Modeling the No-Show of Patients to Exam Appointments of Computed Tomography

Abstract Background: No-shows of patients have negative impacts on healthcare systems, such as resources’ underutilization, efficiency loss, and cost increase. Predicting no-show is key to develop strategies that counteract its effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital.Methods: We carried out a retrospective study on 8,382 appointments made to computed tomography (CT) exams between January and December 2017. Penalized logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients’ no-show. The predictive capabilities of the models were evaluated analyzing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC).Results: The no-show rate in computerized tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalized logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analyzed appearing as significant. One of the variables included in the model (number of exams scheduled in previous year) had not been previously reported in the related literature.Conclusions: Our findings may be used to guide the development of strategies to reduce the no-show of patients to exam appointments.