AI-driven digital merchandising: Optimizing retail healthcare platforms for improved patient outcomes

Babalola, Oladeji (2025) AI-driven digital merchandising: Optimizing retail healthcare platforms for improved patient outcomes. International Journal of Science and Research Archive, 15 (2). pp. 1201-1221. ISSN 2582-8185

The intersection of artificial intelligence, digital merchandising, and healthcare retail represents a transformative frontier in public health accessibility. This research introduces novel approaches to healthcare product discovery and engagement through AI-driven merchandising systems specifically designed for retail healthcare environments. By analyzing consumer interaction patterns across digital health platforms, this study presents the Adaptive Healthcare Merchandising Framework (AHMF), an original methodology that dynamically optimizes product presentation based on health-seeking behaviours, medication adherence patterns, and preventative care needs. Implementation of the framework across three digital healthcare platforms demonstrated significant improvements: a 27% increase in preventative healthcare product discovery, 18% enhancement in medication adherence through improved refill experiences, and a 32% reduction in abandoned healthcare purchases. The integration of machine learning algorithms with healthcare-specific merchandising principles created personalized digital experiences that specifically address barriers to healthcare access, particularly for chronic condition management and preventative care. This research advances the field by establishing new standards for patient-centred digital experiences in healthcare retail, with direct implications for public health outcomes through improved medication adherence and preventative care adoption. The methodologies developed, provides a scalable approach to healthcare accessibility that bridges technological innovation with essential public health priorities.