Many jurisdictions are facing increased demand for intensive care. There are two long-term investment options: intensive care unit (ICU) versus step-down or intermediate care unit (IMCU) capacity expansion. Relative cost-effectiveness of the two investment strategies with regard to patient lives saved has not been studied to date.
Methods:
We expand a generic system dynamics simulation model of emergency patient flow in a typical hospital, populated with empirical evidence found in the medical and hospital administration literature, to estimate the long-term effects of expanding ICU versus IMCU beds on patient lives saved under a common assumption of 2.1% annual increase in hospital arrivals. Two alternative policies of expanding ICU by two beds versus introducing a two-bed IMCU are compared over a ten-year simulation period. Russel equation is used to calculate total cost of patients’ hospitalization. Using two possible values for the ratio of ICU to IMCU cost per inpatient day and four possible values for the percentage of patients transferred from ICU to IMCU found in the literature, nine scenarios are compared against the baseline scenario of no capacity expansion.
Results:
Expanding ICU capacity by two beds is demonstrated as the most cost-effective scenario with an incremental cost-effectiveness ratio of 3684 (US $) per life saved against the baseline scenario. Sensitivity analyses on the mortality rate of patients in IMCU, direct transfer of IMCU-destined patients to the ward upon completing required IMCU length of stay in the ICU, admission of IMCU patient to ICU, adding two ward beds, and changes in hospital size do not change the superiority of ICU expansion over other scenarios.
Conclusions:
In terms of operational costs, ICU beds are more cost effective for saving patients than IMCU beds. However, capital costs of setting up ICU versus IMCU beds should be considered for a complete economic analysis.
HerringAWilperAHimmelsteinDU, et al.Increasing length of stay among adult visits to US emergency departments, 2001-2005. Acad Emerg Med. 2009;16(7):609–616.
5.
HalpernNAPastoresSM. Critical care medicine in the United States 2000-2005: an analysis of bed numbers, occupancy rates, payer mix, and costs. Crit Care Med. 2010;38(1):65–71.
6.
WunschHAngusDCHarrisonDA, et al.Variation in critical care services across North America and Western Europe. Crit Care Med. 2008;36(10):2787–2793.
7.
SimchenESprungCLGalaiN, et al.Survival of critically ill patients hospitalized in and out of intensive care units under paucity of intensive care unit beds. Crit Care Med. 2004;32(8):1654–1661.
8.
LevinPDWornerTMSviriS, et al.Intensive care outflow limitation: frequency, etiology, and impact. J Crit Care. 2003;18(4):206–211.
9.
MoskopJCSklarDPGeidermanJMSchearsRMBookmanKJ. Emergency department crowding, part 1—concept, causes, and moral consequences. Ann Emerg Med. 2009;53(5):605–611.
10.
YoungMPGooderVJMcBrideKJamesBFisherES. Inpatient transfers to the intensive care unit: delays are associated with increased mortality and morbidity. J Gen Intern Med. 2003;18(2):77–83.
11.
ChalfinDBTrzeciakSLikourezosABaumannBMDellingerRP. Impact of delayed transfer of critically ill patients from the emergency department to the intensive care unit. Crit Care Med. 2007;35(6):1477–1483.
12.
OrtigaBSalazarAJovellAEscarrabillJMarcaGCorbellaX. Standardizing admission and discharge processes to improve patient flow: a cross sectional study. BMC Health Serv Res. 2012;12:180.
13.
ChanHLoSLeeL, et al.Lean techniques for the improvement of patients’ flow in emergency department. World J Emerg Med. 2014;5(1):24–28.
14.
DicksonEWSinghSCheungDSWyattCCNugentAS. Application of Lean manufacturing techniques in the emergency department. J Emerg Med. 2009;37(2):177–182.
15.
HoldenRJ. Lean thinking in emergency departments: a critical review. Ann Emerg Med. 2011;57(3):265–278.
16.
WagnerJGablerNBRatcliffeSJBrownSEStromBLHalpernSD. Outcomes among patients discharged from busy intensive care units. Ann Intern Med. 2013;159(7):447–455.
17.
