基于强化学习的医药电商自适应用户旅程：来自SwipeRx的洞察
Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx
AI总结：
本研究提出一种强化学习平台，以东南亚药剂师应用SwipeRx为案例，基于购买历史和应用内参与度实时信息定制商品推荐，可扩展优化医药电商用户旅程，提升医疗相关成效。
摘要：
本文提出了一种强化学习（RL）平台，通过个性化优化医疗数字工具中的端到端用户旅程。我们以东南亚最受欢迎的药剂师一体化应用SwipeRx为案例开展研究，展示了该平台如何用于个性化并适配用户体验。我们的RL框架通过一系列实验进行测试，实验基于各药房的购买历史和应用内参与度的实时信息，为其定制商品推荐，结果显示购物篮规模显著提升。通过将自适应干预措施整合到现有移动医疗解决方案中并丰富用户旅程，该平台为改善医药供应链管理、卫生工作者能力建设以及临床决策与患者护理提供了可扩展的解决方案，最终助力实现更好的医疗效果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
