A multi-class queuing network analysis methodology for improving hospital emergency department performance

Computers and Operations Research

Volumn 36, Issue 5, 2009, Pages 1497-1512

  Cochran, Jeffery K ^a   Roche, Kevin T ^b  

^a AIR FORCE INSTITUTE OF TECHNOLOGY   (United States)

^b Arizona State University   (United States)

Author keywords

Capacity planning; Emergency department; Hospitals; Multi class queuing networks

Indexed keywords

ELECTRIC NETWORK ANALYSIS; EMERGENCY ROOMS; GRAPH THEORY; HOSPITALS; POPULATION STATISTICS; PROBABILITY DISTRIBUTIONS; QUEUEING NETWORKS; QUEUEING THEORY;

ARRIVAL PATTERNS; CAPACITY PLANNING; COMPUTATIONAL MODELS; DATA ELEMENTS; EMERGENCY DEPARTMENT; EMERGENCY DEPARTMENTS; HOSPITAL EMERGENCY DEPARTMENTS; MULTI-CLASS QUEUING NETWORKS; OPERATIONAL RESEARCHES; OVERFLOW PROBABILITIES; POPULATION GROWTHS; PUBLIC HEALTHS; QUEUING NETWORK MODELS; QUEUING NETWORKS; REAL TIMES; SEASONAL PEAKS; SERVICE TIME DISTRIBUTIONS; WAIT TIMES; WAITING TIMES;

QUALITY OF SERVICE;

EID: 55749106201     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2008.02.004     Document Type: Article

References (27)

1. Wilson MJ, Nguyen K. Bursting at the seams. Urgent Matters report. available at 〈info@urgentmatters.org〉; 2004.
2. Goodacre S., and Webster A. Who waits the longest in the emergency department and who leaves without being seen?. Emergency Medical Journal 22 (2005) 93-96
3. Rowe B.H., Channan P., Bullard M., Blitz S., Saunders D., Rosychuk R.J., et al. Characteristics of patients who leave emergency departments without being seen. Academic Emergency Medicine 13 8 (2006) 848-852
4. Solberg L.I., Asplin B.R., Weinick R.M., and Magid D.J. Emergency department crowding: consensus of potential measures. Annals of Emergency Medicine 42 6 (2003) 824-834
5. Jun J.B., Jacobson S.H., and Swisher J.R. Application of discrete-event simulation in healthcare: a survey. Journal of the Operational Research Society 50 (1999) 109-123
6. Kao E.P.C., and Tung G.G. Bed allocation in a public health care delivery system. Management Science 27 5 (1981) 507-520
7. Panayiotopoulos J.C., and Vassilacopoulos G. Simulating hospital emergency departments queuing systems: (GI / G / m (t)) : (IHFF / N / ∞). European Journal of Operational Research 18 (1984) 250-258
8. Cochran J.K., and Bharti A. A multi-stage methodology for whole hospital bed planning. International Journal of Industrial and Systems Engineering 1 1 (2006) 8-36
9. Gorunescu F., McClean S.I., and Millard P.H. A queuing model for bed occupancy management and planning of hospitals. Journal of the Operational Research Society 53 (2002) 19-24
10. Shmueli A., Sprung C.L., and Kaplan E.H. Optimizing admissions to an intensive care unit. Health Care Management Science 6 (2003) 131-136
11. Kim S.C., Horowitz I., Young K.K., and Buckley T.A. Analysis of capacity management of the intensive care unit of a hospital. European Journal of Operational Research 115 (1999) 36-46
12. Stout WA, Tawney B. An Excel forecasting model to aid in decision making that effects hospital bed/resource utilization-hospital capability to admit emergency room patients. In: Bass EJ, editor. Proceedings of the 2005 systems and information engineering design symposium; 2005.
13. Green L.V., Soares J., Giglio J.F., and Green R.A. Using queuing theory to increase the effectiveness of emergency department provider staffing. Academic Emergency Medicine 13 1 (2006) 61-68
14. Garcia ML, Centeno MA, Rivera C, DeCario, N. Reducing time in an emergency room via a fast track. In: Alexopoulos C, Kang K, Lilegdon WR, Goldsman D, editors. Proceedings of the 1995 winter simulation conference; 1995. p. 1048-53.
15. Al Darrab A., Fan J., Fernandes C.M.B., Zimmerman R., Smith R., Worster A., et al. How does fast-track effect quality of care in the emergency department?. European Journal of Emergency Medicine 13 (2006) 32-35
16. Roche, KT, Cochran JK. Improving patient safety by maximizing fast-track benefits in the ED-a queuing network approach. In: IIE industrial engineering research conference, Memphis, TN, 2007. 6pp. on CD-ROM.
17. SoRelle R. Five level ESI passes tests with flying colors. Emergency Medicine News 26 2 (2004) 19
18. Ozcan YA. Forecasting. In: Quantitative methods in health care management, California: Josey-Bass; 2005. p. 10-44 [Chapter 2].
19. HealthTech Briefing Report. Key trends in emergency and trauma services. Health Technology Center, 〈http://www.healthtechcenter.org/〉; October 2006.
20. Gross D, Harris CM. Fundamentals of queueing theory, 3rd ed. Section 4.2: Open Jackson networks. New York: Wiley; 1998. p. 174-83.
21. Allen A. Probability, statistics, and queueing theory with computer science applications. Section 5.5: approximations (1978), Academic Press, New York p. 217-25
22. Fusion K. 'Patient' says it all-ER waits vary widely by state. USA Today (2006, May 31).
23. House B. Arizona growth fastest in US. Arizona Republic (2006, December 22).
24. Gilboy N, Tanabe P, Travers DA, Rosenau AM, Eitel DR. Emergency severity index, version 4: implementation handbook. AHRQ publication no. 05-0046-2. Rockville, MD: Agency for Healthcare Research and Quality; May 2005.
25. Koizumi N., Kuno E., and Smith T.E. Modeling patient flows using a queuing network with blocking. Health Care Management Science 8 1 (2005) 49-60
26. Burdick TL, Cochran JK, Kisiel S, Modena C. Banner Health/Arizona State University Partnership in redesigning emergency department care delivery focusing on patient safety. In: 19th annual IIE Society for Health Systems conference, New Orleans, LA, 2007. 8pp. on CD-ROM.
27. Banner Health Corporation, 2007. Innovations at Banner Health: door-to-doc cuts ED patient "walkout" rate in half, 〈http://www.bannerhealthinnovations.org/DoortoDoc/About+D2D.htm〉; 2007 [last accessed 10:10:07].