Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department

Published Online:https://doi.org/10.1287/mnsc.2014.2058

References

  • Akşin Z, Ata B, Emadi S, Su CL (2013) Structural estimation of callers' delay sensitivity in call centers. Management Sci. 59(12):2727–2746.LinkGoogle Scholar
  • Allon G, Bassamboo A, Gurvich I (2011) “We will be right with you”: Managing customer expectations with vague promises and cheap talk. Oper. Res. 59(6):1382–1394.LinkGoogle Scholar
  • American College of Emergency Physicians (2012) Publishing wait times for emergency department care: An information paper. Report, American College of Emergency Physicians, Baltimore.Google Scholar
  • Arendt KW, Sadosty AT, Weaver AL, Brent CR, Boie ET (2003) The left-without-being-seen patients: what would keep them from leaving? Ann. Emergency Medicine 42(3):317–323.CrossrefGoogle Scholar
  • Armony M, Mandelbaum A (2011) Routing and staffing in large-scale service systems: The case of homogeneous impatient customers and heterogeneous servers. Oper. Res. 59(1):50–65.LinkGoogle Scholar
  • Armony M, Shimkin N, Whitt W (2009) The impact of delay announcements in many-server queues with abandonment. Oper. Res. 57(1):66–81.LinkGoogle Scholar
  • Baccelli F, Hebuterne G (1981) On queues with impatient customers. Kylstra FJ, ed. Performance '81: Proc. 8th Internat. Sympos. Computer Performance Modeling, Measurement, Evaluation (North-Holland, Amsterdam), 159–179.Google Scholar
  • Batt RJ, Terwiesch C (2014) Doctors under load: An empirical study of state-dependent service times in emergency care. Working paper, University of Wisconsin–Madison, Madison.Google Scholar
  • Berry Jaeker J, Tucker AL (2012) Hurry up and wait: Differential impacts of congestion, bottleneck pressure, and predictability on patient length of stay. HBS Working Paper 13-052, Harvard Business School, Boston.Google Scholar
  • Bitran GR, Ferrer J-C, Rocha e Oliveira P (2008) Managing customer experiences: Perspectives on the temporal aspects of service encounters. Manufacturing Service Oper. Management 10(1):61–83.LinkGoogle Scholar
  • Bolandifar E, DeHoratius N, Olsen T, Wiler JL (2014) Modeling the behavior of patients who leave the emergency department without being seen by a physician. Chicago Booth Research Paper 12-14, University of Chicago, Chicago.Google Scholar
  • Brandt A, Brandt M (1999) On the M(n)/M(n)/s queue with impatient calls. Performance Evaluation 35(1–2):1–18.CrossrefGoogle Scholar
  • Brandt A, Brandt M (2002) Asymptotic results and a Markovian approximation for the M(n)/M(n)/s+GI system. Queueing Systems 41(1/2):73–94.CrossrefGoogle Scholar
  • Brown L, Gans N, Mandelbaum A, Sakov A, Shen H, Zeltyn S, Zhao L (2005) Statistical analysis of a telephone call center: A queueing-science perspective. J. Amer. Statist. Assoc. 100(469):36–50.CrossrefGoogle Scholar
  • Cameron CA, Trivedi PK (2005) Microeconometrics: Methods and Applications (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Centers for Medicare and Medicaid Services (2012) Hospital outpatient prospective and ambulatory surgical center payment systems and quality reporting programs; electronic reporting pilot; inpatient rehabilitation facilities quality reporting program; quality improvement organization regulations. Federal Register 77(146):45061–45233.Google Scholar
  • Chan CW, Yom-Tov G, Escobar G (2014) When to use speedup: An examination of service systems with returns. Oper. Res. 62(2):462–482.LinkGoogle Scholar
  • Clifford S, Hardy Q (2013) Attention, shoppers: Store is tracking your cell. New York Times (July 15) A1–A3.Google Scholar
  • Echambadi R, Hess JD (2007) Mean-centering does not alleviate collinearity problems in moderated multiple regression models. Marketing Sci. 26(3):438–445.LinkGoogle Scholar
  • Fernandes C, Daya MR, Barry S, Palmer N (1994) Emergency department patients who leave without seeing a physician: the Toronto hospital experience. Ann. Emergency Medicine 24(6):1092–1096.CrossrefGoogle Scholar
  • Garnett O, Mandelbaum A, Reiman M (2002) Designing a call center with impatient customers. Manufacturing Service Oper. Management 4(3):208–227.LinkGoogle Scholar
  • Gilboy N, Tanabe T, Travers D, Rosenau A (2011) Emergency Severity Index (ESI): A Triage Tool for Emergency Department Care, Version 4, Implementation Handbook, 2012 (Agency for Healthcare Research and Quality, Rockville, MD).Google Scholar
  • Greene WH (2012) Econometric Analysis, 7th ed. (Prentice Hall, Upper Saddle River, NJ).Google Scholar
  • Guo P, Zipkin P (2007) Analysis and comparison of queues with different levels of delay information. Management Sci. 53(6):962–970.LinkGoogle Scholar
  • Hair JF Jr, Anderson RE, Tatham RL, Black WC (1995) Multivariate Data Analysis, 3rd ed. (Macmillan, New York).Google Scholar
  • Hassin R, Haviv M (2003) To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems, International Series in Operations Research and Management Science, Vol. 59 (Springer, New York).CrossrefGoogle Scholar
