From prediction to practice: machine learning models and real-world interventions for magnetic resonance imaging appointment no-shows in a hospital setting.

OBJECTIVE: To develop a machine learning (ML) model to predict magnetic resonance imaging (MRI) appointment no-shows and test the effectiveness of a targeted phone call intervention in reducing the no-show rate. MATERIALS AND METHODS: Outpatient MRI appointments from a university hospital (2015-2021) were used to train and compare Logistic Regression, Random Forest, and XGBoost models. The best-performing model underwent a 13-month prospective evaluation, followed by a 9-month intervention study where high-risk patients were randomized to a phone call reminder or a control group. RESULTS: Our dataset included 38 141 appointments (16.6% no-show) in total. On a held-out validation dataset, XGBoost performed best (AUROC 0.65; AUPRC 0.29). In a prospective evaluation round (5882 appointments), performance decreased (AUROC 0.61; AUPRC 0.24), due in part to observed data drift in appointment types and reasons. The intervention study (274 patients called, 158 answered, 116 not reached; 460 control appointments) showed no overall change in no-show rate (no-show: 21.9% intervention vs 22.0% control; P = .48). Within the intervention group, answering the call was associated with lower no-show rates (18.4% of those reached vs 26.7% not reached). DISCUSSION: Models identified high-risk patients, but translating predictions into attendance improvements proved challenging. Effectiveness was limited by data drift and intervention reach. Results suggest value in dual-prediction strategies (including potential intervention responsiveness) and continuous model monitoring. CONCLUSION: ML can identify likely no-shows, but reducing missed appointments requires robust, up-to-date models and interventions that reliably reach patients; more nuanced, targeted engagement may yield greater impact than phone calls alone.