Patient-Centric Markov-Chain Framework for Predicting Medication Adherence Using De-Identified Data

Long-term adherence to prescribed therapies remains a persistent challenge in chronic and ultra-rare conditions where clinical outcomes depend on continuous medication use. Even brief gaps in therapy can compromise disease control, yet patients frequently encounter structural barriers including high out-of-pocket costs, prior-authorization (PA) delays, annual re-verification cycles, and refill logistics that disrupt persistence. This study evaluates a patient-centric Markov-chain framework for adherence risk stratification trained on eight years of de-identified specialty-pharmacy data representing 1,200 active patients. Certified data aggregators supply longitudinally linkable, tokenized data to preserve privacy while enabling multi-year adherence trajectory modeling. Transition probabilities between fully adherent, partially adherent, and lapsed states are estimated and adjusted using covariates such as age, duration on therapy, refill cadence, PA processing time, copay burden, and foundation-assistance status. The model achieves an accuracy of 0.82, 0.79 F1-score, and an AUC of 0.87, with 95% confidence intervals estimated via bootstrapping across cross-validated folds. Results highlight cost exposure, administrative friction, and mid-treatment duration (1-5 years) as dominant predictors of future non-adherence. Findings demonstrate how probabilistic modeling of privacy-preserved real-world data can support equitable patient-assistance strategies, identifying individuals vulnerable to systemic barriers rather than emphasizing commercial performance metrics.