Bed Capacity Planning Using Stochastic Simulation Approach in Cardiac-surgery Department of Teaching Hospitals, Tehran, Iran
-
Amin TORABIPOUR
,
Hojjat ZERAATI
,
Mohammad ARAB
,
Arash RASHIDIAN
,
Ali AKBARI SARI
,
Mahmuod Reza SARZAIEM
Abstract
Background: To determine the hospital required beds using stochastic simulation approach in cardiac surgery departments.
Methods: This study was performed from Mar 2011 to Jul 2012 in three phases: First, collection data from 649 patients in cardiac surgery departments of two large teaching hospitals (in Tehran, Iran). Second, statistical analysis and formulate a multivariate linier regression model to determine factors that affect patient's length of stay. Third, develop a stochastic simulation system (from admission to discharge) based on key parameters to estimate required bed capacity.
Results: Current cardiac surgery department with 33 beds can only admit patients in 90.7% of days. (4535 d) and will be required to over the 33 beds only in 9.3% of days (efficient cut off point). According to simulation method, studied cardiac surgery department will requires 41-52 beds for admission of all patients in the 12 next years. Finally, one-day reduction of length of stay lead to decrease need for two hospital beds annually.
Conclusion: Variation of length of stay and its affecting factors can affect required beds. Statistic and stochastic simulation model are applied and useful methods to estimate and manage hospital beds based on key hospital parameters.
Adan I, Bekkers J, Dellaert N, Vissers J, Yu X (2009). Patient mix optimisation and stochastic resource requirements: A case study in cardiothoracic surgery planning. Health Care Manag Sci 12:129-141.
Kokangul A (2008). A combination of deterministic and stochastic approaches to optimize bed capacity in a hospital unit. Comput Methods Programs Biomed, 90:56-65.
Anonymous (2003). Bed management: review of national findings. First edn. Audit Commission, London p. 40. http://capitadiscovery.co.uk/brighton-ac/items/977604.
Mackay M, Qin S, Clissold A, Hakendorf P, Ben-Tovim D, McDonnell G (2013). Patient flow simulation modelling–an approach conducive to multi-disciplinary collaboration towards hospital capacity management. 20th International Congress on Modelling and Simulation, Adelaide, Australia p. 134.
Atienza N, Garcia-Heras J, Munoz-Pichardo JM, Villa R (2008). An application of mixture distributions in modelization of length of hospital stay. Stat Med, 27:1403-20.
Conrad D, Wickizer T, Maynard C, Klastorin T, Lessler D, Ross A, Soderstrom N, Sullivan S, Alexander J, Travis K (1996). Managing care, incentives, and information: an exploratory look inside the "black box" of hospital efficiency. Health Serv Res, 31:235-59.
Reed T, Veith FJ, Gargiulo NJ, Timaran CH, Ohki T, Lipsitz EC, Malas MB, Wain RA, Suggs WD (2004). System to decrease length of stay for vascular surgery. J Vasc Surg, 39:395-9.
Skillman JJ, Paras C, Rosen M, Davis RB, Kim D, Kent KC (2000). Improving cost efficiency on a vascular surgery service. Am J Surg, 179:197-200.
Marazzi A, Paccaud F, Ruffieux C, Beguin C (1998). Fitting the distributions of length of stay by parametric models. Med Care, 36:915-27.
Lapierre SD, Goldsman D, Cochran R, DuBow J (1999). Bed allocation techniques based on census data. Socio-Economic Plan Sci, 33:25-38.
Gunal MM (2012). A guide for building hospital simulation models. Health Sys, 1(1):17-25.
Bahadori M, Mohammadnejhad SM, Ravangard R, Teymourzadeh E (2014). Using Queuing Theory and Simulation Model to Optimize Hospital Pharmacy Performance. Iran Red Crescent Med J, 16:e16807.
Ahmed MA, Alkhamis TM (2009). Simulation optimization for an emergency department healthcare unit in Kuwait. Eur J of Oper Res, 198:936-942.
