title: A probabilistic model for predicting the probability of no-show in hospital appointments.
abstract: The number of no-shows has a significant impact on the revenue, cost and resource utilization for almost all healthcare systems. In this study we develop a hybrid probabilistic model based on logistic regression and empirical Bayesian inference to predict the probability of no-shows in real time using both general patient social and demographic information and individual clinical appointments attendance records. The model also considers the effect of appointment date and clinic type. The effectiveness of the proposed approach is validated based on a patient dataset from a VA medical center. Such an accurate prediction model can be used to enable a precise selective overbooking strategy to reduce the negative effect of no-shows and to fill appointment slots while maintaining short wait times.
authors.affiliation: Department of Industrial & Systems Engineering, Wayne State University, Detroit, MI 48202, USA. adel.alaeddini@gmail.com
authors.forename: Adel
authors.initials: A
authors.lastname: Alaeddini
authors.forename: Kai
authors.initials: K
authors.lastname: Yang
authors.forename: Chandan
authors.initials: C
authors.lastname: Reddy
authors.forename: Susan
authors.initials: S
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001071:Appointments and Schedules; D001499:Bayes Theorem; D006801:Humans; D016015:Logistic Models; D010044:Outpatient Clinics, Hospital / Q000706:statistics & numerical data*; D011336:Probability; D012959:Socioeconomic Factors; D014481:United States; D014493:United States Department of Veterans Affairs / Q000706:statistics & numerical data