Machine Learning Models for Predicting Patient Treatment Switching Using Claims Data

Abstract

Treatment switching - including discontinuation, add on therapy, and brand to generic substitution—can have meaningful clinical and economic consequences for patients, payers, and health systems. Administrative claims databases provide large scale longitudinal records of medication use and health care utilization, enabling the study of real world treatment trajectories; however, traditional analytic approaches often struggle to translate high dimensional claims signals into timely and actionable switching predictions. This paper reviews and synthesizes machine learning (ML) strategies that leverage claims data to predict treatment switching across multiple therapeutic areas, including rheumatology, neurology, psychiatry, and cardiovascular disease. The review summarizes major methodological approaches—tree based ensembles, regularized regression, neural networks, and temporal/sequential models—and highlights common feature engineering practices for handling high dimensionality and class imbalance. Across the referenced studies, ML approaches generally improve predictive performance compared with simpler baselines, with clinical phenotype proxies, prior medication burden, utilization intensity, and adherence patterns frequently emerging as important predictive signals. The paper also discusses implications for clinical decision support, real world evidence generation, and market access strategy. Persistent challenges include sparse clinical granularity in administrative data, heterogeneity in switching definitions, class imbalance, and limited cross system transportability; integrating claims with richer clinical sources (e.g., EHR) and performing prospective and external validation remain important directions for future work.