  Google Scholar 

  2. Arabzadeh, B.: Reconfiguration of inpatient services to reduce bed pressure in hospitals. Ph.D. thesis, University of London (2022). https://openaccess.city.ac.uk/id/eprint/28994/

  3. Azcarate, C., Esparza, L., Mallor, F.: The problem of the last bed: contextualization and a new simulation framework for analyzing physician decisions. Omega 96, 102120 (2020). https://doi.org/10.1016/j.omega.2019.102120

    Article  MATH  Google Scholar 

  4. Azcárate, C., Mallor, F., Barado, J.: Calibration of a decision-making process in a simulation model by a bicriteria optimization problem. In: Proceedings of the 2012 Winter Simulation Conference (WSC), pp. 1–10, December 2012. https://doi.org/10.1109/WSC.2012.6465078

  5. Barasa, E.W., Molyneux, S., English, M., Cleary, S.: Hospitals as complex adaptive systems: a case study of factors influencing priority setting practices at the hospital level in Kenya. Soc. Sci. Med. 174, 104–112 (2017). https://doi.org/10.1016/j.socscimed.2016.12.026

  6. Bekker, R., Koole, G., Roubos, D.: Flexible bed allocations for hospital wards. Health Care Manag. Sci. 20(4), 453–466 (2016). https://doi.org/10.1007/s10729-016-9364-4

    Article  MATH  Google Scholar 

  7. Bhattacharjee, P., 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

    Article  MATH  Google Scholar 

  8. Boaden, R., Proudlove, N., Wilson, M.: An exploratory study of bed management. J. Manag. Med. 13(4), 234–250 (1999). https://doi.org/10.1108/02689239910292945

    Article  MATH  Google Scholar 

  9. Busby, C.R.: Modelling hospital surge with data-driven discrete event simulation. Ph.D. thesis, University of Toronto (2018). http://hdl.handle.net/1807/89776

  10. Busby, C.R., Carter, M.W.: Data-driven generic discrete event simulation model of hospital patient flow considering surge. In: 2017 Winter Simulation Conference (WSC), pp. 3006–3017. IEEE, Las Vegas, NV, December 2017.https://doi.org/10.1109/WSC.2017.8248022

  11. Cudney, E.A., et al.: A decision support simulation model for bed management in healthcare. Int. J. Health Care Qual. Assur. 32(2), 499–515 (2019). https://doi.org/10.1108/IJHCQA-10-2017-0186

    Article  MATH  Google Scholar 

  12. Garcia-Vicuña, D., Esparza, L., Mallor, F.: Hospital preparedness during epidemics using simulation: the case of COVID-19. CEJOR 30(1), 213–249 (2021). https://doi.org/10.1007/s10100-021-00779-w

    Article  MathSciNet  MATH  Google Scholar 

  13. Hall, R.: Bed assignment and bed management. In: Hall, R. (ed.) Handbook of Healthcare System Scheduling, pp. 177–200. International Series in Operations Research & Management Science, Springer US, Boston, MA (2012). https://doi.org/10.1007/978-1-4614-1734-7_8

  14. He, L., Chalil Madathil, S., Oberoi, A., Servis, G., 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

  15. Hulshof, P.J.H., Kortbeek, N., Boucherie, R.J., Hans, E.W., Bakker, P.J.M.: Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS. Health Syst. 1(2), 129–175 (2012). https://doi.org/10.1057/hs.2012.18

    Article  Google Scholar 

  16. Humphreys, P., et al.: An overview of hospital capacity planning and optimisation. Healthcare 10(5), 826 (2022). https://doi.org/10.3390/healthcare10050826

    Article  MathSciNet  MATH  Google Scholar 

  17. Izady, N., Arabzadeh, B., Sands, N., Adams, J.: Reconfiguration of inpatient services to reduce bed pressure in hospitals. Eur. J. Oper. Res. (2024). https://doi.org/10.1016/j.ejor.2024.02.008

    Article  MathSciNet  MATH  Google Scholar 

  18. Landa, P., et al.: Modelling hospital medical wards to address patient complexity: a case-based simulation-optimization approach. In: Bélanger, V., Lahrichi, N., Lanzarone, E., Yalçındağ, S. (eds.) ICHCSE 2019. SPMS, vol. 316, pp. 25–39. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39694-7_3

    Chapter  MATH  Google Scholar 

  19. Landa, P., Sonnessa, M., Tànfani, E., Testi, A.: Managing emergent patient flow to inpatient wards: a discrete event simulation approach. In: Obaidat, M.S., Ören, T., Kacprzyk, J., Filipe, J. (eds.) Simulation and Modeling Methodologies, Technologies and Applications, pp. 333–350. Advances in Intelligent Systems and Computing, Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26470-7_17

