The Impact of Delay Announcements on Hospital Network Coordination and Waiting Times

Published Online:https://doi.org/10.1287/mnsc.2018.3048

References

  • Abadie A (2005) Semiparametric difference-in-differences estimators. Rev. Econom. Stud. 72(1):1–19.CrossrefGoogle 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
  • Anderson SP, de Palma A, Thisee J-F (1996) Discrete Choice Theory of Product Differentiation (MIT Press, Cambridge, MA).Google Scholar
  • Ang E, Kwasnick S, Bayati M, Plambeck E, Aratow M (2016) Accurate emergency department wait time prediction. Manufacturing Service Oper. Management 18(1):141–156.LinkGoogle Scholar
  • Armony M, Maglaras C (2004) On customer contact centers with a call-back option: Customer decisions, routing rules, and system design. Oper. Res. 52(2):271–292.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
  • Armony M, Israelit S, Mandelbaum A, Marmor Y, Tseytlin Y, Yom-Tov G (2015) On patient flow in hospitals: A data-based queueing-science perspective. Stochastic Systems 5(1):146–194.LinkGoogle Scholar
  • Campello F, Ingolfsson A, Shumsky RA (2017) Queueing models of case manager. Management Sci. 63(3):882–900.LinkGoogle Scholar
  • Carmon Z, Kahneman D (1996) The experienced utility of queuing: Real time affect and retrospective evaluations of simulated queues. Working paper, Duke University, Durham, NC.Google Scholar
  • Centers for Disease Control and Prevention (2010) National hospital ambulatory medical care survey. Accessed July 21, 2015, ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/dataset_documentation/nhamcs/stata.Google Scholar
  • Chalfin DB, Trzeciak S, Likourezos A, Baumann BM, Dellinger RP (2007) Impact of delayed transfer of critically ill patients from the emergency department to the intensive care unit. Critical Care Medicine 35(6):1477–1485.CrossrefGoogle Scholar
  • Duda RO, Hart PE, Stork DG (2001) Pattern Classification (John Wiley & Sons, New York).Google Scholar
  • Ekström A, Kurland L, Farrokhnia N, Castrén M, Nordberg M (2014) Forecasting emergency department visits using internet data. Ann. Emergency Medicine 65(4):436–442.CrossrefGoogle Scholar
  • Foley RD, McDonald DR (2001) Join the shortest queue: Stability and exact asymptotics. Ann. Appl. Probab. 11(3):569–607.CrossrefGoogle Scholar
  • Fox S, Duggan M (2013) Health online 2013. Pew Research Center Report (January 15), http://www.pewinternet.org/2013/01/15/health-online-2013/.Google Scholar
  • Groeger L, Tigas M, Wei S (2014) ED Wait Watcher. Accessed July 2014, http://projects.propublica.org/emergency/.Google Scholar
  • Huang T, Allon G, Bassamboo A (2013) Bounded rationality in service systems. Manufacturing Service Oper. Management 15(2):263–279.LinkGoogle Scholar
  • Ibrahim R, Whitt W (2009) Real-time delay estimation based on delay history. Manufacturing Service Oper. Management 11(3):397–415.LinkGoogle Scholar
  • Ibrahim R, Whitt W (2011) Real-time delay estimation based on delay history in many-server service systems with time-varying arrivals. Production Oper. Management 20(5):654–667.CrossrefGoogle Scholar
  • Jenkins A (2013) Self-oscillation. Phys. Rep. 525(2):167–222.CrossrefGoogle Scholar
  • Kostami V, Ward AR (2009) Managing service systems with an offline waiting option and customer abandonment. Oper. Res. 11(4):644–656.Google Scholar
  • Larson RC (1987) OR forum—Perspectives on queues: Social justice and the psychology of queueing. Oper. Res. 35(6):895–905.LinkGoogle Scholar
  • Libby D (1999) RSS 0.91 Spec, revision 3. Netscape Communications (July 10), https://web.archive.org/web/20001204093600/http://my.netscape.com/publish/formats/rss-spec-0.91.html.Google Scholar
  • Mandelbaum A, Zeltyn S (2013) Data-stories about (im)patient customers in tele-queues. Queueing Systems 75(2–4):115–146.CrossrefGoogle Scholar
