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Obesity Surgery

, Volume 29, Issue 1, pp 40–47 | Cite as

Predicting Patient No-show Behavior: a Study in a Bariatric Clinic

  • Leila F. Dantas
  • Silvio Hamacher
  • Fernando L. Cyrino Oliveira
  • Simone D. J. Barbosa
  • Fábio ViegasEmail author
Original Contributions

Abstract

Purpose

No-shows of patients to their scheduled appointments have a significant impact on healthcare systems, including lower clinical efficiency and higher costs. The purpose of this study was to investigate the factors associated with patient no-shows in a bariatric surgery clinic.

Materials and Methods

We performed a retrospective study of 13,230 records for 2660 patients in a clinic located in Rio de Janeiro, Brazil, over a 17-month period (January 2015–May 2016). Logistic regression analyses were conducted to explore and model the influence of certain variables on no-show rates. This work also developed a predictive model stratified for each medical specialty.

Results

The overall proportion of no-shows was 21.9%. According to multiple logistic regression, there is a significant association between the patient no-shows and eight variables examined. This association revealed a pattern in the increase of patient no-shows: appointment in the later hours of the day, appointments not in the summer months, post-surgery appointment, high lead time, higher no-show history, fewer numbers of previous appointments, home address 20 to 50 km away from the clinic, or scheduled for another specialty other than a bariatric surgeon. Age group, forms of payment, gender, and weekday were not significant predictors. Predictive models were developed with an accuracy of 71%.

Conclusion

Understanding the characteristics of patient no-shows allows making improvements in management practice, and the predictive models can be incorporated into the clinic dynamic scheduling system, allowing the use of a new appointment policy that takes into account each patient’s no-show probability.

Keywords

No-shows Bariatric clinic Appointment Healthcare Obesity 

Notes

Acknowledgments

The authors would like to thank all the staff from the clinic for their help with data collection.

Funding

This work was supported by the National Council for Scientific and Technological Development (CNPq) [grant numbers 169049/2017-5 to LFD, 306802/2015-5 and 403863/2016-3 to SH, 443595/2014-3 and 304843/2016-4 to FLCO, and 309828/2015-5 and 453996/2014-0 to SDJB]; the Carlos Chagas Filho Foundation (FAPERJ) [grant number E-26/202.806/2015 to FLCO]; the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001; and the Pontifical Catholic University of Rio de Janeiro.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval Statement

This article does not contain any studies with human participants or animals performed by any of the authors. For this type of study, formal consent is not required.

Informed Consent Statement

Does not apply.

Supplementary material

11695_2018_3480_MOESM1_ESM.docx (25 kb)
ESM 1 (DOCX 25 kb)
11695_2018_3480_MOESM2_ESM.docx (37 kb)
ESM 2 (DOCX 36 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial EngineeringPontifical Catholic University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.Department of InformaticsPontifical Catholic University of Rio de JaneiroRio de JaneiroBrazil
  3. 3.Institute of Gastro and Obesity SurgeryRio de JaneiroBrazil

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