用智能代理生成优化患者消息，点击率提升12.2%。
PAME-AI: Patient Messaging Creation and Optimization using Agentic AI
- 构建多智能体系统，从数据逐步提炼成可执行的消息策略
- 实测中消息点击率达68.76%，较基线提升12.2%
- 适合需要大规模个性化医疗沟通的机构使用
患者沟通是医疗传播的关键环节，有助于提高用药依从性和健康行为。然而，传统移动端消息设计因无法探索高维设计空间而存在显著局限。本文提出PAME-AI，一种基于数据-信息-知识-智慧（DIKW）层级结构的患者消息生成与优化方法。该系统由一系列专用计算智能体组成，可将原始实验数据逐步转化为可执行的消息设计策略。通过两阶段实验验证，第一阶段包含444,691次患者互动，第二阶段为74,908次。最优生成消息实现68.76%的参与率，相比61.27%的基线提升12.2%的点击率。该智能体架构支持并行处理、假设验证与持续学习，特别适用于大规模医疗通信优化。
原文摘要 · Abstract (English)
Messaging patients is a critical part of healthcare communication, helping to improve things like medication adherence and healthy behaviors. However, traditional mobile message design has significant limitations due to its inability to explore the high-dimensional design space. We develop PAME-AI, a novel approach for Patient Messaging Creation and Optimization using Agentic AI. Built on the Data-Information-Knowledge-Wisdom (DIKW) hierarchy, PAME-AI offers a structured framework to move from raw data to actionable insights for high-performance messaging design. PAME-AI is composed of a system of specialized computational agents that progressively transform raw experimental data into actionable message design strategies. We demonstrate our approach's effectiveness through a two-stage experiment, comprising of 444,691 patient encounters in Stage 1 and 74,908 in Stage 2. The best-performing generated message achieved 68.76% engagement compared to the 61.27% baseline, representing a 12.2% relative improvement in click-through rates. This agentic architecture enables parallel processing, hypothesis validation, and continuous learning, making it particularly suitable for large-scale healthcare communication optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。