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Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx

arXiv 2408.08024 ↗First posted 15 Aug 2024cs.LG cs.AI stat.MLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 24 of its 67 references that are in the map. First posted on arXiv 15 Aug 2024.

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24 of 67 references.

References · 24

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12 more
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  6. 2021The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerationscited by 11
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