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

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,...

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Autores principales: Davies, Michael L., Goffman, Rachel M., May, Jerrold H., Monte, Robert J., Rodriguez, Keri L., Tjader, Youxu C., Vargas, Dominic L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4934549/
https://www.ncbi.nlm.nih.gov/pubmed/27417603
http://dx.doi.org/10.3390/healthcare4010015
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author Davies, Michael L.
Goffman, Rachel M.
May, Jerrold H.
Monte, Robert J.
Rodriguez, Keri L.
Tjader, Youxu C.
Vargas, Dominic L.
author_facet Davies, Michael L.
Goffman, Rachel M.
May, Jerrold H.
Monte, Robert J.
Rodriguez, Keri L.
Tjader, Youxu C.
Vargas, Dominic L.
author_sort Davies, Michael L.
collection PubMed
description 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.
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spelling pubmed-49345492016-07-12 Large-Scale No-Show Patterns and Distributions for Clinic Operational Research Davies, Michael L. Goffman, Rachel M. May, Jerrold H. Monte, Robert J. Rodriguez, Keri L. Tjader, Youxu C. Vargas, Dominic L. Healthcare (Basel) Article 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. MDPI 2016-02-16 /pmc/articles/PMC4934549/ /pubmed/27417603 http://dx.doi.org/10.3390/healthcare4010015 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Davies, Michael L.
Goffman, Rachel M.
May, Jerrold H.
Monte, Robert J.
Rodriguez, Keri L.
Tjader, Youxu C.
Vargas, Dominic L.
Large-Scale No-Show Patterns and Distributions for Clinic Operational Research
title Large-Scale No-Show Patterns and Distributions for Clinic Operational Research
title_full Large-Scale No-Show Patterns and Distributions for Clinic Operational Research
title_fullStr Large-Scale No-Show Patterns and Distributions for Clinic Operational Research
title_full_unstemmed Large-Scale No-Show Patterns and Distributions for Clinic Operational Research
title_short Large-Scale No-Show Patterns and Distributions for Clinic Operational Research
title_sort large-scale no-show patterns and distributions for clinic operational research
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4934549/
https://www.ncbi.nlm.nih.gov/pubmed/27417603
http://dx.doi.org/10.3390/healthcare4010015
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