Skip to main content
SpringerLink
Log in

Stochastic bed balancing of an obstetrics hospital

  • Published:
Health Care Management Science Aims and scope Submit manuscript

Abstract

Health care is one of the largest industries in the developed world and the top domestic industry in the United States (US). Over the past thirty years there has been a dramatic increase in healthcare costs in the US, of which about one-third can be attributed to hospital spending. One of the key factors in hospital cost containment and revenue enhancement is effective and efficient bed planning and capacity analysis. This study aims to balance bed unit utilizations across an obstetrics hospital and minimize the blocking of beds from upstream units within given constraints on bed reallocation. The methodology includes the assessment and effect of time-dependent patterns of monthly, daily, and hourly demand. Queuing networks are first used to assess the flows between units, establish target utilizations of bed units, and involve stakeholders in a flow characterization that they understand. Discrete-event simulation is then used to maximize the flow through the balanced system including non-homogeneous effects, non-exponential lengths of stay, and blocking behavior. Results of the models are validated against actual data collected from the hospital. Several ‘what if’ scenarios are studied showing that 38% more patient flow can be achieved with only 15% more patient beds. The results of the study have been implemented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Preater J (2002) A bibliography of queues in health and medicine, Health Care Management Science 5: 283–283

    Article  Google Scholar 

  2. Jun JB, Jacobson SH, Swisher JR (1999) Application of discrete–event simulation in health care clinics: a survey, Journal of Operational Research Society 50: 109–123

    Google Scholar 

  3. Churilov L, and Carter M (2004) http://orchid.bsys.monash.edu.au/orchid/

  4. Gorunescu F, McClean SI, Millard PH (2002) Using a queueing model to help plan bed allocation in a department of geriatric medicine, Health Care Management Science 5: 307–312

    Article  Google Scholar 

  5. Ridge JC, Jones SK, Nielsen MS, Shahani AK (1998) Capacity planning for intensive care units, European Journal of Operational Research 105: 346–355

    Article  Google Scholar 

  6. Kim S, Horowitz I, Young KK, Buckley TA (1999) Analysis of capacity management of the intensive care unit in a hospital, European Journal of Operational Research 115: 36–46

    Article  Google Scholar 

  7. Koizumi N, Kuno E, Smith T (2005) Modeling patient flows using a queuing network with blocking, Health Care Management Science 8: 49–60

    Article  Google Scholar 

  8. Gross D, Harris CM (1998) Fundamentals of queuing theory, 3rd. Ed., Wiley and Sons, description and software available at http://mason.gmu.edu/~dgross1/gross-harris/gh3.html

  9. Perros HG (1994) Queuing Networks with Blocking, New York: Oxford University Press

    Google Scholar 

  10. Cochran JK, Mackulak GT, Savory PA (1995) “Simulation project characteristics in industrial settings”, Interfaces 25:4, 104–113

    Google Scholar 

  11. Mahachek AR, Knabe TL (1984) Computer simulation of patient flow in obstetrical/gynecology clinics, Simulation, (August), 95–101

  12. Bagust A, Place M, Posnett JW (1999), Dynamics of bed use in accommodating emergency admissions: stochastic simulation model, British Medical Journal 319: 155–158

    Google Scholar 

  13. Elbeyli S, Krishnan P (2001) In-Patient flow analysis using ProModel™ simulation package, Simulation in the health and medical sciences, 3–6

  14. Harper PR, Shahani AK (2002) Modeling for the planning and management of bed capacities in hospitals, Journal of the Operational Research Society 53: 11–18

    Google Scholar 

  15. Vassilacopoulos G (1985) A simulation model for bed allocation to hospital inpatient departments, Simulation, (November): 233–241

  16. Darzi EE, Vasilakis C, Chaussalet T, Millard PH (1998) A simulation modeling approach to evaluating length of stay, emptiness and bed blocking in a hospital geriatric department, Health Care Management Science 1: 143–149

    Google Scholar 

  17. Vasilakis C, Darzi EE (2001) A simulation study of the winter bed crisis, Health Care Management Science 4: 31–36

    Article  Google Scholar 

  18. Swisher JR, Jacobson SH, Jun JB, Balci O (2001) Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation, Computers & Operations Research 28: 105–125

    Article  Google Scholar 

  19. Rossetti MD, Trzcinski GF, Syverud SA (1999) Emergency department simulation and determination of optimal attending physician staffing schedules, In Proceedings of the 1999 Winter Simulation Conference, ed. P.A. Farrington, H.B. Nembhard, D.T. Sturrock and G.W. Evans, 1532–1540

  20. Standridge CR (1999) A tutorial on simulation in health care: applications and issues, In Proceedings of the 1999 Winter Simulation Conference, ed. Farrington PA, Nembhard HB, Sturrock DT and Evans GW, 49–55

  21. Lowery JC (1996) Introduction to simulation in health care, In Proceedings of the 1996 Winter Simulation Conference, ed. Charnes, JM, Morrice DJ, Brunner DT, Swain JJ, 78–84

  22. Davies HTO, Davies R (1995) Simulating health systems: modeling problems and software solutions, European Journal of Operational Research 87: 35–44

    Article  Google Scholar 

  23. Harper PR (2002) A framework for operational modeling of hospital resources, Health Care Management Science 5: 165–173

    Article  Google Scholar 

  24. Eldabi T, Irani Z, Paul RJ (2002) A proposed approach for modeling health care systems for understanding, Journal of Management in Medicine 16: 170–187

    Article  Google Scholar 

  25. Seymour DG (2001) Health care modeling and clinical practice: theoretical exercise or practical tool?, Health Care Management Science 4: 7–12

    Article  Google Scholar 

  26. Carter MW, Blake JT (2004) Using simulation in an acute-care hospital: easier said than done, in Operations Research and Health Care, A Handbook of Methods and Applications, edited by Brandeau, ML, Sainfort, F, and Pierskalla, WP, Kluwer Academic Publishers

  27. Bharti A (2004) “A Two-Stage Stochastic Methodology for Hospital Bed Planning Under Peak Loading”, MS Thesis, Arizona State University, August

  28. Cochran JK, (1987) “Techniques for Ascertaining the Validity of Large - Scale Production Simulation Models”, International Journal of Production Research 25:2: 233–244

  29. Banks J, Carson JS, Nelson BL (2000) Discrete Event System Simulation, 3rd Ed., Prentice Hall

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Arizona State University, USA

    Jeffery K. Cochran

  2. Glendale Memorial Hospital and Health Center, CA

    Aseem Bharti

Authors
  1. Jeffery K. Cochran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aseem Bharti
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10729-006-6278-6

Keywords

Search

Navigation