Centralized vs. Decentralized Ambulance Diversion: A Network Perspective

References

• Adan I., van Houtum G. J., van der Wal J. Upper and lower bounds for the waiting time in the symmetric shortest queue system. Ann. Oper. Res. (1994) 48(2):197–217Crossref, Google Scholar
• Akşin O. Z., Karaesmen F., Ormeci E. L., Nembhard D. A. A review of workforce cross-training in call centers from an operations management perspective. Workforce Cross Training Handbook. (2007) (CRC Press, Boca Raton, FL) 211–240Crossref, Google Scholar
• Allon G., Federgruen A. Competition in service industries. Oper. Res. (2007) 55(1):37–55Link, Google Scholar
• Allon G., Deo S., Lin W. The impact of size and occupancy of hospital on the extent of ambulance diversion: Theory and evidence. (2009) . Working paper, Kellogg School of Management, Northwestern University, Evanston, ILGoogle Scholar
• Asamoah O. K., Weiss S. J., Ernst A. A., Richards M., Sklar D. P. A novel diversion protocol dramatically reduces diversion hours. Amer. J. Emergency Medicine (2008) 26(6):670–675Crossref, Google Scholar
• Asmussen S.Applied Probability and Queues (2003) (Springer Verlag, New York) Google Scholar
• Atar R. Scheduling control for queueing systems with many servers: Asymptotic optimality in heavy traffic. Ann. Appl. Probab. (2005) 15(4):2606–2650Crossref, Google Scholar
• Bagust A., Place M., Posnett J. W. Dynamics of bed use in accommodating emergency admissions: Stochastic simulation model. British Medical J. (1999) 319(7203):155–158Crossref, Google Scholar
• Baron D., Kalai E. The simplest equilibrium of a majority-rule division game. J. Econom. Theory (1993) 61(2):290–301Crossref, Google Scholar
• Benjaafar S. Performance bounds for the effectiveness of pooling in multi-processing systems. Eur. J. Oper. Res. (1995) 87(2):375–388Crossref, Google Scholar
• Bernstein S. L., Verghese V., Leung W., Lunney A. T., Perez I. Development and validation of a new index to measure emergency department crowding. Emergency Medicine (2003) 10(9):938–942Google Scholar
• Billittier A. J., Moscati R., Janicke D., Lerner E. B., Seymour J., Olsson D. A multisite survey of factors contributing to medically unnecessary ambulance transports. Acad. Emergency Medicine (1996) 3(11):1046–1050Crossref, Google Scholar
• Cachon G. P., Harker P. T. Competition and outsourcing with scale economies. Management Sci. (2002) 48(10):1314–1333Link, Google Scholar
• Cachon G. P., Zhang F. Obtaining fast service in a queueing system via performance-based allocation of demand. Management Sci. (2007) 53(3):408–420Link, Google Scholar
• Chen Y., Maglaras C., Vulcano G. Design of an aggregated marketplace under congestion effects: Asymptotic analysis and equilibrium characterization. (2008) . Working paper, Stern School of Business, New York University, New YorkGoogle Scholar
• Chidester C., Koenig W., Tadeo R. (2009) . Personal communication with LA County EMS AgencyGoogle Scholar
• Cochran J. K., Roche K. T. A multi-class queuing network analysis methodology for improving hospital emergency department performance. Comput. Oper. Res. (2009) 36(5):1497–1512Crossref, Google Scholar
• Deo S., Gurvich I. Proofs of auxiliary lemmas for the manuscript titled: “Centralized vs. decentralized ambulance diversion: A network perspective”. (2011) . Technical report, Kellogg School of Management, Northwestern University, Evanston, ILGoogle Scholar
• Enders P., Scheller-Wolf A., Cho S. H., Shunko M. Hospital capacity effects on inbound ambulance traffic. (2010) . Working paper, Tepper School of Business, Carnegie Mellon University, PittsburghGoogle Scholar
• Epstein S. K., Tian L. Development of an emergency department work score to predict ambulance diversion. Acad. Emergency Medicine (2006) 13(4):421–426Crossref, Google Scholar
• Falvo T., Grove L., Stachura R., Zirkin W. The financial impact of ambulance diversions and patient elopements. Acad. Emergency Medicine (2007) 14(1):58–62Crossref, Google Scholar
• Fatovich D. M., Nagree Y., Sprivulis P. Access block causes emergency department overcrowding and ambulance diversion in Perth, Western Australia. British Medical J. (2005) 22(5):351Google Scholar
