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Patient Sensitivity to Emergency Department Waiting Time Announcements

Author

Listed:
  • Eric Park

    (School of Business, Wake Forest University, Winston-Salem, North Carolina 27109)

  • Huiyin Ouyang

    (Faculty of Business and Economics, The University of Hong Kong, Hong Kong)

  • Jingqi Wang

    (The Chinese University of Hong Kong, Shenzhen 518172, China)

  • Sergei Savin

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Siu Chung Leung

    (Hong Kong Baptist Hospital, Hong Kong)

  • Timothy H. Rainer

    (Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong)

Abstract

Problem definition : Emergency department (ED) delay announcement systems are implemented in many countries. We answer three important questions pertaining to the operations and effectiveness of such systems by studying the public hospital network and ED waiting time (WT) announcement system in Hong Kong’s “universal” public healthcare system: (1) How many patients are aware of (and sensitive to) the ED WT announcements? (2) How sensitive are these patients to the announced WT? (3) How can the Hong Kong government improve the WT announcement system? Methodology/results : We study over 1.3 million patient visits to the 17 tier 1 public EDs. We structurally estimate the fraction of patients sensitive to the announced WT and their sensitivity to the announcements as well as patient characteristics that lead to higher sensitivity. In the patient’s ED choice decision, we estimate the trade-off between the travel distance to an ED and the expected WT at the ED. We find that 3.1% of the patients are sensitive to the announced WT, and they are willing to travel an additional 4.8 km to save one hour of waiting. Urgent patients are less likely to be sensitive to the delay announcement than less urgent patients, but those that are sensitive are more WT averse than their less urgent counterparts. Counterfactual analysis shows that the average actual WT and number of patients who leave without being seen can be reduced by 4.6% and 8.5%, respectively, by increasing the fraction of sensitive patients to 15.0% and, simultaneously, reducing the announced WT assessment window to one hour from the current level of three hours. Further improvement can be achieved by providing predicted WT information based on the current level of ED crowding or less extreme past performance—median WT rather than the currently used 95th percentile. Managerial implications : The Hong Kong government should utilize the two levers of the announcement system: the sensitive fraction of patients and information recency. Increasing the sensitive fraction can benefit the system when it is below a certain threshold level. However, administrators should exercise caution when the sensitive fraction becomes large and consider implementing additional measures to mitigate the negative effects of information delay. The sensitive group of patients can unfairly be punished for their proactiveness. Shortening the announced WT assessment window and providing predicted WT are possible alternatives that not only improve overall performance but also exhibit strong robustness to increases in the sensitive population.

Suggested Citation

  • Eric Park & Huiyin Ouyang & Jingqi Wang & Sergei Savin & Siu Chung Leung & Timothy H. Rainer, 2025. "Patient Sensitivity to Emergency Department Waiting Time Announcements," Manufacturing & Service Operations Management, INFORMS, vol. 27(6), pages 1740-1759, November.
  • Handle: RePEc:inm:ormsom:v:27:y:2025:i:6:p:1740-1759
    DOI: 10.1287/msom.2022.0457
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    References listed on IDEAS