  20. Landa, P., Sonnessa, M., Tànfani, E., Testi, A.: System dynamics modelling of emergent and elective patient flows. In: Matta, A., Sahin, E., Li, J., Guinet, A., Vandaele, N.J. (eds.) Health Care Systems Engineering for Scientists and Practitioners, pp. 179–191. Springer Proceedings in Mathematics & Statistics, Springer, Cham (2016). https://doi.org/10.1007/978-3-319-35132-2_17

  21. Landa, P., Sonnessa, M., Tànfani, E., Testi, A.: Multiobjective bed management considering emergency and elective patient flows. Int. Trans. Oper. Res. 25(1), 91–110 (2018). https://doi.org/10.1111/itor.12360

    Article  MATH  Google Scholar 

  22. Lee, E.K., Wang, Z., Shapoval, A.: Strategies for inpatient bed management. J. Health Med. Inf. 09(02) (2018). https://doi.org/10.4172/2157-7420.1000308

  23. Mallor, F., Azcárate, C.: Combining optimization with simulation to obtain credible models for intensive care units. Ann. Oper. Res. 221(1), 255–271 (2014). https://doi.org/10.1007/s10479-011-1035-8

    Article  MATH  Google Scholar 

  24. Mallor, F., Azcárate, C., Barado, J.: Optimal control of ICU patient discharge: from theory to implementation. Health Care Manag. Sci. 18(3), 234–250 (2015). https://doi.org/10.1007/s10729-015-9320-8

    Article  MATH  Google Scholar 

  25. Mallor, F., Azcárate, C., Barado, J.: Control problems and management policies in health systems: Application to intensive care units. Flexible Serv. Manuf. J. 28(1), 62–89 (2016). https://doi.org/10.1007/s10696-014-9209-8

  26. Proudlove, N., Boaden, R., Jorgensen, J.: Developing bed managers: the why and the how. J. Nurs. Manag. 15(1), 34–42 (2007). https://doi.org/10.1111/j.1365-2934.2006.00632.x

    Article  MATH  Google Scholar 

  27. Redondo, E., et al.: A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis. Healthcare Anal. 3, 100197 (2023). https://doi.org/10.1016/j.health.2023.100197

    Article  Google Scholar 

  28. Roy, S.N., Shah, B.J., Gajjar, H.: Application of simulation in healthcare service operations: a review and research agenda. ACM Trans. Model. Comput. Simul. 31(1), 1–23 (2020). https://doi.org/10.1145/3427753

    Article  MathSciNet  MATH  Google Scholar 

  29. Roy, S., Prasanna Venkatesan, S., Goh, M.: Healthcare services: a systematic review of patient-centric logistics issues using simulation. J. Oper. Res. Soc. 72(10), 2342–2364 (2021). https://doi.org/10.1080/01605682.2020.1790306

  30. Watson, S.K., Rudge, J.W., Coker, R.: Health Systems’ “Surge Capacity”: state of the art and priorities for future research. Milbank Q. 91(1), 78–122 (2013). https://doi.org/10.1111/milq.12003

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Authors and Affiliations

  1. Departamento de Organización de Empresas, ROGLE, Universitat Politècnica de València, Camino de Vera, s/n., 46022, València, Spain

    Alberto Lacort & Julien Maheut

  2. CIRRELT and Département de mathématiques et de génie industriel, Polytechnique Montréal, CP 6079 Succ. Centre-ville, H3C 3A7, Montréal (Québec), Canada

    Alberto Lacort & Nadia Lahrichi

Authors
  1. Alberto Lacort
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  2. Nadia Lahrichi
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  3. Julien Maheut
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Correspondence to Alberto Lacort .

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Editors and Affiliations

  1. Universidad Politécnica de Valencia, Valencia, Spain

    Angel A. Juan

  2. Public University of Navarre, Pamplona, Spain

    Javier Faulin

  3. ESADE Business School, Sant Cugat, Spain

    David Lopez-Lopez

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Lacort, A., Lahrichi, N., Maheut, J. (2025). Hospital Bed Management Modelling: A Conceptual Framework. In: Juan, A.A., Faulin, J., Lopez-Lopez, D. (eds) Decision Sciences. DSA ISC 2024. Lecture Notes in Computer Science, vol 14779. Springer, Cham. https://doi.org/10.1007/978-3-031-78241-1_16

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  • DOI: https://doi.org/10.1007/978-3-031-78241-1_16

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