• Franaszek J. B., Asplin B. R., Brunner B. Responding to emergency department crowding: A guidebook for chapters. (2002) . Technical report, American College of Emergency Physicians, Irving, TXGoogle Scholar
• Gamarnik D., Goldberg D. A. On the rate of convergence to stationarity of the M/M/N queue in the Halfin-Whitt regime. (2010) . Working paper, Massachusetts Institute of Technology, CambridgeGoogle Scholar
• Gans N., Liu N., Mandelbaum A., Shen H., Ye H. Service times in call centers: Agent heterogeneity and learning with some operational consequences. Festschrift for Lawrence D. Brown (2010) 6(IMS Collections, Beachwood, OH) 99–123Crossref, Google Scholar
• GAO (U.S. Government Accountability Office) Hospital emergency departments: Crowded conditions vary among hospitals and communities. (2003) . Report to the Ranking Minority Member Committee, on Finance, U.S. Senate, GAO, Washington, DCGoogle Scholar
• Green L. V. How many hospital beds? Inquiry (2002) 39(4):400–412Crossref, Google Scholar
• Gurvich I., Whitt W. Scheduling flexible servers with convex delay costs in many-server service systems. Manufacturing Service Oper. Management (2009) 11(2):237–253Link, Google Scholar
• Hagtvedt R., Ferguson M., Griffin P., Jones G. T., Keskinocak P., Rossetti M. D., Hill R. R., Johansson B., Dunkin A., Ingalls R. G. Cooperative strategies to reduce ambulance diversion. Proc. 2009 Winter Simulation Conf. (WSC) (2009) Austin, TX:1861–1874Crossref, Google Scholar
• Hoot N. R., Aronsky D. Systematic review of emergency department crowding: Causes, effects, and solutions. Ann. Emergency Medicine (2008) 52(2):126–136Crossref, Google Scholar
• Hoot N. R., Zhou C., Jones I., Aronsky D. Measuring and forecasting emergency department crowding in real time. Ann. Emergency Medicine (2007) 49(6):747–755Crossref, Google Scholar
• Kalai E., Kamien M. I., Rubinovitch M. Optimal service speeds in a competitive environment. Management Sci. (1992) 38(8):1154–1163Link, Google Scholar
• Kowalczyk L. State orders ERs to halt “diversions”: Bid to ease overcrowding seen to sometimes delay care. Boston Globe (2008) September 13). http://www.boston.com/news/local/articles/2008/09/13/state_orders_hospital_ers_to_halt_diversionsGoogle Scholar
• Levhari D., Luski I. Duopoly pricing and waiting lines. Eur. Econom. Rev. (1978) 11(1):17–35Crossref, Google Scholar
• Lin K. L. Decentralized admission control of a queueing system: A game-theoretic model. Naval Res. Logist. (2003) 50(7):702–718Crossref, Google Scholar
• Maman S. Uncertainty in the demand for service: The case of call centers and emergency departments. (2009) . MSc. thesis, Technion–Israel Institute of Technology, Technion City, HaifaGoogle Scholar
• MARCER (Mid-America Regional Council Emergency Rescue) Community plan for ambulance diversion for the greater Kansas City metropolitan area. (2007) (Diversion Work Group of the Health Alliance of MidAmerica and MARCER Committee, Kansas City, MO) Google Scholar
• McCarthy M. L., Aronsky D., Jones I. D., Miner J. R., Band R. A., Baren J. M., Desmond J. S., Baumlin K. M., Ding R., Shesser R. The emergency department occupancy rate: A simple measure of emergency department crowding? Ann. Emergency Medicine (2008) 51(1):15–24Crossref, Google Scholar
• Meyn S. P., Tweedie R. L. Stability of Markovian processes III: Foster-Lyapunov criteria for continuous-time processes. Ann. Appl. Prob. (1993) 25(3):518–548Crossref, Google Scholar
• Mihal N., Moilanen R. When emergency rooms close: Ambulance diversion in the West San Fernando Valley. (2005) . Technical report, The Ralph and Goldy Lewis Center for Regional Policy Studies, University of California, Los Angeles. Accessed May 9, 2011, http://www.escholarship.org/uc/item/4g80h84vGoogle Scholar
• Moskop J. C., Sklar D. P., Geiderman J. M., Schears R. M., Bookman K. J. Emergency department crowding, Part 1—Concept, causes, and moral consequences. Ann. Emergency Medicine (2009) 53(5):605–611Crossref, Google Scholar
• Naor P. The regulation of queue size by levying tolls. Econometrica (1969) 37(1):15–24Crossref, Google Scholar