  • Marco CA, Weiner M, Ream SL, Lumbrezer D, Karanovic D (2012) Access to care among emergency department patients. Emergency Medicine J. 29(1):28–31.CrossrefGoogle Scholar
  • Meyer BD (1995) Natural and quasi-experiments in economics. J. Bus. Econom. Statist. 13(2):151–161.CrossrefGoogle Scholar
  • Munichor N, Rafaeli A (2007) Numbers or apologies? Customer reactions to telephone waiting time fillers. J. Appl. Psych. 92(2):511–518.CrossrefGoogle Scholar
  • NEHI (2010) A matter of urgency: Reducing emergency department overuse. Research brief, New England Healthcare Institute, Boston.Google Scholar
  • Ofran Y, Paltiel O, Pelleg D, Rowe JM, Yom-Tov E (2012) Patterns of information-seeking for cancer on the internet: An analysis of real world data. PLoS One 7(9):e45921.CrossrefGoogle Scholar
  • Perrin A, Duggan M (2015) Americans’ internet access: 2000–2015. Pew Research Center Report (June 26), http://www.pewinternet.org/2015/06/26/americans-internet-access-2000-2015.Google Scholar
  • Plunkett PK, Byrne DG, Breslin T, Bennett K, Silke B (2011) Increasing wait times predict increasing mortality for emergency medical admissions. Eur. J. Emergency Medical 18(4):192–196.CrossrefGoogle Scholar
  • Polgreen PM, Chen Y, Pennock DM, Nelson FD, Weinstein RA (2008) Using internet searches for influenza surveillance. Clinical Infectious Diseases 47(11):1443–1448.CrossrefGoogle Scholar
  • Reiman MI (1984) Open queueing networks in heavy traffic. Math. Oper. Res. 9(3):441–458.LinkGoogle Scholar
  • Selen J, Adan I, Kapodistria S, van Leeuwaarden JSH (2016) Steady-state analysis of shortest expected delay routing. Queueing Syst. 84(3–4):309–354.CrossrefGoogle Scholar
  • Senderovich A, Weidlich M, Gal A, Mandelbaum A (2014) Queue mining–Predicting delays in service processes. Jarke Met al. eds. Advanced Information Systems Engineering, CAiSE 2014. Lecture Notes in Computer Science, Vol. 8484 (Springer, Cham), 42–57.CrossrefGoogle Scholar
  • Shi P, Chou MC, Dai JG, Ding D, Sim J (2016) Models and insights for hospital inpatient operations: Time-dependent ED boarding time. Management Sci. 62(1):1–28.LinkGoogle Scholar
  • Song H, Tucker AL, Murrell KL (2015) The diseconomies of queue pooling: An empirical investigation of emergency department length of stay. Management Sci. 61(12):3032–3053.LinkGoogle Scholar
  • Turner SRE (2000) A join the shorter queue model in heavy traffic. J. Appl. Probab. 37(1):212–223.CrossrefGoogle Scholar
  • Whitt W (1992) Understanding the efficiency of multi-server service systems. Management Sci. 38(5):708–723.LinkGoogle Scholar
  • Widrow B, Kollar I, Liu M-C (1996) Statistical theory of quantization. IEEE Trans. Instrumentation Measurement 45(2):353–361.CrossrefGoogle Scholar
  • Xu Y, Armony M, Ghose A (2016) The effect of online reviews on physician demand: A structural model of patient choice. Working paper, New York University, New York.Google Scholar
  • Yom-Tov E, Boyd DM (2014) On the link between media coverage of anorexia and pro-anorexic practices on the web. Internat. J. Eating Disorders 47(2):196–202.CrossrefGoogle Scholar
  • Yom-Tov E, Gabrilovich E (2013) Postmarket drug surveillance without trial costs: Discovery of adverse drug reactions through large-scale analysis of web search queries. J. Medical Internet Res. 15(6):e124.CrossrefGoogle Scholar
  • Yom-Tov E, Lalmas M, Baeza-Yates RA, Dupret G, Lehmann J, Donmez P (2013) Measuring inter-site engagement. Proc. 2013 IEEE Internat. Conf. Big Data, 6–9 October 2013, Santa Clara, CA, 228–236.Google Scholar
  • Yu Q, Allon G, Bassamboo A (2017) How do delay announcements shape customer behavior? An empirical study. Management Sci. 63(1):1–20.LinkGoogle Scholar
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