IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i6p3288-d522032.html
   My bibliography  Save this article

Evaluation of Patient No-Shows in a Tertiary Hospital: Focusing on Modes of Appointment-Making and Type of Appointment

Author

Listed:
  • Mi Young Suk

    (Severance Children’s Hospital, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
    These authors contributed equally to this study.)

  • Bomgyeol Kim

    (Department of Public Health, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
    These authors contributed equally to this study.)

  • Sang Gyu Lee

    (Department of Preventive Medicine, College of Medicine, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Chang Hoon You

    (Seoul Health Foundation, 31 Maebongsan-ro, Mapo-gu, Seoul 03909, Korea)

  • Tae Hyun Kim

    (Department of Healthcare Management, Graduate School of Public Health, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

Abstract

No-show appointments waste resources and decrease the sustainability of care. This study is an attempt to evaluate patient no-shows based on modes of appointment-making and types of appointments. We collected hospital information system data and appointment data including characteristics of patients, service providers, and clinical visits over a three-month period (1 September 2018 to 30 November 2018), at a large tertiary hospital in Seoul, Korea. We used multivariate logistic regression analyses to identify the factors associated with no-shows (Model 1). We further assessed no-shows by including the interaction term (“modes of appointment-making” X “type of appointment”) (Model 2). Among 1,252,127 appointments, the no-show rate was 6.12%. Among the modes of appointment-making, follow-up and online/telephone appointment were associated with higher odds of no-show compared to walk-in. Appointments for treatment and surgery had higher odds ratios of no-show compared to consultations. Tests for the interaction between the modes of appointment-making and type of appointment showed that follow-up for examination and online/telephone appointments for treatment and surgery had much higher odds ratios of no-shows. Other significant factors of no-shows include age, type of insurance, time of visit, lead time (time between scheduling and the appointment), type of visits, doctor’s position, and major diagnosis. Our results suggest that future approaches for predicting and addressing no-show should also consider and analyze the impact of modes of appointment-making and type of appointment on the model of prediction.

