Hauxhurst Presentation (Spring 2022)
- Author (aut): Hauxhurst, Spencer
- Thesis director: Arquiza, Apollo
- Committee member: Sharer, Rustan
- Contributor (ctb): Barrett, The Honors College
- Contributor (ctb): Dean, W.P. Carey School of Business
In the following paper, I aim to form relationships between different patient factors and no-show rates. The culmination of these relationships will then be used in a logistic regression model. Data collected from a survey at 26 HonorHealth clinics were analyzed using odds ratios and relative risk methods. Of 310,307 visits collected, 22,280 of them were no shows (7.2%), an 11% decrease from national averages (18.8%). This fueled the study, along with a grant filed by HonorHealth looking at the impact of telehealth on the working poor. A binary logistic regression method was run over the data, and less than 1% of patients' no-shows were predicted correctly. By adding factors, and improving the diversity in the data collected, model accuracy can be improved.