Predictive Analytics for Personalized Medication Adherence

Abstract

Medication non-adherence remains one of the most persistent barriers to effective healthcare, contributing to disease progression, avoidable hospitalizations, and increased healthcare expenditure. Existing predictive models primarily estimate whether a patient will adhere to treatment but rarely account for the dynamic behavioral transitions that occur throughout long-term therapy. This study addresses this limitation by proposing a predictive analytics framework centered on temporal adherence trajectories rather than static adherence classification. The proposed research integrates longitudinal electronic health records, pharmacy refill behavior, wearable-derived activity indicators, and patient-reported outcomes to forecast adherence deterioration before missed medication events occur. Recent advances in explainable artificial intelligence and temporal deep learning are incorporated to improve both predictive performance and clinical interpretability. The study aims to support proactive clinical interventions by identifying individualized adherence risk patterns across different stages of treatment. The framework emphasizes transparent decision support suitable for integration into digital health ecosystems. This manuscript establishes the conceptual foundation and literature context for developing next-generation personalized medication adherence prediction systems