Suggested Citation

  • Mi Young Suk & Bomgyeol Kim & Sang Gyu Lee & Chang Hoon You & Tae Hyun Kim, 2021. "Evaluation of Patient No-Shows in a Tertiary Hospital: Focusing on Modes of Appointment-Making and Type of Appointment," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:3288-:d:522032
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/6/3288/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/6/3288/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wen-Ya Wang & Diwakar Gupta, 2011. "Adaptive Appointment Systems with Patient Preferences," Manufacturing & Service Operations Management, INFORMS, vol. 13(3), pages 373-389, July.
    2. Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    3. Ye Seol Lee & Tae Hyun Kim & Juyeong Kim, 2018. "Association between missed appointment and related factors of patients with cancer in a tertiary hospital," International Journal of Health Planning and Management, Wiley Blackwell, vol. 33(3), pages 873-882, July.
    4. Ayten Turkcan & Lynn Nuti & Po-Ching DeLaurentis & Zhiyi Tian & Joanne Daggy & Lingsong Zhang & Mark Lawley & Laura Sands, 2013. "No-Show Modeling for Adult Ambulatory Clinics," International Series in Operations Research & Management Science, in: Brian T. Denton (ed.), Handbook of Healthcare Operations Management, edition 127, chapter 0, pages 251-288, Springer.
    5. Renata Kopach & Po-Ching DeLaurentis & Mark Lawley & Kumar Muthuraman & Leyla Ozsen & Ron Rardin & Hong Wan & Paul Intrevado & Xiuli Qu & Deanna Willis, 2007. "Effects of clinical characteristics on successful open access scheduling," Health Care Management Science, Springer, vol. 10(2), pages 111-124, June.
    6. Adel Alaeddini & Kai Yang & Chandan Reddy & Susan Yu, 2011. "A probabilistic model for predicting the probability of no-show in hospital appointments," Health Care Management Science, Springer, vol. 14(2), pages 146-157, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tugba Cayirli & Pinar Dursun & Evrim D. Gunes, 2019. "An integrated analysis of capacity allocation and patient scheduling in presence of seasonal walk-ins," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 524-561, June.
    2. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    3. J. Dunstan & F. Villena & J.P. Hoyos & V. Riquelme & M. Royer & H. Ramírez & J. Peypouquet, 2023. "Predicting no-show appointments in a pediatric hospital in Chile using machine learning," Health Care Management Science, Springer, vol. 26(2), pages 313-329, June.
    4. Dantas, Leila F. & Fleck, Julia L. & Cyrino Oliveira, Fernando L. & Hamacher, Silvio, 2018. "No-shows in appointment scheduling – a systematic literature review," Health Policy, Elsevier, vol. 122(4), pages 412-421.
    5. Yu Fu & Amarnath Banerjee, 2021. "A Stochastic Programming Model for Service Scheduling with Uncertain Demand: an Application in Open-Access Clinic Scheduling," SN Operations Research Forum, Springer, vol. 2(3), pages 1-32, September.
    6. Hari Balasubramanian & Sebastian Biehl & Longjie Dai & Ana Muriel, 2014. "Dynamic allocation of same-day requests in multi-physician primary care practices in the presence of prescheduled appointments," Health Care Management Science, Springer, vol. 17(1), pages 31-48, March.
    7. Kılıç, Hakan & Güneş, Evrim Didem, 2024. "Patient adherence in healthcare operations: A narrative review," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    8. Sina Faridimehr & Saravanan Venkatachalam & Ratna Babu Chinnam, 2021. "Managing access to primary care clinics using scheduling templates," Health Care Management Science, Springer, vol. 24(3), pages 482-498, September.
    9. Matthias Deceuninck & Stijn Vuyst & Dieter Claeys & Dieter Fiems, 2021. "Appointment games with unobservable and observable schedules," Annals of Operations Research, Springer, vol. 307(1), pages 93-110, December.
    10. Shan Wang & Nan Liu & Guohua Wan, 2020. "Managing Appointment-Based Services in the Presence of Walk-in Customers," Management Science, INFORMS, vol. 66(2), pages 667-686, February.
    11. Dogru, Ali K. & Melouk, Sharif H., 2019. "Adaptive appointment scheduling for patient-centered medical homes," Omega, Elsevier, vol. 85(C), pages 166-181.
    12. Izady, Navid, 2019. "An integrated approach to demand and capacity planning in outpatient clinics," European Journal of Operational Research, Elsevier, vol. 279(2), pages 645-656.
    13. Katsumi Morikawa & Katsuhiko Takahashi & Daisuke Hirotani, 2018. "Performance evaluation of candidate appointment schedules using clearing functions," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 509-518, March.
    14. Nan Liu & Serhan Ziya & Vidyadhar G. Kulkarni, 2010. "Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 347-364, September.
    15. Gheno Gloria, 2018. "A new link function for the prediction of binary variables," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 4(2), pages 67-77, November.
    16. Ulla Vaeggemose & Emely Ek Blæhr & Anne Marie L. Thomsen & Viola Burau & Pia Vedel Ankersen & Stina Lou, 2020. "Fine for non‐attendance in public hospitals in Denmark: A survey of non‐attenders' reasons and attitudes," International Journal of Health Planning and Management, Wiley Blackwell, vol. 35(5), pages 1055-1064, September.
    17. Qu, Xiuli & Shi, Jing, 2011. "Modeling the effect of patient choice on the performance of open access scheduling," International Journal of Production Economics, Elsevier, vol. 129(2), pages 314-327, February.
    18. Genevieve E O'Connor, 2016. "Investigating the significance of insurance and income on health service utilization across generational cohorts," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 21(1), pages 19-33, March.
    19. Bowen Jiang & Jiafu Tang & Chongjun Yan, 2019. "A comparison of fixed and variable capacity-addition policies for outpatient capacity allocation," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 150-182, January.
    20. Eduardo Pérez & David P. Dzubay, 2021. "A scheduling-based methodology for improving patient perceptions of quality of care in intensive care units," Health Care Management Science, Springer, vol. 24(1), pages 203-215, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:3288-:d:522032. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.