Skip to main content

Interdependency of Hospital Departments and Hospital-Wide Patient Flows

  • Chapter
  • First Online:
Patient Flow

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 206))

Abstract

This chapter presents a quantitative analysis of patient flows for a typical hospital-wide system that consists of a set of interdependent subsystems: Emergency Department (ED), Intensive Care Unit (ICU), Operating Rooms (OR), and Inpatient Nursing units (NU) including an effect of patient readmission within 30 days of discharge. It is quantitatively demonstrated that local improvement of one subsystem (ED) does not necessarily result in performance and throughput improvement of the entire system. It is also demonstrated that local improvement targets should be aligned to each other in order to prevent unintended consequences of creating another system bottleneck, and worsening the performance of downstream units.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • AHA. (2011). Trend watch. Examining the drivers of readmissions and reducing unnecessary readmissions for better patient care. Washington, DC: American Hospital Association (AHA). September 2011.

    Google Scholar 

  • Blasak, R., Armel, W., Starks, D., & Hayduk, M. (2003). The use of simulation to evaluate hospital operations between the ED and medical telemetry unit. In S. Chick et al. (Eds.), Proceedings of the 2003 winter simulation conference (pp. 1887–1893). Washington, DC: IEEE.

    Google Scholar 

  • CAEP. (2007). Canadian Association of Emergency Physicians. Position statement on emergency department overcrowding, February issue. www.CAEP.ca

  • Carr, S., & Roberts, S. (2010). Computer simulation in healthcare. Chapter 14. In Y. Yih (Ed.), Handbook of healthcare delivery systems. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Cho, S. J., Jeong, J., Han, S., Yeom, S., Park, S. W., Kim, H. H., et al. (2011). Decreased emergency department length of stay by application of a computerized consultation management system. Academic Emergency Medicine, 18(4), 398–402. doi:10.1111/j.1553-2712.2011.01039.x.

    Article  Google Scholar 

  • Clifford, J., Gaehde, S., Marinello, J., Andrews, M., & Stephens, C. (2008). Improving inpatient and emergency department flow for veterans. Improvement report. Institute for Healthcare Improvement. Retrieved from http://www.IHI.org/ihi

  • Cochran, J., & Bharti, A. (2006). A Multi-staged stochastic methodology for whole hospital bed planning under peak loading. International Journal of Industrial and System Engineering, 1, 8–36.

    Article  Google Scholar 

  • Costa, A., Ridley, S., Shahani, A., Harper, P., De Senna, V., & Nielsen, M. (2003). Mathematical modeling and simulation for planning critical care capacity. Anesthesia, 58, 320–327.

    Article  Google Scholar 

  • De Bruin, A., van Rossum, A., Visser, M., & Koole, G. (2007). Modeling the emergency cardiac in-patient flow: An application of queuing theory. Health Care Management Science, 10, 125–137.

    Article  Google Scholar 

  • Goldratt, E., & Cox, J. (2004). The goal (3rd ed.). Great Barrington, MA: North River Press.

    Google Scholar 

  • Gunal, M., & Pidd, M. (2006). Understanding accident and emergency department performance using simulation. In L. Perrone et al. (Eds.), Proceedings of the 2006 winter simulation conference (pp. 446–452). Washington, DC: IEEE.

    Chapter  Google Scholar 

  • Haraden, C., Nolan, T., & Litvak, E. (2003). Optimizing patient flow: Moving patients smoothly through acute care setting. Institute for Healthcare Improvement Innovation Series 2003. White Papers 2: Cambridge, MA.

    Google Scholar 

  • Hopp, W., & Lovejoy, W. (2013). Hospital operations: Principles of high efficiency health care. Fontana, CA: FT Press, Upper Saddle River, NJ.

    Google Scholar 

  • Jacobson, H., Hall, S., & Swisher, J. (2006). Discreet-event simulation of health care systems. In R. Hall (Ed.), Patient flow: Reducing delay in healthcare delivery (pp. 210–252). New York, NY: Springer.

    Google Scholar 

  • Jencks, S., Williams, M., & Coleman, E. (2009). Re-hospitalizations among patients in medicare fee for service program. New England Journal of Medicine, 360, 1418–1428.

