Leveraging Big Data Analytics for Personalized Medication Adherence Strategies

Published Paper: PDF

DOI: https://doi.org/10.63345/ijrmp.v14.i7.3

Shalu Jain

 Maharaja Agrasen Himalayan Garhwal University

Uttarakhand, India

Abstract

Medication non-adherence remains one of the costliest and most complex challenges in contemporary health systems, leading to avoidable hospitalizations, therapeutic failures, antimicrobial resistance, and inflated insurance premiums. Traditional one-size-fits-all reminder systems and counseling sessions often underperform because they ignore the heterogeneity of patient behaviors, socio-economic realities, comorbidities, and digital literacies. This manuscript proposes and elaborates on a comprehensive framework that leverages big data analytics—integrating electronic health records (EHRs), pharmacy refill logs, wearable/IoT streams, mobile app engagement metrics, and social determinants of health—to generate individualized adherence risk profiles and just-in-time adaptive interventions (JITAIs). Using a mixed-methods, explanatory-sequential design, we outline data engineering pipelines, feature engineering strategies, and model-building approaches (gradient boosting, temporal deep learning, causal forests) that predict day-level adherence risk with high sensitivity while preserving fairness and privacy. We complement quantitative modeling with qualitative inquiry to capture motivational triggers and barriers uncovered through patient interviews and digital ethnography. The results section simulates findings from a multi-site cohort (N = 3,200 chronic-disease patients) showing that personalized nudges, dosage-synced reminders, and family-involved reinforcement improved medication-possession ratio (MPR) by 14.7% and reduced 6-month readmissions by 9.2% compared to standard care. Ethical, regulatory, and implementation considerations—including algorithmic bias auditing, consent layers, and explainable AI dashboards for clinicians—are critically discussed. The manuscript concludes that big data–driven personalization is not merely a technological solution but an interdisciplinary endeavor requiring clinical insight, behavioral science, robust data governance, and patient co-design. The proposed roadmap provides actionable guidance for hospitals, insurers, digital therapeutics start-ups, and public health agencies aiming to translate predictive insights into humane, context-sensitive adherence support.

Keywords

Big data analytics; medication adherence; personalized interventions; predictive modeling; electronic health records; wearable sensors; just-in-time adaptive interventions; explainable AI; health informatics; digital therapeutics

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