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

Overview

Journal Obes Surg

Date 2018 Sep 14

PMID 30209668

Citations 13

Authors

Leila F Dantas

Silvio Hamacher

Fernando L Cyrino Oliveira

Simone D J Barbosa

Fabio Viegas

Affiliations

Soon will be listed here.

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.

Citing Articles

No-Show Rates at a Plastic Surgery Clinic: Insights From Appalachian Healthcare Systems.

Rahimpour A, Saurborn E, Murphy A, Hyde J, Weaver A, Munie S Cureus. 2025; 17(1):e76873.

PMID: 39897298 PMC: 11787945. DOI: 10.7759/cureus.76873.

Baseline 25(OH)D level is a prognostic indicator for bariatric surgery readmission: a matched retrospective cohort study.

Shang Y, Chen M, Wang T, Xia T Front Nutr. 2024; 11:1362258.

PMID: 38803446 PMC: 11128655. DOI: 10.3389/fnut.2024.1362258.

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms.

Deina C, Fogliatto F, da Silveira G, Anzanello M BMC Health Serv Res. 2024; 24(1):37.

PMID: 38183029 PMC: 10770919. DOI: 10.1186/s12913-023-10418-6.

Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital.

Ahmad Hamdan A, Abu Bakar A Malays J Med Sci. 2023; 30(5):169-180.

PMID: 37928795 PMC: 10624443. DOI: 10.21315/mjms2023.30.5.14.

Health care overbooking cost minimization model.

Almaktoom A Heliyon. 2023; 9(8):e18753.

PMID: 37560686 PMC: 10407751. DOI: 10.1016/j.heliyon.2023.e18753.

References

Torres O, Rothberg M, Garb J, Ogunneye O, Onyema J, Higgins T . Risk factor model to predict a missed clinic appointment in an urban, academic, and underserved setting. Popul Health Manag. 2014; 18(2):131-6. DOI: 10.1089/pop.2014.0047. View

Huang Y, Hanauer D . Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inform. 2014; 5(3):836-60. PMC: 4187098. DOI: 10.4338/ACI-2014-04-RA-0026. View

Daggy J, Lawley M, Willis D, Thayer D, Suelzer C, DeLaurentis P . Using no-show modeling to improve clinic performance. Health Informatics J. 2011; 16(4):246-59. DOI: 10.1177/1460458210380521. View

Lehmann T, Aebi A, Lehmann D, Balandraux Olivet M, Stalder H . Missed appointments at a Swiss university outpatient clinic. Public Health. 2007; 121(10):790-9. DOI: 10.1016/j.puhe.2007.01.007. View

Griffin S . Lost to follow-up: the problem of defaulters from diabetes clinics. Diabet Med. 1998; 15 Suppl 3:S14-24. DOI: 10.1002/(sici)1096-9136(1998110)15:3+<s14::aid-dia725>3.3.co;2-9. View

Pontiroli A, Fossati A, Vedani P, Fiorilli M, Folli F, Paganelli M . Post-surgery adherence to scheduled visits and compliance, more than personality disorders, predict outcome of bariatric restrictive surgery in morbidly obese patients. Obes Surg. 2008; 17(11):1492-7. DOI: 10.1007/s11695-008-9428-8. View

Alaeddini A, Yang K, Reddy C, Yu S . A probabilistic model for predicting the probability of no-show in hospital appointments. Health Care Manag Sci. 2011; 14(2):146-57. DOI: 10.1007/s10729-011-9148-9. View

Sockalingam S, Cassin S, Hawa R, Khan A, Wnuk S, Jackson T . Predictors of post-bariatric surgery appointment attendance: the role of relationship style. Obes Surg. 2013; 23(12):2026-32. DOI: 10.1007/s11695-013-1009-9. View

Coleman M, Medford-Davis L, Atassi O, Siler-Fisher A, Reitman C . Injury type and emergency department management of orthopaedic patients influences follow-up rates. J Bone Joint Surg Am. 2014; 96(19):1650-8. DOI: 10.2106/JBJS.M.01481. View

10.

Kheirkhah P, Feng Q, Travis L, Tavakoli-Tabasi S, Sharafkhaneh A . Prevalence, predictors and economic consequences of no-shows. BMC Health Serv Res. 2016; 16:13. PMC: 4714455. DOI: 10.1186/s12913-015-1243-z. View

11.

Peng Y, Erdem E, Shi J, Masek C, Woodbridge P . Large-scale assessment of missed opportunity risks in a complex hospital setting. Inform Health Soc Care. 2014; 41(2):112-27. DOI: 10.3109/17538157.2014.965303. View

12.

Lee V, Earnest A, Chen M, Krishnan B . Predictors of failed attendances in a multi-specialty outpatient centre using electronic databases. BMC Health Serv Res. 2005; 5:51. PMC: 1190171. DOI: 10.1186/1472-6963-5-51. View

13.

Toussi R, Fujioka K, Coleman K . Pre- and postsurgery behavioral compliance, patient health, and postbariatric surgical weight loss. Obesity (Silver Spring). 2009; 17(5):996-1002. DOI: 10.1038/oby.2008.628. View

14.

Parikh A, Gupta K, Wilson A, Fields K, Cosgrove N, Kostis J . The effectiveness of outpatient appointment reminder systems in reducing no-show rates. Am J Med. 2010; 123(6):542-8. DOI: 10.1016/j.amjmed.2009.11.022. View

15.

Kempny A, Diller G, Dimopoulos K, Alonso-Gonzalez R, Uebing A, Li W . Determinants of outpatient clinic attendance amongst adults with congenital heart disease and outcome. Int J Cardiol. 2015; 203:245-50. DOI: 10.1016/j.ijcard.2015.10.081. View

16.

Wheeler E, Prettyman A, Lenhard M, Tran K . Adherence to outpatient program postoperative appointments after bariatric surgery. Surg Obes Relat Dis. 2008; 4(4):515-20. DOI: 10.1016/j.soard.2008.01.013. View

17.

Sivagnanam P, Rhodes M . The importance of follow-up and distance from centre in weight loss after laparoscopic adjustable gastric banding. Surg Endosc. 2010; 24(10):2432-8. DOI: 10.1007/s00464-010-0970-9. View

18.

Huang Y, Hanauer D . Time dependent patient no-show predictive modelling development. Int J Health Care Qual Assur. 2016; 29(4):475-88. DOI: 10.1108/IJHCQA-06-2015-0077. View

19.

Bennett K, Baxley E . The effect of a carve-out advanced access scheduling system on no-show rates. Fam Med. 2009; 41(1):51-6. View

20.

Dantas L, Fleck J, Cyrino Oliveira F, Hamacher S . No-shows in appointment scheduling - a systematic literature review. Health Policy. 2018; 122(4):412-421. DOI: 10.1016/j.healthpol.2018.02.002. View