Large-scale assessment of missed opportunity risks in a complex hospital setting

In this research, we apply a large-scale logistic regression analysis to assess the patient missed opportunity risks at a complex VA (US Department of Veterans Affairs) hospital in three categories, namely, no-show alone, no-show combined with late patient cancellation and no-show combined with late...

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Bibliographic Details
Published in:Informatics for health & social care Vol. 41; no. 2; pp. 112 - 127
Main Authors: Peng, Yidong, Erdem, Ergin, Shi, Jing, Masek, Christopher, Woodbridge, Peter
Format: Journal Article
Language:English
Published: England Informa Healthcare 02.04.2016
Subjects:
Adolescent
Adult
Aged
Appointments and Schedules
Female
Hospitals
Hospitals, Veterans
Humans
Late appointment cancellation
Logistic Models
logistic regression
Male
Middle Aged
missed opportunity
no-show
Young Adult
Late appointment cancellation
missed opportunity
VA
logistic regression
no-show
ISSN:1753-8157, 1753-8165, 1753-8165
Online Access:Get full text
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Summary:In this research, we apply a large-scale logistic regression analysis to assess the patient missed opportunity risks at a complex VA (US Department of Veterans Affairs) hospital in three categories, namely, no-show alone, no-show combined with late patient cancellation and no-show combined with late patient and clinic cancellations. The analysis includes unique explanatory variables related to VA patients for predicting missed opportunity risks. Furthermore, we develop two aggregated weather indices by combining many weather measures and include them as explanatory variables. The results indicate that most of the explanatory variables considered are significant factors for predicting the missed opportunity risks. Patients with afternoon appointment, higher percentage service connected, and insurance, married patients, shorter lead time and appointments with longer appointment length are consistently related to lower risks of missed opportunity. Furthermore, the VA patient-related factors and the two proposed weather indices are useful predictors for the risks of no-show and patient cancellation. More importantly, this research presents an effective procedure for VA hospitals and clinics to analyze the missed opportunity risks within the complex VA information technology system, and help them to develop proper interventions to mitigate the adverse effects caused by the missed opportunities.
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ISSN:1753-8157
1753-8165
1753-8165
DOI:10.3109/17538157.2014.965303