  • Haviv M, Ritov Y (2001) Homogeneous customers renege from invisible queues at random times under deteriorating waiting conditions. Queueing Systems 38(4):495–508.CrossrefGoogle Scholar
  • Hobbs D, Kunzman SC, Tandberg D, Sklar D (2000) Hospital factors associated with emergency center patients leaving without being seen. Amer. J. Emergency Medicine 18(7):767–772.CrossrefGoogle Scholar
  • Hsia RY, Asch SM, Weiss RE, Zingmond D, Liang LJ, Han W, McCreath H, Sun BC (2011) Hospital determinants of emergency department left without being seen rates. Ann. Emergency Medicine 58(1):24–32.CrossrefGoogle Scholar
  • Huang T, Allon G, Bassamboo A (2013) Bounded rationality in service systems. Manufacturing Service Oper. Management 15(2):263–279.LinkGoogle Scholar
  • Hui MK, Tse DK (1996) What to tell consumers in waits of different lengths: An integrative model of service evaluation. J. Marketing 60(2):81–90.CrossrefGoogle Scholar
  • Janakiraman N, Meyer R, Hoch SJ (2011) The psychology of decisions to abandon waits for service. J. Marketing Res. 48(6):970–984.CrossrefGoogle Scholar
  • Jouini O, Aksin Z, Dallery Y (2011) Call centers with delay information: Models and insights. Manufacturing Service Oper. Management 13(4):534–548.LinkGoogle Scholar
  • Jouini O, Dallery Y, Aksin Z (2009) Queueing models for full-flexible multi-class call centers with real-time anticipated delays. Internat. J. Production Econom. 120(2):389–399.CrossrefGoogle Scholar
  • Kremer M, Debo LG (2013) Herding in a queue: A laboratory experiment. Chicago Booth Research Paper 12-28, University of Chicago, Chicago.Google Scholar
  • Larson RC (1987) Perspectives on queues: Social justice and the psychology of queueing. Oper. Res. 35(6):895–905.LinkGoogle Scholar
  • Lindstrom M (2011) Shopping carts will track customers' every move. Harvard Bus. Rev. (blog) (December 9), http://blogs.hbr.org/2011/12/shopping-carts-will-track-cons.Google Scholar
  • Little RJA (1988) Missing-data adjustments in large surveys. J. Bus. Econom. Statist. 6(3):287–296.Google Scholar
  • Liu Y, Whitt W (2012) Stabilizing customer abandonment in many-server queues with time-varying arrivals. Oper. Res. 60(6):1551–1564.LinkGoogle Scholar
  • Lu Y, Musalem A, Olivares M, Schilkrut A (2013) Measuring the effect of queues on customer purchases. Management Sci. 59(8):1743–1763.LinkGoogle Scholar
  • Lucas J, Batt RJ, Soremekun OA (2014) Setting wait times to achieve targeted left-without-being-seen rates. Amer. J. Emergency Medicine 32(4):342–345.CrossrefGoogle Scholar
  • Mandelbaum A, Shimkin N (2000) A model for rational abandonments from invisible queues. Queueing Systems 36(1–3):141–173.CrossrefGoogle Scholar
  • Mandelbaum A, Zeltyn S (2013) Data-stories about (im)patient customers in tele-queues. Queueing Systems 75(2–4):115–146.CrossrefGoogle Scholar
  • Nagler J (1994) Scobit: An alternative estimator to logit and probit. Amer. J. Political Sci. 38(1):230–255.CrossrefGoogle Scholar
  • Palm C (1943) Intensitätsschwankungen im Fernsprechverkehr (Esselte, Stockholm).Google Scholar
  • Pham JC, Ho GK, Hill PM, McCarthy ML, Pronovost PJ (2009) National study of patient, visit, and hospital characteristics associated with leaving an emergency department without being seen: Predicting LWBS. Acad. Emergency Medicine 16(10):949–955.CrossrefGoogle Scholar
  • Plambeck EL, Wang Q (2013) Implications of hyperbolic discounting for optimal pricing and scheduling of unpleasant services that generate future benefits. Management Sci. 59(8):1927–1946.LinkGoogle Scholar
  • Polevoi SK, Quinn JV, Kramer NR (2005) Factors associated with patients who leave without being seen. Acad. Emergency Medicine 12(3):232–236.CrossrefGoogle Scholar
  • Rubin DB (1977) Formalizing subjective notions about the effect of nonrespondents in sample surveys. J. Amer. Statist. Assoc. 72(359):538–543.CrossrefGoogle Scholar
  • Rubin DB (1980) Handling nonresponse in sample surveys by multiple imputations. Bureau of the Census Monograph, U.S. Department of Commerce, Washington, DC.Google Scholar
  • Rubin DB (1996) Multiple imputation after 18+ years. J. Amer. Statist. Assoc. 91(434):473–489.CrossrefGoogle Scholar
  • Schenker N, Taylor JMG (1996) Partially parametric techniques for multiple imputation. Computational Statist. Data Anal. 22(4):425–446.CrossrefGoogle Scholar
  • Shimkin N, Mandelbaum A (2004) Rational abandonment from tele-queues: Nonlinear waiting costs with heterogeneous preferences. Queueing Systems 47(1–2):117–146.CrossrefGoogle Scholar
  • Whitt W (1984) The amount of overtaking in a network of queues. Networks 14(3):411–426.CrossrefGoogle Scholar
  • Wooldridge JM (2010) Econometric Analysis of Cross Section and Panel Data, 2nd ed. (MIT Press, Cambridge, MA).Google Scholar
  • Zohar E, Mandelbaum A, Shimkin N (2002) Adaptive behavior of impatient customers in tele-queues: Theory and empirical support. Management Sci. 48(4):566–583.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.