Hancock WM, Walter PF (1979). The use of computer simulation to develop hospital systems. SIGSIM Simul Dig, 10:28-32.
Speybroeck N, Van Malderen C, Harper S, Müller B, Devleesschauwer B (2013). Simulation Models for Socioeconomic Inequalities in Health: A Systematic Review. Int J Environ Res and Public Health, 10:5750-5780.
Holm LB, Luras H, Dahl FA (2013). 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 Inform, 82:80-9.
Henderson SG, Nelson BL (2006). Hand-books in operations research and management sci-ence: simulation. Elsevier, Amsterdam, pp. 193-223.
Zai AH, Farr KM, Grant RW, Mort E, Ferris TG, Chueh HC (2009). Queuing theory to guide the implementation of a heart failure inpatient Registry Program. J Am Med Inform Assoc: JAMIA, 16:516-523.
McDonagh MS, Smith DH, Goddard M (2000). Measuring appropriate use of acute beds. A systematic review of methods and results. Health Policy, 53:157-84.
Zeraati H, Zayeri F, Babaee G, Khanafshar N, Ramezanzadeh F (2005). Required hospital beds estimation: a simulation study. J Applied Sci, 5:1189-1191.
Weinberg J (1995). The impact of ageing upon the need for medical beds: a Monte-Carlo simulation. J Public Health Med, 17:290-6.
Nguyen J, Six P, Antonioli D, Glemain P, Potel G, Lombrail P, Le Beux P (2005). A simple method to optimize hospital beds capacity. Int J Med Inform, 74:39-49.
Lee AH, Fung WK, Fu B (2003). Analyzing hospital length of stay: mean or median regression? Med Care, 41:681-6.
Xiao J, Douglas D, Lee AH, Vemuri SR (1997). A Delphi evaluation of the factors influencing length of stay in Australian hospitals. Int J Health Plann Manag, 12:207-18.
Cwynar R, Albert NM, Butler R, Hall C (2009). Factors associated with long hospital length of stay in patients receiving warfarin after cardiac surgery. J Cardiovasc Nurs, 24:465-74.
Khairudin Z (2012). Determinants of Prolonged Stay after Coronary Artery Bypass Graft Surgery. Procedia - Soci and Behavioral Sci, 36:87-95.
Weintraub WS, Jones EL, Craver J, Guyton R, Cohen C (1989). Determinants of prolonged length of hospital stay after coronary bypass surgery. Circulation, 80:276-84.
De Cocker J, Messaoudi N, Stockman BA, Bossaert LL, Rodrigus IE (2011). Preoperative prediction of intensive care unit stay following cardiac surgery. Eur J Cardiothorac Surg, 39:60-7.
Nickerson NJ, Murphy SF, Davila-Roman VG, Schechtman KB, Kouchoukos NT (1999). Obstacles to early discharge after cardiac surgery. Am J Manag Care, 5:29-34.
Hirose H, Amano A, Yoshida S, Takahashi A, Nagano N, Kohmoto T (2000). Coronary artery bypass grafting in the elderly. Chest, 117:1262-1270.
Tajvar M, Arab M, Montazeri A (2008). Determinants of health-related quality of life in elderly in Tehran, Iran. BMC Public Health, 8:323-323.
Kroneman M, Siegers JJ (2004). The effect of hospital bed reduction on the use of beds: A comparative study of 10 European countries. Soc Sci Med., 59:1731-1740.
Delamater PL, Messina JP, Grady SC, WinklerPrins V, Shortridge AM (2013). Do More Hospital Beds Lead to Higher Hospitalization Rates? A Spatial Examination of Roemer’s Law. PLoS ONE, 8:e54900.
Green LV (2002). How many hospital beds? Inquiry, 39:400-12.
Akkerman R, Knip M (2004). Reallocation of beds to reduce waiting time for cardiac surgery. Health Care Manag Sci, 7:119-26.
| Files | ||
| Issue | Vol 45 No 9 (2016) | |
| Section | Original Article(s) | |
| Keywords | ||
| Hospital beds Cardiac surgery department Hospital stay Stochastic simulation | ||
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