Counterfactual Prescriptions Via Hierarchical ML For Missed Chemotherapy Appointment Prevention

Abstract

Missed medical appointments, including cancellations and no-shows, disrupt clinical workflows, reduce efficiency, and compromise patient care. Using 1.8 million chemotherapy appointments from the Dana-Farber Cancer Institute, this study develops a hierarchical machine learning framework that first predicts cancellations and then no-shows among remaining cases. Combining operational and temporal features, the model achieves F1-scores of 0.76 and 0.82, outperforming a multinomial baseline by 7–10 points on minority classes. To extend interpretability, we infer missed-visit reasons through semi-supervised learning on short-notice cancellations, training supervised predictors with weighted F1-scores of 0.57 and 0.54. Counterfactual analysis identifies modifiable scheduling factors—such as appointment timing and provider consistency—while showing that standard interventions like reminders are less effective than previously reported. These findings underscore the value of predictive models with prescriptive intent, highlighting their role in designing tailored, context-specific interventions to improve clinical efficiency and patient care.