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Development and validation of a patient no-show predictive model at a primary care setting in Southern Brazil

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  • Henry Lenzi
  • Ângela Jornada Ben
  • Airton Tetelbom Stein

Abstract

Patient no-show is a prevalent problem in health care services leading to inefficient resources allocation and limited access to care. This study aims to develop and validate a patient no-show predictive model based on empirical data. A retrospective study was performed using scheduled appointments between 2011 and 2014 from a Brazilian public primary care setting. Fifty percent of the dataset was randomly assigned to model development, and 50% was assigned to validation. Predictive models were developed using stepwise naïve and mixed-effect logistic regression along with the Akaike Information Criteria to select the best model. The area under the ROC curve (AUC) was used to assess the best model performance. Of the 57,586 scheduled appointments in the period, 70.7% (n = 40,740) were evaluated including 5,637 patients. The prevalence of no-show was 13.0% (n = 5,282). The best model presented an AUC of 80.9% (95% CI 80.1–81.7). The most important predictors were previous attendance and same-day appointments. The best model developed from data already available in the scheduling system, had a good performance to predict patient no-show. It is expected the model to be helpful to overbooking decision in the scheduling system. Further investigation is needed to explore the effectiveness of using this model in terms of improving service performance and its impact on quality of care compared to the usual practice.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0214869
    DOI: 10.1371/journal.pone.0214869
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    References listed on IDEAS

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    1. David A Ellis & Rob Jenkins, 2012. "Weekday Affects Attendance Rate for Medical Appointments: Large-Scale Data Analysis and Implications," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-4, December.
    2. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
    3. Bjorn P. Berg & Michael Murr & David Chermak & Jonathan Woodall & Michael Pignone & Robert S. Sandler & Brian T. Denton, 2013. "Estimating the Cost of No-Shows and Evaluating the Effects of Mitigation Strategies," Medical Decision Making, , vol. 33(8), pages 976-985, November.
    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.
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    Cited by:

    1. 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).

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