Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx
<|reference_start|>Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx: 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.<|reference_end|>
@article{del río2024adaptive,
    title={Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning:
  Insights from SwipeRx},
    author={Ana Fern'andez del R'io, Michael Brennan Leong, Paulo Saraiva, Ivan
  Nazarov, Aditya Rastogi, Moiz Hassan, Dexian Tang and 'Africa Peri'a~nez},
    journal={arXiv preprint arXiv:2408.08024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08024},
    primaryClass={cs.LG cs.AI stat.ML}
}