Skip to main content
Log in

Simulation analysis of hospital intensive care unit reimbursement policies from the triple bottom line perspective

  • Original Article
  • Published:
Journal of Simulation

Abstract

We consider how alternative intensive care unit (ICU) cost reimbursement policies impact an ICU’s ability to satisfy the triple bottom line (TBL) of sustainability. Towards this end, we develop a discrete event simulation model of an ICU and use simulation optimization to identify ‘near-optimal’ ICU cost reimbursement policies, staffing levels, and sizes (number of beds) that allow investors to reap profits while still respecting the TBL of sustainability. The studied ICU is reimbursed based on either (1) the total time patients spend in the ICU or (2) the total time medical doctors spend treating patients. Results show that reimbursement Policy 1 generates higher profits but is associated with socially and environmentally unsustainable outcomes including high numbers of early patient discharges and readmissions and a large ICU size. Reimbursement Policy 2, on the other hand, respects the TBL of sustainability to a much greater degree while still yielding a healthy profit.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Figure 1
Figure 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Anderson D, Golden B, Jank W and Wasil E (2012). The impact of hospital utilization on patient readmission rate. Health Care Management Science 15: 29–36.

    Article  Google Scholar 

  • Armony M, Chan CW and Zhu B (2014). Critical care in hospitals: When to introduce a step down unit. Working Paper, Columbia University, http://www.columbia.edu/~cc3179/icusdu_2014.pdf, accessed 8 May 2014.

  • Aspenson M and Hazaray S (2012). The clock is ticking on readmission penalties. Healthcare Financial Management 66 (7): 58–63.

    Google Scholar 

  • Baker AM (2006). Human and environmental health: Sustainable design for the NICU. Journal of Perinatology 26 (S3): S31–S33.

    Article  Google Scholar 

  • Berk E and Moinzadeh K (1998). The impact of discharge decisions on health care quality. Management Science 44 (3): 400–415.

    Article  Google Scholar 

  • Bittner MI et al (2013). How is intensive care reimbursed? A review of eight European countries. Annals of Intensive Care 3 (1): 37–45.

    Article  Google Scholar 

  • Brailsford SC, Harper PR, Patel B and Pitt M (2009). An analysis of the academic literature on simulation and modelling in health care. Journal of Simulation 3 (3): 130–140.

    Article  Google Scholar 

  • Cahill W and Render M (1999). Dynamic simulation modeling of ICU bed availability. IEEE Winter Simulation Conference Proceedings, Vol. 2, ACM, New York, pp. 1573—1576.

  • Campbell AJ, Cook JA, Adey G and Cuthbertson BH (2008). Predicting death and readmission after intensive care discharge. British Journal of Anaesthesia 100 (5): 656–662.

    Article  Google Scholar 

  • Carling PC and Bartley JM (2010). Evaluating hygienic cleaning in health care settings: What you do not know can harm your patients. American Journal of Infection Control 38 (5): S41–50.

    Article  Google Scholar 

  • Chan CW, Farias VF, Bambos N and Escobar GJ (2012). Optimizing intensive care unit discharge decisions with patient readmissions. Operations Research 60 (6): 1323–1341.

    Article  Google Scholar 

  • Chen LM, Martin CM, Keenan SP and Sibbald WJ (1998). Patients readmitted to the intensive care unit during the same hospitalization: Clinical features and outcomes. Critical Care Medicine 26 (11): 1834–1841.

    Article  Google Scholar 

  • Chrusch CA, Olafson KP, McMillan PM, Roberts DE and Gray PR (2009). High occupancy increases the risk of early death or readmission after transfer from intensive care. Critical Care Medicine 37 (10): 2753–2758.

    Article  Google Scholar 

  • Cooper LM and Linde-Zwirble WT (2004). Medicare intensive care unit use: Analysis of incidence, cost, and payment. Critical Care Medicine 32 (11): 2247–2253.

    Article  Google Scholar 

  • Coopersmith CM et al (2012). A comparison of critical care research funding and the financial burden of critical illness in the United States. Critical Care Medicine 40 (4): 1072–1079.

    Article  Google Scholar 

  • Csomos A et al (2010). Intensive care reimbursement practices: Results from the ICUFUND survey. Intensive Care Medicine 36 (10): 1759–1764.

    Article  Google Scholar 

  • Dobson G, Lee HH and Pinker E (2010). A model of ICU bumping. Operations Research 58 (6): 1564–1576.

    Article  Google Scholar 

  • Elkington J (1997). Cannibals with Forks: The Triple Bottom Line of 21st Century Business. Capstone: Oxford.

    Google Scholar 

  • Epstein MJ and Yuthas K (2012). Analyzing sustainability impacts. Strategic Finance 97 (7): 27–33.

