Abstract
High patient non-attendance rates cause critical problems for many outpatient clinics in the United States. For historical patient attendance data in a primary care clinic, six categorical factors are analyzed in this paper: appointment type, session, patient attendance history, insurance, age group and weather. The main effects of session, insurance, age group and weather are statistically significant, and nine 2-factor interaction effects of the six factors are significant too. Furthermore, using logistic regression, a statistical model is built to predict the non-attendance rate of each classified group of appointments.
Original language | English (US) |
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Title of host publication | 2006 IIE Annual Conference and Exposition |
State | Published - Dec 1 2006 |
Event | 2006 IIE Annual Conference and Exposition - Orlando, FL, United States Duration: May 20 2006 → May 24 2006 |
Other
Other | 2006 IIE Annual Conference and Exposition |
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Country/Territory | United States |
City | Orlando, FL |
Period | 5/20/06 → 5/24/06 |
Keywords
- Healthcare
- Logistic regression
- Patient non-attendance prediction
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering