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Bed-Occupancy Management and Hospital Planning: A Handbook

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Regionalized Management of Medicine

Part of the book series: Translational Bioinformatics ((TRBIO,volume 17))

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Abstract

When dealing with bed-occupancy management and hospital planning, one must take into account many factors, whether we are discussing diseases, patients’ characteristics, cultural background, budget, local and national political considerations, etc. Daily, hospital managers are faced with decision-making processes, having to find the correct balance between all of the abovementioned aspects. The aim of this chapter is to provide scientifically valid models of healthcare planning that can be applied cross-national.

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References

  • Belciug S. Artificial intelligence in cancer—diagnostic to tailored treatment. Elsevier; 2020.

    Google Scholar 

  • Belciug S, Gorunescu F. Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation. J Biomed Inf. 2015;53:261–9.

    Article  Google Scholar 

  • Belciug S, Gorunescu F. Intelligent decision support systems—a journey to smarter healthcare. Springer; 2020.

    Book  Google Scholar 

  • Belciug S, Bejinariu S, Costin H. An artificial immune system approach for a multi-compartment queueing model for improving medical resources and inpatient bed occupancy in pandemics. Adv Electr Comput Eng. 2020;20(3):23–30.

    Article  Google Scholar 

  • Blickle T, Thiele K A comparison of selection schemes used in genetic algorithms, TIK-Report, 11; 1995.

    Google Scholar 

  • Bruin AM, Bekker R, Zante L, Koole GM. Dimensioning hospital wards using the Erlang loss model. Ann Oper Res. 2010;178:23–43.

    Article  Google Scholar 

  • Burnet FM. A modification of Jerne’s theory of antibody production using the concept of clonal selection. Aust J Sci. 1957;20:67–9.

    Google Scholar 

  • Burnet FM. The clonal selection theory of acquired immunity. Vanderbilt University Press; 1959.

    Book  Google Scholar 

  • Cochran J, Roche K. A multi-class queuing network analysis methodology for improving hospital emergency department performance. Comput Oper Res. 2009;36(5):1497–512.

    Article  Google Scholar 

  • Cooper RB. Introduction to queueing theory. New York: McMillan; 1972.

    Google Scholar 

  • Cox DR. Renewal theory. London: Methuen; 1962.

    Google Scholar 

  • Eiben AE. Multiparent recombination in evolutionary computing. In: Gosh A, Tsutsui S, editors. Advances in evolutionary computation: theory and applications. Heildelberg: Springer; 2003. p. 175–92.

    Chapter  Google Scholar 

  • Eiben AE, Smith JE. Introduction to evolutionary computing. Heildelberg: Springer; 2015.

    Book  Google Scholar 

  • Faddy M. Examples of fitting structured phase-type distributions. Appl Stoch Model Data Anal. 1994;10:247–55.

    Article  Google Scholar 

  • Gorunescu F, McClean SI, Millard PH. A queueing model for bed-occupancy management and planning of hospitals. J Oper Res Soc. 2002;53(1):19–24.

    Article  Google Scholar 

  • Harrison GW, Millard PH. Balancing acute and long-stay care: the mathematics of throughput in departments of geriatric medicine. Meth Inform Med. 1991;30:221–8.

    Article  CAS  Google Scholar 

  • Irvine V, McClean S, Millard P. Stochastic models for geriatric in-patient behavior. IMA J Math Appl Med Biol. 1994;11:207–2016.

    Article  CAS  Google Scholar 

  • Jebari K, Madiafi M. Selection methods for genetic algorithm. In J Emerg Sci. 2013;3(4):333–44.

    Google Scholar 

  • Koizumi N, Kuno E, Smith T. A queuing network model with blocking: analysis of congested patient flows in mental health systems. Health Care Manage Sci. 2005;8:49–60.

    Article  Google Scholar 

  • McClean SI. Modelling and Simulation for health applications, modeling hospital resource use. In: A different approach to the planning and control of health care systems. Royal Society of Medicine Press; 1994. p. 21–7.

    Google Scholar 

  • Millard PH. Geriatric medicine: a new method of measuring bed usage and a theory for planning. MD thesis. University of London; 1988.

    Google Scholar 

  • Millard PH. Background to potential benefits of flow modeling medical and social services for an aging population. In: Go with the flow. A systems approach to healthcare planning. The Royal Society of Medicine Press; 1996. p. 95–110.

    Google Scholar 

  • Millard PH, Rae B, Busby W. Why nosokinetics? Measuring and modelling the process. In: McClean S, Millard P, El-Darzi E, Nugent C, editors. Intelligent patient management (studies in computational intelligence, 189), Part I: intelligent patient management. Berlin, Heidelberg: Springer; 2009. p. 3–23.

    Google Scholar 

  • Silver EA, Smith SA. A graphical aid for determining optimal inventories in a unit inventory replenishment system. Mngt Sci. 1977;24:358–9.

    Article  Google Scholar 

  • Stevenson WJ. Production/operations management. 6th ed. Boston: Irwin, McGraw-Hill; 1996.

    Google Scholar 

  • Taylor G, McClean S, Millard P. Continuous-time Markov models for geriatric patient behavior. Appl Stoch Model Data Anal. 1998;13:315–23.

    Article  Google Scholar 

  • Taylor G, McClean S, Millard P. Stochastic models of geriatric patient bed occupancy behavior. JRSS Ser A. 2000;163:39–48.

    Google Scholar 

  • Tijms HC. Stochastic modeling and analysis. In: A computational approach. Chichester: Wiley; 1986.

    Google Scholar 

  • Vasilakis C, Marshal AH. Modelling nationwide hospital length of stay: opening the black box. J Oper Res Soc. 2005;56:862–9.

    Article  Google Scholar 

  • Vasilakis C, El-Darzi E, Chountas P. A decision support system for measuring the multi-phase nature of patient flow in hospitals (Studies in computational intelligence 109). In: McClean S, Millard P, Nugent C, editors. Intelligent techniques and tools for novel system architectures. Berlin, Heidelberg: Springer; 2008.

    Google Scholar 

  • Vinicchayakul R. Costing care in geriatric medicine. University of London; 2000. MSc dissertation

    Google Scholar 

  • White R. The development of the Poor Law nursing service 1848–1948. A discussion of the historical method and a summary of the findings. Int J Nurs Studies. 1977;14:19–27.

    Article  CAS  Google Scholar 

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Correspondence to Smaranda Belciug .

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Belciug, S. (2022). Bed-Occupancy Management and Hospital Planning: A Handbook. In: Shen, H., Zeng, Y., Li, L., Wang, X. (eds) Regionalized Management of Medicine. Translational Bioinformatics, vol 17. Springer, Singapore. https://doi.org/10.1007/978-981-16-7893-6_10

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