AndersonDPriceCGoldenBJankWWasilE. Examining the discharge practices of surgeons at a large medical center. Health Care Manag Sci. 2011;14(4):338–347.
18.
DalyKBealeRChangRW. Reduction in mortality after inappropriate early discharge from intensive care unit: logistic regression triage model. BMJ. 2001;322(7297):1274–1276.
19.
GoldfradCRowanK. Consequences of discharges from intensive care at night. Lancet. 2000;355(9210):1138–1142.
20.
CardosoLTGrionCMMatsuoT, et al.Impact of delayed admission to intensive care units on mortality of critically ill patients: a cohort study. Crit Care. 2011;15(1):R28.
21.
Mahmoudian-DehkordiASadatS. Sustaining critical care: using evidence-based simulation to evaluate ICU management policies. Health Care Manag Sci. 2016. doi:10.1007/s10729-016-9369-z.
22.
LyonsRAWarehamKHutchingsHAMajorEFergusonB. Population requirement for adult critical care beds: a prospective quantitative and qualitative study. Lancet. 2000;355(9204):595–598.
23.
HardingAD. What can an intermediate care unit do for you?J Nurs Adm. 2009;39(1):4–7.
24.
StacyKM.Progressive care units: different but the same. Crit Care Nurse. 2011;31(3):77–83.
25.
ForresterJW. Industrial Dynamics. Cambridge, MA: MIT Press; 1961.
26.
VanderbySCarterMW. An evaluation of the applicability of system dynamics to patient flow modelling. J Oper Res Soc. 2009;61(11):1572–1581.
27.
LaneDCHusemannE. System dynamics mapping of acute patient flows. J Oper Res Soc. 2008;59(2):213–224.
28.
LattimerVBrailsfordSTurnbullJ, et al.Reviewing emergency care systems I: insights from system dynamics modelling. Emerg Med J. 2004;21(6):685–691.
29.
BrailsfordSCLattimerVATarnarasPTurnbullJC. Emergency and on-demand health care: model-ling a large complex system. J Oper Res Soc. 2004;55(1):34–42.
30.
WolstenholmeEF. A patient flow perspective of UK Health Services: exploring the case for new ‘intermediate care’ initiatives. Syst Dyn Rev. 1999;15(3):253–271.
31.
PaulSAReddyMCDeFlitchCJ. A systematic review of simulation studies investigating emergency department overcrowding. Simulation. 2010;86(8-9):559–571.
32.
SobolevBGSanchezVVasilakisC. Systematic review of the use of computer simulation modeling of patient flow in surgical care. J Med Syst. 2011;35(1):1–16.
33.
BrailsfordSC. Tutorial: Advances and Challenges in Healthcare Simulation Modeling. In: Proceedings of the 2007 Winter Simulation Conference; Washington, DC, 9–12 December 2007;1436–1448. USA: IEEE.
34.
MarshallDABurgos-LizLIJzermanMJ, et al.Selecting a dynamic simulation modeling method for health care delivery research—part 2: report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force. Value Health. 2015;18(2):147–160.
35.
MarshallDABurgos-LizLIJzermanMJ, et al.Applying dynamic simulation modeling methods in health care delivery research—The SIMULATE checklist: report of the ISPOR Simulation Modeling Emerging Good Practices Task Force. Value Health. 2015;18(1):5–16.
36.
StermanJ. Learning from evidence in a complex world. Am J Public Health. 2006;96(3):505–514.
37.
StermanJ. Business Dynamics: Systems Thinking and Modeling for a Complex World. New York, NY: McGraw-Hill; 2000.
38.
OlshakerJSRathlevNK. Emergency department overcrowding and ambulance diversion: the impact and potential solutions of extended boarding of admitted patients in the emergency department. J Emerg Med. 2006;30(3):351–356.
39.
HootNRAronskyD. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med. 2008;52(2):126–136.
40.
FletcherAWorthingtonD. What is a ‘generic’ hospital model?—a comparison of ‘generic’ and ‘specific’ hospital models of emergency patient flows. Health Care Manag Sci. 2009;12(4):374–391.
41.
PrinMWunschH. The role of stepdown beds in hospital care. Am J Respir Crit Care Med. 2014;190(11):1210–1216.
