AI DRIVEN HEALTHCARE AT SCALE: PERSONALIZATION AND PREDICTIVE TOOLS IN THE CVS HEALTH MOBILE APP
DOI:
https://doi.org/10.15662/IJRAI.2025.0803004Keywords:
AI Personalization, Predictive Analytics, Medication Adherence, Mobile Health, CVS Health, Patient EngagementAbstract
In this study, we explore the application of AI‑enabled personalization and predictive analytics in the CVSHealth mobile app to drive personalized medication adherence, care navigation and chronic disease management. We performed a mixed‑methods evaluation, involving quantitative analysis of app usage as well as clinical outcomes, and qualitative interviews with users and clinicians. The approach itself involved capturing app engagement behavior (e.g., refill‑reminder engagement, predictive refill accuracy, and health outcome proxies [e.g., blood‑pressure readings]) in real time from 30,000 users, over a 6‑month period. This was complemented by semi‑structured interviews with 20 patients and 10 pharmacists to evaluate perceived usability and impact. Findings Findings indicate a 28% increase in rates of on-time medication refill among patients engaging with the personalized “health to‑do” list and AI chatbot when compared to a control group. Predictive refill notifications achieved a sensitivity / specificity of 83% (±3days) for risk of drug gaps. Qualitative feedback demonstrated improved patient confidence and provider efficiency, though concerns for transparency and app complexity emerged. This is consistent with the recommendation that introducing AI personalization at scale will lead to substantial improvements in adherence and pharmacy efficienciesIT optimizations across CVS’s digital ecosystem. We close with suggestions for model transparency, adaptive UI design, and clinical integration strategies to enable broad-based deployment and equity.
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