A hybrid prediction model for no-shows and cancellations of outpatient appointments

A no-show occurs when a scheduled patient neither keeps nor cancels the appointment. A cancellation happens when individuals contact the clinic and cancel their scheduled appointments. Such disruptions not only cause inconvenience to hospital management, they also have a significant impact on the revenue, cost and resource utilization for almost all of the healthcare systems. In this paper, we develop a hybrid probabilistic model based on multinomial logistic regression and Bayesian inference to predict accurately the probability of no-show and cancellation in real-time. First, a multinomial logistic regression model is built based on the entire population’s general social and demographic information to provide initial estimates of no-show and cancellation probabilities. Next, the estimated probabilities from the logistic model are transformed into a bivariate Dirichlet distribution, which is used as the prior distribution of a Bayesian updating mechanism to personalize the initial estimates for each patient based on his/her attendance record. In addition, to further improve the estimates, prior to applying the Bayesian updating mechanism, each appointment in the database is weighted based on its recency, weekday of occurrence, and clinic type. The effectiveness of the proposed approach is demonstrated using healthcare data collected at a medical center. We also discuss the advantages of the proposed hybrid model and describe possible real-world applications.