• NYBEMS (New York State Department of Health, Bureau of Emergency Medical Services) Emergency patient destinations and hospital diversion. (2006) (NYBEMS, Albany, NY) Google Scholar
• PEHSC (Pennsylvania Emergency Health Services Council) Joint position statement: Guidelines for hospital ambulance-diversion policies. Medical advisory committee. (2004) (PEHSC, Mechanicsburg, PA) Google Scholar
• Pham J. C., Story J. L., Hicks R. W., Shore A. D., Morlock L. L., Cheung D. S., Kelen G. D., Pronovost P. J. National study on the frequency, types, causes, and consequences of voluntarily reported emergency department medication errors. J. Emergency Medicine (2008) . ePub ahead of print September 26, doi:10.1016/j.jemermed.2008.02.059Google Scholar
• Ramirez A., Fowler J. W., Wu T., Rossetti M. D., Hill R. R., Johansson B., Dunkin A., Ingalls R. G. Analysis of ambulance diversion policies for a large-size hospital. Proc. 2009 Winter Simulation Conf. (WSC) (2009) Austin, TX:1875–1886Crossref, Google Scholar
• Reeder T. J., Garrison H. G. When the safety net is unsafe: Real-time assessment of the overcrowded emergency department. Acad. Emergency Medicine (2001) 8(11):1070–1074Crossref, Google Scholar
• Richards J. R., Ferrall S. J. Inappropriate use of emergency medical services transport: Comparison of provider and patient perspectives. Acad. Emergency Medicine (1999) 6(1):14–20Crossref, Google Scholar
• Schelling T. C. The strategy of conflict: Prospectus for a reorientation of game theory. J. Conflict Resolution (1958) 2(3):203–264Crossref, Google Scholar
• Schull M. J., Slaughter P. M., Redelmeier D. A. Urban emergency department overcrowding: Defining the problem and eliminating misconceptions. Canadian J. Emergency Medicine (2002) 4(2):76–83Google Scholar
• Schull M. J., Morrison L. J., Vermeulen M., Redelmeier D. A. Emergency department gridlock and out-of-hospital delays for cardiac patients. Acad. Emergency Medicine (2003) 10(7):709–716Crossref, Google Scholar
• Schull M. J., Vermuelen M., Slaughter G., Morrison L., Daly P. Emergency department crowding and thrombolysis delays in acute myocardial infarction. Ann. Emergency Medicine (2004) 44(6):577–585Crossref, Google Scholar
• Stolyar A. L. Optimal routing in output-queued flexible server systems. Probab. Engrg. Inform. Sci. (2005) 19(2):141–189Crossref, Google Scholar
• Sun B. C., Mohanty S. A., Weiss R., Tadeo R., Hasbrouck M., Koenig W., Meyer C., Asch S. Effects of hospital closures and hospital characteristics on emergency department ambulance diversion, Los Angeles County, 1998 to 2004. Ann. Emergency Medicine (2006) 47(4):309–316Crossref, Google Scholar
• Tezcan T. Optimal control of distributed parallel server systems under the Halfin and Whitt regime. Math. Oper. Res. (2008) 33(1):51–90Link, Google Scholar
• Vassilocopoulos G. A simulation model for bed allocation to hospital inpatient departments. Simulation (1985) 45(5):233–241Crossref, Google Scholar
• Vilkes G. M., Castillo E. M., Metz M. A., Ray L. U., Murrin P. A., Lev R., Chan T. C. Community trial to decrease ambulance diversion hours: The San Diego County patient destination trial. Ann. Emergency Medicine (2004) 44(4):295–303Crossref, Google Scholar
• Vives X.Oligopoly Pricing: Old Ideas and New Tools (2001) (MIT Press, Cambridge, MA) Google Scholar
• Weiss S. J., Derlet R., Arndahl J., Ernst A. A., Richards J., Fernandez-Frankelton M., Schwab R., Stair T. O., Vicellio P., Levy D., et al. Estimating the degree of emergency department overcrowding in academic medical centers: Results of the National ED Overcrowding Study (NEDOCS). Acad. Emergency Medicine (2004) 11(1):38–50Crossref, Google Scholar
• Whitt W. Deciding which queue to join: Some counterexamples. Oper. Res. (1986) 34(1):55–62Link, Google Scholar
• Whitt W. A review of L=λW and extensions. Queueing Systems (1991) 9:235–268Crossref, Google Scholar
• Yom-Tov G. Queues in hospitals: Queueing networks with Reentering customers in the QED regime. (2009) . Ph.D. thesis, Technion–Israel Institute of Technology, Technion City, HaifaGoogle Scholar