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Article

Large-Scale No-Show Patterns and Distributions for Clinic Operational Research

1
Access and Clinic Administration Program (ACAP), U.S. Department of Veterans Affairs, Washington, DC 57741, USA
2
Veterans Engineering Resource Center, VA Pittsburgh Healthcare System, Pittsburgh, PA 15240, USA
3
Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA
4
Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA 15240, USA
5
Department of Medicine, Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Sampath Parthasarathy
Healthcare 2016, 4(1), 15; https://doi.org/10.3390/healthcare4010015
Received: 2 November 2015 / Revised: 22 January 2016 / Accepted: 2 February 2016 / Published: 16 February 2016
Patient no-shows for scheduled primary care appointments are common. Unused appointment slots reduce patient quality of care, access to services and provider productivity while increasing loss to follow-up and medical costs. This paper describes patterns of no-show variation by patient age, gender, appointment age, and type of appointment request for six individual service lines in the United States Veterans Health Administration (VHA). This retrospective observational descriptive project examined 25,050,479 VHA appointments contained in individual-level records for eight years (FY07-FY14) for 555,183 patients. Multifactor analysis of variance (ANOVA) was performed, with no-show rate as the dependent variable, and gender, age group, appointment age, new patient status, and service line as factors. The analyses revealed that males had higher no-show rates than females to age 65, at which point males and females exhibited similar rates. The average no-show rates decreased with age until 75–79, whereupon rates increased. As appointment age increased, males and new patients had increasing no-show rates. Younger patients are especially prone to no-show as appointment age increases. These findings provide novel information to healthcare practitioners and management scientists to more accurately characterize no-show and attendance rates and the impact of certain patient factors. Future general population data could determine whether findings from VHA data generalize to others. View Full-Text
Keywords: outpatient appointments; no-shows; frequent attenders; statistical analysis outpatient appointments; no-shows; frequent attenders; statistical analysis
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MDPI and ACS Style

Davies, M.L.; Goffman, R.M.; May, J.H.; Monte, R.J.; Rodriguez, K.L.; Tjader, Y.C.; Vargas, D.L. Large-Scale No-Show Patterns and Distributions for Clinic Operational Research. Healthcare 2016, 4, 15. https://doi.org/10.3390/healthcare4010015

AMA Style

Davies ML, Goffman RM, May JH, Monte RJ, Rodriguez KL, Tjader YC, Vargas DL. Large-Scale No-Show Patterns and Distributions for Clinic Operational Research. Healthcare. 2016; 4(1):15. https://doi.org/10.3390/healthcare4010015

Chicago/Turabian Style

Davies, Michael L., Rachel M. Goffman, Jerrold H. May, Robert J. Monte, Keri L. Rodriguez, Youxu C. Tjader, and Dominic L. Vargas. 2016. "Large-Scale No-Show Patterns and Distributions for Clinic Operational Research" Healthcare 4, no. 1: 15. https://doi.org/10.3390/healthcare4010015

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