Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx
This work addresses improving pharmaceutical supply chain management and healthcare outcomes through adaptive interventions in mobile health solutions, though it appears incremental as it applies existing RL methods to a specific domain.
The paper tackles personalizing user experiences in healthcare digital tools by developing a reinforcement learning platform, tested on the SwipeRx app for pharmacists, resulting in a significant increase in basket size.
This paper introduces a reinforcement learning (RL) platform that enhances end-to-end user journeys in healthcare digital tools through personalization. We explore a case study with SwipeRx, the most popular all-in-one app for pharmacists in Southeast Asia, demonstrating how the platform can be used to personalize and adapt user experiences. Our RL framework is tested through a series of experiments with product recommendations tailored to each pharmacy based on real-time information on their purchasing history and in-app engagement, showing a significant increase in basket size. By integrating adaptive interventions into existing mobile health solutions and enriching user journeys, our platform offers a scalable solution to improve pharmaceutical supply chain management, health worker capacity building, and clinical decision and patient care, ultimately contributing to better healthcare outcomes.