Abstract
The ability to accurately measure and assess current and potential health care system capacities is an issue of local and national significance. Recent joint statements by the Institute of Medicine and the Agency for Healthcare Research and Quality have emphasized the need to apply industrial and systems engineering principles to improving health care quality and patient safety outcomes. To address this need, a decision support tool was developed for planning and budgeting of current and future bed capacity, and evaluating potential process improvement efforts. The Strategic Bed Analysis Model (StratBAM) is a discrete-event simulation model created after a thorough analysis of patient flow and data from Geisinger Health System’s (GHS) electronic health records. Key inputs include: timing, quantity and category of patient arrivals and discharges; unit-level length of care; patient paths; and projected patient volume and length of stay. Key outputs include: admission wait time by arrival source and receiving unit, and occupancy rates. Electronic health records were used to estimate parameters for probability distributions and to build empirical distributions for unit-level length of care and for patient paths. Validation of the simulation model against GHS operational data confirmed its ability to model real-world data consistently and accurately. StratBAM was successfully used to evaluate the system impact of forecasted patient volumes and length of stay in terms of patient wait times, occupancy rates, and cost. The model is generalizable and can be appropriately scaled for larger and smaller health care settings.
Similar content being viewed by others
References
Reid, P. R., Compton, W. D., Grossman, J. H., and Faniang, G., Building a better delivery system: a new engineering health care partnership. The National Academies Press, Washington, 2005.
IOM, Best care at lower cost: The path to continuously learning health care in America technical report the institute of medicine. National Academies Press, Washington, 2012.
Valdez, R. S., Ramly, E., and Brennan, P. F., Industrial and systems engineering and health care: Critical areas of research–final report (prepared by professional and scientific associates under contract no. 290-09-00027u). Technical report, AHRQ: Agency for Healthcare Research and Quality, Rockville, 2010.
JCAHO, R3 report issue 4 - patient flow through the emergency department. Technical report, Joint Commission. 2012 http://www.jointcommission.org/assets/1/18/R3_Report_Issue_4.pdf. Accessed October 2014.
Bagust, A., Place, M., and Posnett, J. W., Dynamics of bed use in accommodating emergency admissions. BMJ 319:155–158, 1999. doi:10.1136/bmj.319.7203.155.
Green, L. V., and Nguyen, V., Strategies for cutting hospital beds: The impact on patient service. Health Serv. Res. 36:421–442, 2001.
Green, L. V., How many hospital beds? Inquiry 39:400–412, 2002. doi:10.5034/inquiryjrnl_39.4.400.
Bain, C. A., Taylor, P. G., McDonnell, G., and Georgiou, A., Myths of ideal hospital occupancy. Med. J. Aust. 192:42–43, 2010.
Freeman, R. K., and Poland, R. L., Guidelines for perinatal care, 4th edition. D.C. American College of Obstetricians and Gynecologists, Washington, 1997.
Jun, J., Jacobsen, S., and Swisher, J. R., Application of discrete-event simulation in health care clinics: A survey. J. Oper. Res. Soc. 50:109–123, 1999. doi:10.1057/palgrave.jors.2600669.
Jacobsen, S., Hall, S., and Swisher, J. R., Discrete-event simulation of health care systems. In: Hall, R. (Ed.), Patient flow: Reducing delay in healthcare delivery, international series in operations research and management science, vol. 91. Springer, New York, pp. 211–252, 2006.
Marshall, A., Vasilakis, C., and Elia, E., Length of stay-based patient flow models: Recent developments and future directions. Health Care Manag Sci 8:213–220, 2005. doi:10.1007/s10729-005-2012-z.
Fomundam, S., and Herrmann, J. W., A survey of queuing theory applications in healthcare. Technical report 24. University of Maryland, College Park, 2007.
Gorunescu, F., McClean, S. I., Millar, P. H., A queueing model for bed-occupancy management and planning of hospitals. J. Oper. Res. Soc. 53(1). 2002 10.1057/palgrave/jors/2601244
Palvannan, R. K., and Teow, K. L., Queueing for healthcare. J. Med. Syst. 36:541–547, 2012. doi:10.1007/s10916-010-9499-7.
Sobolev, B. G., Sanchez, V., and Vasilakis, C., Systematic review of the use of computer simulation modeling of patient flow in surgical care. J. Med. Syst. 35:1–16, 2011. doi:10.1007/s10916-009-9336-z.
Kolker, A., Process modeling of emergency department patient flow: effect of patient length of stay on ED diversion. J. Med. Syst. 32:389–401, 2008.
Kolker, A., Process modeling of ICU patient flow: effect of daily load leveling of elective surgeries on ICU diversion. J. Med. Syst. 33:27–40, 2009.
Bair, A. E., Song, W. T., Chen, Y. C., and Morris, B. A., The impact of inpatient boarding on ED efficiency: A discrete-event simulation study. J. Med. Syst. 34:919–929, 2010. doi:10.1007/s10916-009-9307-4.
Strömblad, C. T, Devapriya, D. P., Modeling time-dependent patient arrivals in hospital simulation models. Proceedings of the 2012 Industrial and Systems Engineering Research Conference. Eds. Lim G and Herrmann JW, Montreal, 2012
Griffin, J., Xia, S., Peng, S., and Keskinocak, P., Improving patient flow in an obstetric unit. Health Care Manag Sci 15:1–14, 2012. doi:10.1007/s10729-011-9175-6.
Barado, J., Guergué, J. M., Esparza, L., Azcárate, C., Mallor, F., and Ochoa, S., A mathematical model for simulating daily bed occupancy in an intensive care unit. Crit. Care Med. 40:1098–1104, 2012. doi:10.1097/CCM.0b013e3182374828.
Cohen, M. A., Hershey, J. C., and Weiss, E. N., Analysis of capacity decisions for progressive patient care hospital facilities. Health Serv. Res. 15:145–160, 1980.
Acknowledgments
We thank Ronald Dravenstott, Stephen Gower, Matt Kolinovsky, Eric Reich, and Nathan Stoudt for their contributions in gathering data and expanding the application of the simulation model. We would like to acknowledge the contribution of a medical writer, Sandy Field, PhD, to the preparation of this manuscript.
Conflict of Interest
The authors have no conflicts of interest to disclose.
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is part of the Topical Collection on Systems-Level Quality Improvement
Rights and permissions
About this article
Cite this article
Devapriya, P., Strömblad, C.T.B., Bailey, M.D. et al. StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions. J Med Syst 39, 130 (2015). https://doi.org/10.1007/s10916-015-0325-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-015-0325-0