A statistical model for the prediction of patient non-attendance in a primary care clinic

Xiuli Qu, Ronald L. Rardin, Lisa Tieman, Hong Wan, Julie Ann Stuart Williams, Deanna Willis, Marc Rosenman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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 languageEnglish
Title of host publication2006 IIE Annual Conference and Exhibition
StatePublished - 2006
Event2006 IIE Annual Conference and Exposition - Orlando, FL, United States
Duration: May 20 2006May 24 2006

Other

Other2006 IIE Annual Conference and Exposition
CountryUnited States
CityOrlando, FL
Period5/20/065/24/06

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Keywords

  • Healthcare
  • Logistic regression
  • Patient non-attendance prediction

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Qu, X., Rardin, R. L., Tieman, L., Wan, H., Williams, J. A. S., Willis, D., & Rosenman, M. (2006). A statistical model for the prediction of patient non-attendance in a primary care clinic. In 2006 IIE Annual Conference and Exhibition