Same day cancellations of surgery have adverse effects on both patients and health care systems. To date, the majority of research has evaluated reasons for same day cancellation, and relatively little is known about risk factors for cancellation. The aim of this study is to develop and evaluate the accuracy of a model for preoperatively predicting which patients are at risk for experiencing same day cancellation. While accurately predicting which patients are likely to experience same day cancellation remains challenging, predictive models may aid in the early identification of patients at risk for cancellation. Future studies are required to assess whether the use of predictive analytics leads to reduced cancellation rates in practice.
Keywords: Elective surgery; Operational efficiency; Patient experience; Predictive model; Same day cancellation.