42.
RahmandadHStermanJD. Reporting guidelines for simulation-based research in social sciences. Syst Dyn Rev. 2012;28(4):396–411.
43.
McCarthyMLAronskyDJonesID, et al.The emergency department occupancy rate: a simple measure of emergency department crowding?Ann Emerg Med. 2008;51(1):15–24.
44.
SchneiderSMGalleryMESchafermeyerRZwemerFL. Emergency department crowding: a point in time. Ann Emerg Med. 2003;42(2):167–172.
45.
PronovostPJNeedhamDMWatersH, et al.Intensive care unit physician staffing: financial modelling of the Leapfrog standard. Crit Care Med. 2004;32(6):1247–1253.
46.
HosseinifardSAbbasiBMinasJ. Intensive care unit discharge policies prior to treatment completion. Oper Res Health Care. 2014;3(3):168–175.
47.
de BruinABekkerRvan ZantenLKooleG. Dimensioning hospital wards using the Erlang loss model. Ann Oper Res. 2010;178(1):23–43.
48.
MDA Williams. Developing a System Resilience Approach to the Improvement of Patient Safety in NHS Hospitals. England: University of Exeter. 2011.
49.
HalpernNAPastoresSMOropelloJMKvetanV. Critical care medicine in the United States: addressing the intensivist shortage and image of the specialty. Crit Care Med. 2013;41(12):2754–2761.
50.
BellomoRGoldsmithDUchinoS, et al.A before and after trial of the effect of a high-dependency unit on post-operative morbidity and mortality. Crit Care Resusc. 2005;7(1):16–21.
51.
FranklinCMRackowECMamdaniBNightingaleSBurkeGWeilMH. Decreases in mortality on a large urban medical service by facilitating access to critical care. An alternative to rationing. Arch Intern Med. 1988;148(6):1403–1405.
52.
SolbergBCDirksenCDNiemanFH, et al.Introducing an integrated intermediate care unit improves ICU utilization: a prospective intervention study. BMC Anesthesiol. 2014;14:76.
53.
BeckDHMcQuillanPJSmithGB. Waiting for the break of dawn? The effects of discharge TISS scores, discharge time and discharge facilities on mortality after intensive care. Intensive Care Med. 2002;28(9):1287–1293.
54.
RanzaniOTZampieriFGTaniguchiLUForteDNAzevedoLCParkM. The effects of discharge to an intermediate care unit after a critical illness: a 5-year cohort study. J Crit Care. 2014;29(2):230–235.
55.
ZimmermanJEWagnerDPKnausWAWilliamsJFKolakowskiDDrapperEA. The use of risk predictions to identify candidates for intermediate care units. Chest. 1995;108(2):490–499.
56.
MilbrandtEBKerstenARahimMT, et al.Growth of intensive care unit resource use and its estimated cost in Medicare. Crit Care Med. 2008;36(9):2504–2510.
57.
HalpernNAPastoresSM. Understanding the Russell equation and projection estimates to describe critical care costs in the USA. Intensive Care Med. 2015;41(10):1828–1830.
58.
PastoresSMDakwarJHalpernNA. Costs of critical care medicine. Crit Care Clin. 2012;28(1):1–10.
59.
SolbergBCDirksenCDNiemanFHvan MerodeGPoezeMRamsayG. Changes in hospital costs after introducing an intermediate care unit: a comparative observational study. Crit Care. 2008;12(3):R68.
60.
PorathAReuveniHGrinbergGLiebermanD. The intermediate care unit as a cost-effective option for the treatment of medical patients in critical condition. Isr J Med Sci. 1995;31(11):674–680.
61.
CadyNMattesMBurtonS. Reducing intensive care unit length of stay. A stepdown unit for first-day heart surgery patients. J Nurs Adm. 1995;25(12):29–35.
62.
WunschHHarrisonDAJonesARowanK. The impact of the organization of high-dependency care on acute hospital mortality and patient flow for critically ill patients. Am J Respir Crit Care Med. 2015;191(2):186–193.
63.
