Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore.
Future Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, Singapore, Singapore.
J Med Internet Res. 2023 Nov 1;25:e50767. doi: 10.2196/50767.
Conversational agents (CAs), or chatbots, are computer programs that simulate conversations with humans. The use of CAs in health care settings is recent and rapidly increasing, which often translates to poor reporting of the CA development and evaluation processes and unreliable research findings. We developed and published a conceptual framework, designing, developing, evaluating, and implementing a smartphone-delivered, rule-based conversational agent (DISCOVER), consisting of 3 iterative stages of CA design, development, and evaluation and implementation, complemented by 2 cross-cutting themes (user-centered design and data privacy and security).
This study aims to perform in-depth, semistructured interviews with multidisciplinary experts in health care CAs to share their views on the definition and classification of health care CAs and evaluate and validate the DISCOVER conceptual framework.
We conducted one-on-one semistructured interviews via Zoom (Zoom Video Communications) with 12 multidisciplinary CA experts using an interview guide based on our framework. The interviews were audio recorded, transcribed by the research team, and analyzed using thematic analysis.
Following participants' input, we defined CAs as digital interfaces that use natural language to engage in a synchronous dialogue using ≥1 communication modality, such as text, voice, images, or video. CAs were classified by 13 categories: response generation method, input and output modalities, CA purpose, deployment platform, CA development modality, appearance, length of interaction, type of CA-user interaction, dialogue initiation, communication style, CA personality, human support, and type of health care intervention. Experts considered that the conceptual framework could be adapted for artificial intelligence-based CAs. However, despite recent advances in artificial intelligence, including large language models, the technology is not able to ensure safety and reliability in health care settings. Finally, aligned with participants' feedback, we present an updated iteration of the conceptual framework for health care conversational agents (CHAT) with key considerations for CA design, development, and evaluation and implementation, complemented by 3 cross-cutting themes: ethics, user involvement, and data privacy and security.
We present an expanded, validated CHAT and aim at guiding researchers from a variety of backgrounds and with different levels of expertise in the design, development, and evaluation and implementation of rule-based CAs in health care settings.
对话代理（CA）或聊天机器人是模拟与人类对话的计算机程序。CA 在医疗保健环境中的使用是最近才开始的，而且发展迅速，这往往导致 CA 开发和评估过程的报告不佳，以及研究结果不可靠。我们开发并发布了一个概念框架，用于设计、开发、评估和实施基于规则的智能手机对话代理（DISCOVER），该代理由 CA 设计、开发和评估实施的 3 个迭代阶段组成，并辅以 2 个交叉主题（以用户为中心的设计和数据隐私与安全）。
本研究旨在与医疗保健 CA 领域的多学科专家进行深入的半结构化访谈，分享他们对医疗保健 CA 的定义和分类的看法，并评估和验证 DISCOVER 概念框架。
我们使用基于框架的访谈指南，通过 Zoom（Zoom Video Communications）与 12 名多学科 CA 专家进行一对一的半结构化访谈。访谈由研究团队进行录音、转录，并使用主题分析进行分析。
根据参与者的意见，我们将 CA 定义为使用自然语言通过 ≥1 种通信方式（如文本、语音、图像或视频）进行同步对话的数字接口。CA 按 13 个类别分类：响应生成方法、输入和输出方式、CA 目的、部署平台、CA 开发方式、外观、交互长度、CA-用户交互类型、对话启动、沟通风格、CA 个性、人工支持和医疗干预类型。专家们认为，该概念框架可以适应基于人工智能的 CA。然而，尽管人工智能技术取得了最新进展，包括大型语言模型，但该技术在医疗保健环境中仍无法确保安全性和可靠性。最后，根据参与者的反馈，我们提出了一个经过更新迭代的医疗保健对话代理（CHAT）概念框架，其中包含了 CA 设计、开发和评估实施的关键考虑因素，并辅以 3 个交叉主题：伦理、用户参与和数据隐私与安全。
我们提出了一个扩展的、经过验证的 CHAT，并旨在为来自不同背景和不同专业水平的研究人员提供指导，帮助他们在医疗保健环境中设计、开发和评估基于规则的 CA。
