Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

个性化 梅德林 数据提取 主题分析 科克伦图书馆 医疗保健 灰色文学 斯科普斯 万维网 随机对照试验 计算机科学 医学 情报检索 系统回顾 定性研究 经济 社会学 外科 法学 经济增长 社会科学 政治学
作者
Lorainne Tudor Car,Dhakshenya Ardhithy Dhinagaran,Bhone Myint Kyaw,Tobias Kowatsch,Shafiq Joty,Yin Leng Theng,Rifat Atun
出处
期刊:Journal of Medical Internet Research [JMIR Publications]
卷期号:22 (8): e17158-e17158 被引量:474
标识
DOI:10.2196/17158
摘要

Background Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care. Objective This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application. Methods We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis. Results The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education. Conclusions The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.
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