Abstract
This chapter examines the barriers to, and opportunities for, improving the planning and scheduling of complex outpatient care environments. First, this chapter explores the effects of two phenomena—complexity and uncertainty—that exacerbate operational challenges for clinics. Mitigating the effects of these phenomena is motivation for proposing a framework for managing clinic operations more effectively. By collecting historical data, and using it effectively, staff are better able to create realistic plans for upcoming clinics. The chapter reviews some of the analytical techniques that can be used to then improve that plan, including an example of a mixed-integer program for determining the optimal sequence of clinical activities. Once the clinic session has begun, the nature of the decisions being made in real time is more tactical, but similar analytical approaches may be useful. Combined with powerful information technology, a proposed analytical and coordination framework can help guide systems improvement efforts. The chapter then concludes with discussion of several opportunities for future research on methods for improving decision-making in clinical operations management.
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Notes
- 1.
The term “optimization” is used here to include both models/systems that produce truly optimal solutions as well as those that may rely on other methods, such as heuristics, to generate optimal or near-optimal solutions.
References
Baker KR, Trietsch D (2009) Safe scheduling: setting due dates in single-machine problems. Eur J Oper Res 196(1):69–77
Baker KR, Trietsch D (2009) Principles of sequencing and scheduling. Wiley, New York
Bazzoli GJ, Brewster LR, Liu G, Kuo S (2003) Does U.S. hospital capacity need to be expanded? Health Aff 22(6):40–54
Berry LL, Seltman KD (2008) Management lessons from Mayo Clinic. McGraw-Hill, New York
Camm J, Magazine M, Polak G, Zaric G (2008) Scheduling parallel assembly lines to minimize a shared pool of labor. IIE Trans 40(8):749–758
Cardoen B, Demeulemeester E, Belien J (2010) Operating room planning and scheduling: a literature review. Eur J Oper Res 201(3):921–932
Cayirli T, Veral E (2003) Outpatient scheduling in health care: a review of the literature. Prod Oper Manag 12(4):519–549
Chand S, Moskowitz H, Norris JB, Shade S, Willis DR (2009) Improving patient flow at an outpatient clinic: study of sources of variability and improvement factors. Health Care Manag Sci 12(3):325–340
Dobson G, Hasija S, Pinker EJ (2011) Reserving capacity for urgent patients in primary care. Prod Oper Manag 20(3):456–473
Fiore K (2010) Living room is new ED waiting room. MedPage Today. http://www.medpagetoday.com/EmergencyMedicine/EmergencyMedicine/23315. Accessed 12 May 2011
Fisher ML, Raman A (1996) Reducing the cost of demand uncertainty through accurate response to early sales. Oper Res 44(1):87–99
Froehle CM, Platt M, Magazine MJ (2011) Optimal outpatient clinic scheduling and rescheduling. University of Cincinnati working paper
Gul S, Denton BT, Fowler JW, Huschka T (2011) Bi-criteria scheduling of surgical services for an outpatient procedure center. Prod Oper Manag 20(3):406–417
Gupta D, Denton BT (2008) Appointment scheduling in health care: challenges and opportunities. IIE Trans 40:800–819
Hall RW (2008) Patient flow: the new queueing theory for health care. OR/MS Today 33:36–40
Harper PR, Gamlin HM (2003) Reduced outpatient waiting times with improved appointment scheduling: a simulation modelling approach. OR Spectr 25:207–222
Ho C-J (1989) Evaluating the impact of operating environments on MRP system nervousness. Int J Prod Res 27(7):1115–1135
LaGanga L (2011) Lean service operations: reflections and new directions for capacity expansion in outpatient clinics. J Oper Manag 29(5):422–433
Li Z, Ierapetritou M (2008) Process scheduling under uncertainty: review and challenges. Comput Chem Eng 32(4–5):715–727
Litvak E (2005) Optimizing patient flow by managing its variability. In: Berman S (ed) Front office to front line: essential issues for health care leaders. Joint Commission Resources, Oakbrook Terrace, pp 91–111
Makridakis S, Winkler RL (1983) Averages of forecasts: some empirical results. Manag Sci 29(9):987–996
Minella G, Ruiz R, Ciavotta M (2008) A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. INFORMS J Comput 20(3):451–471
Murray M, Berwick DM (2003) Advanced access: reducing waiting and delays in primary care. JAMA 289(8):1035–1040
Peterson CL, Burton R (2007) U.S. health care spending: comparison with other OECD countries. CRS Reports for Congress, Congressional Research Service, Washington, DC. http://assets.opencrs.com/rpts/RL34175_20070917.pdf. Accessed 25 Aug 2011
Pham D-N, Klinkert A (2008) Surgical case scheduling as a generalized job shop scheduling problem. Eur J Oper Res 185(3):1011–1025
Pinedo M (2009) Planning and scheduling in manufacturing and services. Springer, New York
Salzarulo PA, Bretthauer KM, Côté MJ, Schultz KL (2011) The impact of variability and patient information on health care system performance. Prod Oper Manag 20(6):848–859
StateHealthFacts.org (2011) www.statehealthfacts.org/comparetrend.jsp?cat=8&sort=a&sub=94&typ=1&yr=63&ind=392&srgn=1. Accessed 18 Jan 2011
White D, Froehle CM, Klassen KJ (2011a) The effect of integrated scheduling and capacity policies on clinical efficiency. Prod Oper Manag 20(3):442–455
White D, Magazine M, Morelli S (2012) Who’s next: using patient analysis and operations management to address non-punctual and no-show patients. University of Cincinnati working paper
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Froehle, C.M., Magazine, M.J. (2013). Improving Scheduling and Flow in Complex Outpatient Clinics. In: Denton, B. (eds) Handbook of Healthcare Operations Management. International Series in Operations Research & Management Science, vol 184. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5885-2_9
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