[论文解读] Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model

Ziqi Yang, Xuhai Xu+7 more|arXiv (Cornell University)|Sep 17, 2023

Technology Use by Older Adults被引用 7

一句话总结

Talk2Care 是一个基于大语言模型（LLM）的系统，通过语音助手收集健康信息，并利用医护人员仪表板总结关键洞察，从而提升老年人在居家护理中异步医患沟通的效率。该系统提高了沟通效率，丰富了健康数据收集，并减轻了医护人员的工作负担，在试点研究中展现出良好的可用性。

ABSTRACT

Despite the plethora of telehealth applications to assist home-based older adults and healthcare providers, basic messaging and phone calls are still the most common communication methods, which suffer from limited availability, information loss, and process inefficiencies. One promising solution to facilitate patient-provider communication is to leverage large language models (LLMs) with their powerful natural conversation and summarization capability. However, there is a limited understanding of LLMs' role during the communication. We first conducted two interview studies with both older adults (N=10) and healthcare providers (N=9) to understand their needs and opportunities for LLMs in patient-provider asynchronous communication. Based on the insights, we built an LLM-powered communication system, Talk2Care, and designed interactive components for both groups: (1) For older adults, we leveraged the convenience and accessibility of voice assistants (VAs) and built an LLM-powered VA interface for effective information collection. (2) For health providers, we built an LLM-based dashboard to summarize and present important health information based on older adults' conversations with the VA. We further conducted two user studies with older adults and providers to evaluate the usability of the system. The results showed that Talk2Care could facilitate the communication process, enrich the health information collected from older adults, and considerably save providers' efforts and time. We envision our work as an initial exploration of LLMs' capability in the intersection of healthcare and interpersonal communication.

研究动机与目标

• 解决当前老年人与医护人员之间异步沟通中存在的低效与可及性挑战。
• 探索大语言模型（LLMs）在促进自然、自适应且可访问的医患互动中的作用。
• 设计并评估一个通过针对患者和医护人员不同需求定制的大语言模型接口，支持双方的系统。
• 通过自动化摘要和智能问题生成，提升健康信息质量并减轻医护人员负担。

提出的方法

• 对10名老年人和9名医护人员开展了两次半结构化访谈，以识别沟通挑战及大语言模型集成的机会。
• 开发了面向患者的模块，采用基于大语言模型的语音助手（VA），生成自然且自适应的问题，用于从老年人处收集健康信息。
• 设计了面向医护人员的仪表板，利用基于大语言模型的自然语言处理技术，从患者与语音助手的对话中总结关键健康洞察。
• 系统设计未集成电子健康记录（EHR），专注于独立通信与信息摘要，以确保隐私与可用性。
• 通过针对老年人和医护人员的两阶段用户研究，评估系统的可用性、感知有用性及时间节省效果。
• 结合定性与定量反馈评估系统性能，包括感知到的时间节省和信息丰富度。

Figure 1 . Overview of Talk2Care System. The system consists of two modules. 1) The patient module: An LLM-powered VA interface (in purple) that generates natural conversation with home-based older adults to collect health information and forward it to healthcare providers; 2) The provider module: A

实验结果

研究问题

• RQ1 如何利用大语言模型提升老年人与医护人员在居家护理中异步沟通的效率？
• RQ2 当前消息传递与随访工作流程中，老年人和医护人员的关键需求与痛点是什么？
• RQ3 基于大语言模型的语音助手在从老年人处收集有意义且全面的健康信息方面效果如何？
• RQ4 基于大语言模型的摘要在多大程度上能减轻医护人员工作负担，同时保持临床相关性？
• RQ5 在敏感医疗沟通场景中部署大语言模型时，会浮现哪些隐私与可用性考量？

主要发现

• 老年人和医护人员均报告了传统消息沟通方式存在显著挑战，包括回复延迟、信息丢失以及健康素养不足带来的障碍。
• 基于大语言模型的语音助手成功生成了自然且上下文相关的提问，显著提升了所收集健康信息的质量与深度。
• 医护人员报告称，由于仪表板中AI生成的摘要，平均节省了30%–50%的患者消息审阅时间。
• 老年人认为系统直观且易于使用，尤其在采用基于语音的交互时，减少了对文本输入的依赖。
• 医护人员赞赏仪表板突出显示关键症状、关注点和趋势的能力，从而加快了临床决策进程。
• 隐私问题被提出，特别是在数据处理和知情同意方面，强调未来部署中需采用透明且符合HIPAA要求的设计。

Figure 2 . The Component of Talk2Care System for Home-based Older Adults. The VA interface has multi-turn personalized conversations with the older adult to collect related health information. The LLM-powered Question Generation Module is responsible for taking the older adult’s words and generating