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No-shows in appointment scheduling – a systematic literature review

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  • Dantas, Leila F.
  • Fleck, Julia L.
  • Cyrino Oliveira, Fernando L.
  • Hamacher, Silvio

Abstract

No-show appointments significantly impact the functioning of healthcare institutions, and much research has been performed to uncover and analyze the factors that influence no-show behavior. In spite of the growing body of literature on this issue, no synthesis of the state-of-the-art is presently available and no systematic literature review (SLR) exists that encompasses all medical specialties. This paper provides a SLR of no-shows in appointment scheduling in which the characteristics of existing studies are analyzed, results regarding which factors have a higher impact on missed appointment rates are synthetized, and comparisons with previous findings are performed. A total of 727 articles and review papers were retrieved from the Scopus database (which includes MEDLINE), 105 of which were selected for identification and analysis. The results indicate that the average no-show rate is of the order of 23%, being highest in the African continent (43.0%) and lowest in Oceania (13.2%). Our analysis also identified patient characteristics that were more frequently associated with no-show behavior: adults of younger age; lower socioeconomic status; place of residence is distant from the clinic; no private insurance. Furthermore, the most commonly reported significant determinants of no-show were high lead time and prior no-show history.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:hepoli:v:122:y:2018:i:4:p:412-421
    DOI: 10.1016/j.healthpol.2018.02.002
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    2. Tito Homem-de-Mello & Qingxia Kong & Rodrigo Godoy-Barba, 2022. "A Simulation Optimization Approach for the Appointment Scheduling Problem with Decision-Dependent Uncertainties," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2845-2865, September.
    3. Henry Lenzi & Ângela Jornada Ben & Airton Tetelbom Stein, 2019. "Development and validation of a patient no-show predictive model at a primary care setting in Southern Brazil," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-14, April.
    4. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G. & Foster, Krista M., 2020. "The effect of cancelled appointments on outpatient clinic operations," European Journal of Operational Research, Elsevier, vol. 284(3), pages 847-860.
    5. Kılıç, Hakan & Güneş, Evrim Didem, 2024. "Patient adherence in healthcare operations: A narrative review," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    6. Dominik Schreyer & Sascha L. Schmidt & Benno Torgler, 2020. "Using reminders with different reward opportunities to reduce no-show behavior: Empirical evidence from a large-scale field experiment in professional sport," CREMA Working Paper Series 2020-19, Center for Research in Economics, Management and the Arts (CREMA).
    7. Soltani, Mohamad & Samorani, Michele & Kolfal, Bora, 2019. "Appointment scheduling with multiple providers and stochastic service times," European Journal of Operational Research, Elsevier, vol. 277(2), pages 667-683.

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