CapuzzoMVoltaCTassinatiT, et al.Hospital mortality of adults admitted to intensive care units in hospitals with and without intermediate care units: a multicentre European cohort study. Crit Care. 2014;18(5):551.
64.
WildCNarathM. Evaluating and planning ICUs: methods and approaches to differentiate between need and demand. Health Policy. 2005;71(3):289–301.
65.
ChatterjeeSLevinCLaxminarayanR. Unit cost of medical services at different hospitals in India. PLoS One. 2013;8:e69728.
66.
WedigGHassanMSloanF. Hospital investment decisions and the cost of capital. J Bus. 1989;62(4):517–536.
67.
RobertRReignierJTournoux-FaconC, et al.Influence of ICU-bed availability on ICU admission decisions. Ann Intensive Care. 2015;5(1):55.
68.
JengJJAnL. System Dynamics Modeling for SOA Project Management. In: SOCA ‘07: Proceedings of the IEEE International Conference on Service-Oriented Computing and Applications; Washington, DC: IEEE Computer Society; 2007:286–292.
69.
StermanJD. System Dynamics Modeling for Project Management, Technical Report. Cambridge, MA: MIT Sloan School of Management; 1992.
70.
GeorgiadisPVlachosDIakovouE. A system dynamics modeling framework for the strategic supply chain management of food chains. J Food Eng. 2005;70(3):351–364.
71.
MinegishiSThielD. System dynamics modeling and simulation of a particular food supply chain. Simul Pract Theory. 2000;8(5):321–339.
72.
TobiasMICavanaRYBloomfieldA. Application of a system dynamics model to inform investment in smoking cessation services in New Zealand. Am J Public Health. 2010;100(7):1274–1281.
73.
RobertsEBHomerJKasabianAVarrellM. A systems view of the smoking problem: perspective and limitations of the role of science in decision-making. Int J Biomed Comput. 1982;13(1):69–86.
74.
TengsTOOsgoodNDChenLL. The cost-effectiveness of intensive national school-based anti-tobacco education: results from the tobacco policy model. Prev Med. 2001;33(6):558–570.
75.
HomerJHirschGMinnitiMPiersonM. Models for collaboration: how system dynamics helped a community organize cost-effective care for chronic illness. System Dynamics Rev. 2004;20(3):199–222.
76.
RobertsCDangerfieldB. Modelling the epidemiological consequences of HIV infection and AIDS: a contribution from operational research. J Oper Res Soc. 1990;41(4):273–289.
77.
DangerfieldBFangYRobertsC. Model-based scenarios for the epidemiology of HIV/AIDS: the consequences of highly active antiretroviral therapy. Syst Dyn Rev. 2001;17(2):119–150.
78.
RoystonGDostATownshendJTurnerH. Using system dynamics to help develop and implement policies and programmes in health care in England. Syst Dyn Rev. 1999;15(3):293–313.
79.
HomerJRitchie-DunhamJRabbinoHPuenteLMJorgensenJHendricksK. Toward a dynamic theory of antibiotic resistance. Syst Dyn Rev. 2000;16(4):287–319.
80.
ShepherdSP. A review of system dynamics models applied in transportation. Transp B Transp Dyn. 2014;2(2):83–105.
81.
MillingPStumpfeJ. Product and Process Innovation, A System Dynamics-Based Analysis of the Interdependencies. Paper presented at: The 18th International Conference of The System Dynamics Society; August 6-10, 2000; Bergen, Norway.
82.
StermanJBoothSL. Cloudy skies: assessing public understanding of global warming. Syst Dyn Rev. 2002;18(2):207–240.
83.
GuoHCLiuLHuangGHFullerGAZouRYinYY. A system dynamics approach for regional environmental planning and management: a study for the Lake Erhai Basin. J Environ Manag. 2001;61(1):93–111.
84.
DysonBChangN. Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling. Waste Manag. 2005;25(7):669–679.
85.
SufianMABalaBK. Modeling of urban solid waste management system: the case of Dhaka city. Waste Manag. 2007;27(7):858–868.
86.
ChaerulMTanakaMAshokVShekdarAV. A system dynamics approach for hospital waste management. Waste Manag. 2008;28(2):442–449.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