    Google Scholar 

  • Fone D et al (2003). Systematic review of the use and value of computer simulation modelling in population health and healthcare delivery. Journal of Public Health Medicine 25 (4): 325–335.

    Article  Google Scholar 

  • Franklin C and Jackson D (1983). Discharge decision-making in a medical ICU: Characteristics of unexpected readmissions. Critical Care Medicine 11 (2): 61–66.

    Article  Google Scholar 

  • Griffiths JD, Price-Lloyd N, Smithies M and Williams JE (2005). Modelling the requirement for supplementary nurses in an intensive care unit. Journal of the Operational Research Society 56 (2): 126–133.

    Article  Google Scholar 

  • Günal M and Pidd M (2010). Discrete event simulation for performance modelling in health care: A review of the literature. Journal of Simulation 4 (1): 42–51.

    Article  Google Scholar 

  • Hamrock E, Paige K, Parks J, Scheulen J and Levin S (2013). Discrete event simulation for healthcare organizations: A tool for decision making. Journal of Healthcare Management 58 (2): 110–125.

    Google Scholar 

  • Harrell C, Ghosh BK and Bowden RO (2012). Simulation Using ProModel. McGraw-Hill: New York.

    Google Scholar 

  • Hoehn T, Drabik A, Lehmann C, Christaras A, Stannigel H and Mayatepek E (2008). Correlation between severity of disease and reimbursement of costs in neonatal and paediatric intensive care patients. ActaPaediatrica 97 (10): 1438–1442.

    Google Scholar 

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

    Article  Google Scholar 

  • Kc D and Terwiesch C (2012). An econometric analysis of patient flows in the cardiac intensive care unit. Manufacturing and Service Operations Management 14 (1): 50–65.

    Article  Google Scholar 

  • Kim SC, Horowitz I, Young KK and Buckley TA (2000). Flexible bed allocation and performance in the intensive care unit. Journal of Operations Management 18 (4): 427–443.

    Article  Google Scholar 

  • Kramer AA, Higgins TL and Zimmerman JE (2013). The association between ICU readmission rate and patient outcomes. Critical Care Medicine 41 (1): 24–33.

    Article  Google Scholar 

  • Milbrandt EB et al (2008). Growth of intensive care unit resource use and its estimated cost in medicare. Critical Care Medicine 36 (9): 2504–2510.

    Article  Google Scholar 

  • Popely D (2009). Beyond the bin: How healthcare is responding to the sustainability movement. Healthcare Executive 24 (3): 9–18.

    Google Scholar 

  • Practice Green Health website (2014). Typical categories of Medical Waste, https://practicegreenhealth.org/topics/waste, accessed 1 February 2014.

  • Rosenberg AL, Hofer TP, Hayward RA, Strachan C and Watts CM (2001). Who bounces back? Physiologic and other predictors of intensive care unit readmission. Critical Care Medicine 29 (3): 511–518.

    Article  Google Scholar 

  • Rosenberg AL and Watts C (2000). Patients readmitted to ICUs: A systematic review of risk factors and outcomes. Chest 118 (2): 492–502.

    Article  Google Scholar 

  • Sadler BL et al (2011). Fable hospital 2.0: The business case for building better health care facilities. Hastings Center Report 41 (1): 13–23.

    Article  Google Scholar 

  • Schreyögg J, Tiemann O and Busse R (2006). Cost accounting to determine prices: How well do prices reflect costs in the German DRG-system? Health Care Management Science 9 (3): 269–279.

    Article  Google Scholar 

  • Smith-Daniels VL, Schweikhart SB and Smith-Daniels DE (1988). Capacity management in health care services: Review and future research directions. Decision Sciences 19 (4): 889–919.

    Article  Google Scholar 

  • Solomon NB (2004). Environmentally-friendly building strategies slowly make their way into medical facilities: New guidelines highlight the relationship between sustainable design and human health. Architectural Record 192 (8): 179–188.

    Google Scholar 

  • Weisz U, Haas W, Pelikan JM and Schmied H (2011). Sustainable hospitals: A socio-ecological approach. GAIA 20 (3): 191–198.

    Google Scholar 

  • Zhu Z, Hen BH and Kiok LT (2012). Estimating ICU bed capacity using discrete event simulation. International Journal of Health Care Quality Assurance 25 (2): 134–144.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Petering, M., Aydas, O., Kuzu, K. et al. Simulation analysis of hospital intensive care unit reimbursement policies from the triple bottom line perspective. J Simulation 9, 86–98 (2015). https://doi.org/10.1057/jos.2014.24

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jos.2014.24

Keywords

Navigation