This paper is devoted to the design and analysis of the gastroenterology (GI) clinic in the Digestive Health Center (DHC) of University of Wisconsin Health. The DHC will consolidate several existing clinic and endoscopy locations into a single center. First, the work flow at a current GI clinic is studied. A Markov chain model is developed and then extended to non-Markovian case to evaluate patient average length of stay and staff utilization. The model is validated by the data observed in the clinic. It is shown that the model can provide accurate estimation of system performance. Then, using such a model, design options of the new GI clinic in the DHC are studied. To investigate the impact of different system configurations, what-if analyses are carried out and different patient check-out processes are investigated. Finally, recommendations for enhancing service at the new GI clinic are proposed to the DHC leadership.
Gastroenterology (GI) Patient flow Markov chain Length of stay Staff utilization Check-out process
This is a preview of subscription content, log in to check access.
The authors thank L. Woodhouse and other staff of University of Wisconsin Medical Foundation and G. Eaton, G. Tomas, A. Velando and S. Wirsbinski of University of Wisconsin - Madison for their help in the project. This work is supported in part by NSF Grant No. CMMI-1233807 and NSFC Grant No. 71301003
Brailsford SC (2007) Advances and challenges in healthcare simulation modeling: tutorial. In: Proceedings of the 39th winter simulation conference, pp 1436–1448Google Scholar
Cono M, Dawson KA (1993) Determining the size of the gastroenterology division expansion using simulation: a case study. Proc Ann HIMSS Conf 2:127–137Google Scholar
Fomundam S, Herrmann JW A survey of queuing theory applications in healthcare. Technical report, University of Maryland, College Park, MDGoogle Scholar
Gershwin SB (1993) Variance of output of a tandem production system. In: Onvaral RO, Akyildi IF (eds) Queuing networks with finite capacity. Elsevier, AmsterdamGoogle Scholar
Godfrey K (1983) Compartmental models and their application. Academic Press, LondonGoogle Scholar
Green LV, Soares J, Giglio JF, Green RA (2006) Using queueing theory to increase the effectiveness of emergency department provider staffing. Acad Emerg Med 13:61–68CrossRefGoogle Scholar
Harrison GW (1994) Compartmental models of hospital patient occupancy patterns. In: McClean SI, Millard PH (eds) Modelling hospital resource use: a different apporach to the planning and control of health care systems. Royal Society of Medicine, London, pp 53–64Google Scholar
Irvine V, McClean SI, Millard PH (1994) Stochastic models for geriatric in-patient behaviour. IMA J Math Appl Med Biol 11:207–216CrossRefMATHGoogle Scholar
Jacobson SH, Hall SN, Swisher JR (2006) Discrete-event simulation of health care systems. In: Hall RW (eds) Patient flow: reducing delay in healthcare delivery. International series in operations research and management science, vol 91, pp 211–252Google Scholar