    as
    1. Qiuping Yu & Gad Allon & Achal Bassamboo & Seyed Iravani, 2018. "Managing Customer Expectations and Priorities in Service Systems," Management Science, INFORMS, vol. 64(8), pages 3942-3970, August.
    2. Yichuan Ding & Eric Park & Mahesh Nagarajan & Eric Grafstein, 2019. "Patient Prioritization in Emergency Department Triage Systems: An Empirical Study of the Canadian Triage and Acuity Scale (CTAS)," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 723-741, October.
    3. Erjie Ang & Sara Kwasnick & Mohsen Bayati & Erica L. Plambeck & Michael Aratow, 2016. "Accurate Emergency Department Wait Time Prediction," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 141-156, February.
    4. Siddharth Arora & James W. Taylor & Ho-Yin Mak, 2023. "Probabilistic Forecasting of Patient Waiting Times in an Emergency Department," Manufacturing & Service Operations Management, INFORMS, vol. 25(4), pages 1489-1508, July.
    5. Zeynep Akşin & Baris Ata & Seyed Morteza Emadi & Che-Lin Su, 2017. "Impact of Delay Announcements in Call Centers: An Empirical Approach," Operations Research, INFORMS, vol. 65(1), pages 242-265, February.
    6. Naor, P, 1969. "The Regulation of Queue Size by Levying Tolls," Econometrica, Econometric Society, vol. 37(1), pages 15-24, January.
    7. Sarang Deo & Itai Gurvich, 2011. "Centralized vs. Decentralized Ambulance Diversion: A Network Perspective," Management Science, INFORMS, vol. 57(7), pages 1300-1319, July.
    8. Soroush Saghafian & Wallace J. Hopp & Mark P. Van Oyen & Jeffrey S. Desmond & Steven L. Kronick, 2014. "Complexity-Augmented Triage: A Tool for Improving Patient Safety and Operational Efficiency," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 329-345, July.
    9. Najiya Fatma & Varun Ramamohan, 2023. "Patient diversion using real-time delay predictions across healthcare facility networks," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(2), pages 437-476, June.
    10. Shone, Rob & Knight, Vincent A. & Williams, Janet E., 2013. "Comparisons between observable and unobservable M/M/1 queues with respect to optimal customer behavior," European Journal of Operational Research, Elsevier, vol. 227(1), pages 133-141.
    11. Lawrence Brown & Noah Gans & Avishai Mandelbaum & Anat Sakov & Haipeng Shen & Sergey Zeltyn & Linda Zhao, 2005. "Statistical Analysis of a Telephone Call Center: A Queueing-Science Perspective," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 36-50, March.
    12. Achal Bassamboo & Rouba Ibrahim, 2021. "A General Framework to Compare Announcement Accuracy: Static vs. LES-Based Announcement," Management Science, INFORMS, vol. 67(7), pages 4191-4208, July.
    13. Kuang Xu & Carri W. Chan, 2016. "Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 314-331, July.
    14. Hassin, Refael, 1986. "Consumer Information in Markets with Random Product Quality: The Case of Queues and Balking," Econometrica, Econometric Society, vol. 54(5), pages 1185-1195, September.
    15. Mor Armony & Nahum Shimkin & Ward Whitt, 2009. "The Impact of Delay Announcements in Many-Server Queues with Abandonment," Operations Research, INFORMS, vol. 57(1), pages 66-81, February.
    16. Crandall, M. & Sharp, D. & Unger, E. & Straus, D. & Brasel, K. & Hsia, R. & Esposito, T., 2013. "Trauma deserts: Distance from a trauma center, transport times, and mortality from gunshot wounds in Chicago," American Journal of Public Health, American Public Health Association, vol. 103(6), pages 1103-1109.
    17. Oualid Jouini & Zeynep Akşin & Yves Dallery, 2011. "Call Centers with Delay Information: Models and Insights," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 534-548, October.
    18. Hong Il Yoo, 2020. "lclogit2: An enhanced command to fit latent class conditional logit models," Stata Journal, StataCorp LLC, vol. 20(2), pages 405-425, June.
    19. Ward Whitt, 1999. "Improving Service by Informing Customers About Anticipated Delays," Management Science, INFORMS, vol. 45(2), pages 192-207, February.
    20. Edelson, Noel M & Hildebrand, David K, 1975. "Congestion Tolls for Poisson Queuing Processes," Econometrica, Econometric Society, vol. 43(1), pages 81-92, January.
    21. Soroush Saghafian & Wallace J. Hopp & Mark P. Van Oyen & Jeffrey S. Desmond & Steven L. Kronick, 2012. "Patient Streaming as a Mechanism for Improving Responsiveness in Emergency Departments," Operations Research, INFORMS, vol. 60(5), pages 1080-1097, October.
    22. Jing Dong & Elad Yom-Tov & Galit B. Yom-Tov, 2019. "The Impact of Delay Announcements on Hospital Network Coordination and Waiting Times," Management Science, INFORMS, vol. 67(5), pages 1969-1994, May.
    23. Oualid Jouini & Zeynep Aksin & Yves Dallery, 2011. "Call Centers with Delay Information: Models and Insights," Post-Print hal-00680769, HAL.
    24. Rouba Ibrahim & Ward Whitt, 2009. "Real-Time Delay Estimation in Overloaded Multiserver Queues with Abandonments," Management Science, INFORMS, vol. 55(10), pages 1729-1742, October.
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