    Article  Google Scholar 

  • Kamanth, J., Osborn, J., Roger, V., & Rohleder, T. (2011). Highlights from the third annual mayo clinic conference on systems engineering and operations research in health care. Mayo Clinic Proceedings, 86(8), 781–786.

    Article  Google Scholar 

  • Kolker, A. (2008). Process modeling of emergency department patient flow: Effect of patient length of stay on ED diversion. Journal of Medical Systems, 32(5), 389–401.

    Article  Google Scholar 

  • Kolker, A. (2009). Process modeling of ICU patient flow: Effect of daily load leveling of elective surgeries on ICU diversion. Journal of Medical Systems, 33(1), 27–40.

    Article  Google Scholar 

  • Kolker, A. (2012). Healthcare management engineering: What does this fancy term really mean? Springer_Briefs series in healthcare management & economics. New York, NY: Springer. 122.

    Book  Google Scholar 

  • Law, A. (2007). Simulation modeling and analysis (4th ed.). New-York: McGraw-Hill.

    Google Scholar 

  • Lefcowitz, M. (2007, February 26). Why does process improvement fail? Builder-AU by Developers for developers. Retrieved from www.builderau.com.au/strategy/projectmanagement/

  • Litvak, E., Long, M., Cooper, A., & McManus, M. (2001). Emergency department diversion: Causes and solutions. Academic Emergency Medicine, 8, 1108–1110.

    Google Scholar 

  • Marshall, A., Vasilakis, C., & El-Darzi, E. (2005). Length of stay-based patient flow models: Recent developments and future directions. Health Care Management Science, 8, 213–220.

    Article  Google Scholar 

  • Mayhew, L., & Smith, D. (2008). Using queuing theory to analyze the Government’s 4-h completion time target in Accident and Emergency departments. Health Care Management Science, 11, 11–21.

    Article  Google Scholar 

  • McManus, M., Long, M., Cooper, A., Mandel, J., Berwick, D., Pagano, M., et al. (2003). Variability in surgical caseload and access to intensive care services. Anesthesiology, 98(6), 1491–1496.

    Article  Google Scholar 

  • Miller, M., Ferrin, D., & Szymanski, J. (2003). Simulating Six Sigma Improvement Ideas for a Hospital Emergency Department. In S. Chick et al. (Eds.), Proceedings of the 2003 winter simulation conference (pp. 1926–1929). Washington, DC: IEEE.

    Google Scholar 

  • MPAC (Medicare Payment Advisory Commission). (2007). Payment policy for inpatient readmissions. Report to the congress: Promoting greater efficiency in Medicare. Washington, DC.

    Google Scholar 

  • Oredsson, S., Jonsson, H., Rognes, J., Lind, L., Göransson, K., Ehrenberg, A., et al. (2011). A systematic review of triage-related interventions to improve patient flow in emergency departments. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 19, 43. doi:10.1186/1757-7241-19-43.

    Article  Google Scholar 

  • Reid, P., Compton, W., Grossman, J., & Fanjiang, G. (Eds.). (2006). Building a better system: A new engineering healthcare partnership. Washington, DC: National Academy of Engineering and Institute of Medicine. The National Academy Press

    Google Scholar 

  • Savage, S. (2009). The flaw of averages. Hoboken, NJ: Wiley. 392.

    Google Scholar 

  • Simon, S., & Armel, W. (2003). The use of simulation to reduce the length of stay in an emergency department. In S. Chick et al. (Eds.), Proceedings of the 2003 winter simulation conference (pp. 1907–1911). Washington, DC: IEEE.

    Google Scholar 

  • Wang, J. (2012). Reducing length of stay in emergency department: A simulation study at a community hospital. IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems and Humans, 42(6), C1–1309.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexander Kolker .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Kolker, A. (2013). Interdependency of Hospital Departments and Hospital-Wide Patient Flows. In: Hall, R. (eds) Patient Flow. International Series in Operations Research & Management Science, vol 206. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-9512-3_2

Download citation

Publish with us

Policies and ethics