Predicting non-attendance for elective surgeries using machine learning

Non-attendance to elective surgeries contributes to inefficient healthcare delivery and utilization. Prediction models may be limited when outcome definitions combine patient-related factors with hospital-initiated cancellations, potentially obscuring the behavioral patterns underlying true patient non-attendance. This study aims to develop and evaluate machine learning models for predicting patient-related non-attendance to elective surgeries using electronic health records.

Electronic health record data from 24,507 elective surgery appointments scheduled between 2017 and 2022 at Shamir Medical Center, Israel, were analyzed. Hospital-initiated cancellations were excluded, and non-attendance outcomes were defined based on patient-related factors. Two prediction targets were evaluated: patient-related non-attendance at any time before surgery and within 24 h of the scheduled procedure. Six machine learning models, including Logistic regression, decision tree, random forest, and gradient boosting models were trained and evaluated. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), precision, recall, the F1 measure, the area under the precision-recall curve, and the Brier score.

The best-performing models, gradient boosting and random forest, achieved AUC values of approximately 0.93 for patient-related non-attendance both at any time before surgery and within 24 h of the scheduled procedure. Previous cancellation history was the strongest single predictor and alone achieved substantial predictive accuracy discrimination (AUC = 0.88), approaching that of the full machine learning models. By comparison, previously published models for surgical cancellation and non-attendance have reported AUC values that did not exceed 0.80. This improvement may be attributable to the isolation of patient-related cancellations from hospital-initiated cancellations, reducing outcome heterogeneity.

Accurate prediction of patient-related non-attendance to elective surgeries is feasible using electronic health record data. These findings highlight the importance of outcome definition in healthcare prediction models and suggest that reducing outcome heterogeneity may improve identification of meaningful behavioral patterns. Simple indicators, such as previous cancellation history, may support targeted interventions to improve healthcare resource utilization.