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Patient no-show predictive model development using multiple data sources for an effective overbooking approach.

TitlePatient no-show predictive model development using multiple data sources for an effective overbooking approach.
Publication TypeJournal Article
Year of Publication2014
AuthorsHuang, Y, Hanauer, DA
JournalAppl Clin Inform
Volume5
Issue3
Pagination836-60
Date Published2014
ISSN1869-0327
Abstract

BACKGROUND: Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity.OBJECTIVE: To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows.METHODS: Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost.RESULTS: From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods.CONCLUSIONS: This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient's show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.

DOI10.4338/ACI-2014-04-RA-0026
Alternate JournalAppl Clin Inform
PubMed ID25298821
PubMed Central IDPMC4187098
People: 
David Hanauer
University of Michigan Comprehensive Cancer Center at North Campus Reserach Complex
1600 Huron Parkway, Bldg 100, Rm 100 
Mailing Address: 2800 Plymouth Rd, NCRC 100-1004
Ann Arbor, MI 48109-2800 
Ph. (734) 764-8848 Fax. (734) 615-0517
Please acknowledge the Cancer Center Support Grant (P30 CA046592) when publishing manuscripts or abstracts that utilized the services of the University of Michigan's Comprehensive Cancer Center's Shared Resource: Cancer Informatics.
Suggested language: "Research reported in this [publication/press release] was supported by the National Cancer Institute of the National Institutes of Health under award number P30CA046592."

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