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

• URL: http://arxiv.org/abs/2408.08024v1
• Date: Thu, 15 Aug 2024 08:47:35 GMT
• Title: Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx
• Authors: Ana Fernández del Río, Michael Brennan Leong, Paulo Saraiva, Ivan Nazarov, Aditya Rastogi, Moiz Hassan, Dexian Tang, África Periáñez,
• Abstract summary: 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. Our framework is tested through a series of experiments with product recommendations tailored to each pharmacy based on real-time information on their purchasing history.
• Score: 2.7180345210658814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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