Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2020 | 30 | nr 3 | 5--19
Tytuł artykułu

Simulation Modelling for Predicting Hospital Admissions and Bed Utilisation

Treść / Zawartość
Warianty tytułu
Języki publikacji
Demographic research of the world population shows that societies are ageing. The ongoing changes in the population structure will require appropriate quantitative and qualitative adjustments in health services to meet the needs of society. Simulation methods turn out to be helpful in these kinds of analyses. In this paper, the authors present a case study on using discrete event simulation (DES) to support decision-making in the field of hospital bed management in the light of demographic changes. The case study was elaborated for one of the Polish district hospitals. A DES model was built to simulate admissions to two hospital wards: paediatric and geriatric. A series of experiments were carried out as based on real data extracted from the hospital database and forecasted demographic trends elaborated by the Central Statistical Office of Poland (CSO). The influence of demographic changes on hospital admissions in the chosen age-gender cohorts was explored, examining different variants of hospital bed availability. The results of the experiments show that demographic trends significantly influence healthcare admission and bed utilisation. The reduction in the number of admissions to the paediatric ward by about 6% results in a change in average bed utilisation from 57.90% to 54.06%. With about 12% more admissions to the geriatric ward, the change is from 68.88% to 75.59%. (original abstract)
Opis fizyczny
  • Wroclaw University of Science and Technology, Wrocław, Poland
  • Wroclaw University of Science and Technology, Wrocław, Poland
  • [1] ADUDODLA R.R., BABU G., KUMAR S., SARMA S., Bed allocation in hospitals. A case study, Int. J. Inn. Technol. Res., 2015, 3, 2455-2458.
  • [2] ALLEN R.T., HALES N.M., BACCARELLI A., JERRETT M., EZZATI M., DOCKERY D.W., POPE C.A., Countervailing effects of income, air pollution, smoking, and obesity on ageing and life expectancy: Population-based study of U.S. Counties, Environmental Health: A Global Access Science Source, Environ. Health, 2016, 15 (1), 1-10.
  • [3] ANDERSEN A.R., NIELSEN B.F., REINHARDT L.B., Optimization of hospital ward resources with patient relocation using Markov chain modeling, Eur. J. Oper. Res., 2017, 260 (3), 1152-1163.
  • [4] BELCIUG S., GORUNESCU F., Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation, J. Biom. Inf., 2015, 53, 261-269.
  • [5] BELCIUG S., GORUNESCU F., A hybrid genetic algorithm-queuing multi-compartment model for optimizing inpatient bed occupancy and associated costs, Art. Int. Med., 2016, 68, 59-69.
  • [6] CRAWFORD E.A., PARIKH P.J., KONG N., THAKAR C.V., Analyzing discharge strategies during acute care: A discrete-event simulation study, Med. Dec. Making, 2014, 34 (2), 231-241.
  • [7] Central Statistical Office of Poland (CSO), (accessed: March 2019).
  • [8] GORUNESCU F., MCCLEAN S.I., MILLARD P.H., Using a queueing model to help plan bed allocation in a department of geriatric medicine, Health Care Manage. Sci., 2002, 5 (4), 307-312.
  • [9] GURALNIK J.M., Disability as a public health outcome in the ageing population, Ann. Rev. Publ. Health, 1996, 17 (1), 25-46.
  • [10] 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, Comp. Ind. Eng., 2019, 127, 451-466.
  • [11] HOLM L.B., LURÅS H., 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., 2013, 82 (2), 80-89.
  • [12] KELTON W.D., SADOWSKI B.P., STURROCK D.T., Simulation with Arena, 3rd Ed., McGraw-Hill, Inc., New York 2004.
  • [13] KISLIAKOVSKII I., BALAKHONTCEVA M., KOVALCHUK S., ZVARTAU N., KONRADI A., Towards a simulation-based framework for decision support in healthcare quality assessment, Proc. Comp. Sci., 2017, 119, 207-214.
  • [14] LAKSHMI C., SIVAKUMAR A.I., Application of queuing theory in health care: a literature review, Oper. Res. Health Care, 2013, 2 (1-2), 25-39.
  • [15] LANDA P., SONNESSA M., TÀNFANI E., TESTI A., A discrete event simulation model to support bed management, [In:] M.S. Obaidat, J. Kacprzyk, T. Ören (Eds.), Proc. 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH), SciTePress, 2014, 1 (HA), 901-912.
  • [16] LAW A.M., KELTON W.D., Simulation modelling and analysis, McGraw-Hill, Inc., New York 1991.
  • [17] Local Data Bank (LDB), (accessed: March 2019).
  • [18] NAYLOR T.H., Digital modelling of economic systems, PWN, Warsaw 1975 (in Polish).
  • [19] SALTZMAN R., ROEDER T., LAMBTON J., PARAM L., FROST B., FERNANDES R., The impact of a discharge holding area on the throughput of a paediatric unit, Service Science, 2017, 9 (2), 121-135.
  • [20] WHO World Health Organization. Ageing and health (2018), room/fact-sheets/detail/ageing-and-health (accessed: July 2019).
  • [21] ZHANG X., Application of discrete event simulation in health care. A systematic review, BMC Health Serv. Res., 2018, 18 (1), 1-11.
Typ dokumentu
Identyfikator YADDA

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.