背景:人工智能和聊天机器人技术在医疗保健中的集成由于其改善患者护理和简化历史记录的潜力而引起了极大的关注。作为人工智能驱动的对话代理,聊天机器人提供了彻底改变历史的机会,需要全面检查它们对医疗实践的影响。
目的:本系统综述旨在评估角色,有效性,可用性,以及患者在病史记录中接受聊天机器人。它还研究了融入临床实践的潜在挑战和未来机遇。
方法:系统搜索包括PubMed,Embase,MEDLINE(通过Ovid),中部,Scopus,和开放科学,并涵盖到2024年7月的研究。审查的研究的纳入和排除标准是基于PICOS(参与者,干预措施,比较器,结果,和研究设计)框架。人口包括使用医疗保健聊天机器人进行病史记录的个人。干预措施的重点是旨在促进病史记录的聊天机器人。感兴趣的结果是可行性,接受,以及基于聊天机器人的病史采集的可用性。未报告这些结果的研究被排除。除会议论文外,所有研究设计均符合纳入条件。只考虑了英语学习。对研究持续时间没有具体限制。主要搜索词包括“chatbot*”,“”对话代理*,\"\"虚拟助手,\"\"人工智能聊天机器人,\"\"病史,“和”历史记录。“观察性研究的质量使用STROBE(加强流行病学观察性研究的报告)标准进行分类(例如,样本量,设计,数据收集,和后续行动)。RoB2(风险偏倚)工具评估了随机对照试验(RCTs)中偏倚的领域和水平。
结果:该综述包括15项观察性研究和3项RCT,以及来自不同医学领域和人群的综合证据。聊天机器人通过有针对性的查询和数据检索系统地收集信息,提高患者参与度和满意度。结果表明,聊天机器人具有很大的历史记录潜力,并且可以通过24/7自动数据收集来提高医疗保健系统的效率和可访问性。偏见评估显示,在15项观察性研究中,5项(33%)研究质量高,5项(33%)研究质量中等,5项(33%)研究质量低。在RCT中,2具有较低的偏见风险,1有高风险。
结论:本系统综述为使用聊天机器人获取病史的潜在益处和挑战提供了重要见解。纳入的研究表明,聊天机器人可以增加患者的参与度,简化数据收集,改善医疗保健决策。为了有效地融入临床实践,设计用户友好的界面至关重要,确保强大的数据安全,并保持有同情心的患者-医生互动。未来的研究应该集中在改进聊天机器人算法上,提高他们的情绪智力,并将其应用扩展到不同的医疗保健环境,以充分发挥其在现代医学中的潜力。
背景:PROSPEROCRD42023410312;www.crd.约克。AC.英国/普洛佩罗。
BACKGROUND: The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence-driven conversational agents,
chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice.
OBJECTIVE: This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of
chatbots in medical history-taking. It also examines potential challenges and future opportunities for integration into clinical practice.
METHODS: A systematic search included PubMed, Embase, MEDLINE (via Ovid), CENTRAL, Scopus, and Open Science and covered studies through July 2024. The inclusion and exclusion criteria for the studies reviewed were based on the PICOS (participants, interventions, comparators, outcomes, and study design) framework. The population included individuals using health care
chatbots for medical history-taking. Interventions focused on
chatbots designed to facilitate medical history-taking. The outcomes of interest were the feasibility, acceptance, and usability of chatbot-based medical history-taking. Studies not reporting on these outcomes were excluded. All study designs except conference papers were eligible for inclusion. Only English-language studies were considered. There were no specific restrictions on study duration. Key search terms included \"chatbot*,\" \"conversational agent*,\" \"virtual assistant,\" \"artificial intelligence chatbot,\" \"medical history,\" and \"history-taking.\" The quality of observational studies was classified using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria (eg, sample size, design, data collection, and follow-up). The RoB 2 (Risk of Bias) tool assessed areas and the levels of bias in randomized controlled trials (RCTs).
RESULTS: The review included 15 observational studies and 3 RCTs and synthesized evidence from different medical fields and populations. Chatbots systematically collect information through targeted queries and data retrieval, improving patient engagement and satisfaction. The results show that chatbots have great potential for history-taking and that the efficiency and accessibility of the health care system can be improved by 24/7 automated data collection. Bias assessments revealed that of the 15 observational studies, 5 (33%) studies were of high quality, 5 (33%) studies were of moderate quality, and 5 (33%) studies were of low quality. Of the RCTs, 2 had a low risk of bias, while 1 had a high risk.
CONCLUSIONS: This systematic review provides critical insights into the potential benefits and challenges of using chatbots for medical history-taking. The included studies showed that
chatbots can increase patient engagement, streamline data collection, and improve health care decision-making. For effective integration into clinical practice, it is crucial to design user-friendly interfaces, ensure robust data security, and maintain empathetic patient-physician interactions. Future research should focus on refining chatbot algorithms, improving their emotional intelligence, and extending their application to different health care settings to realize their full potential in modern medicine.
BACKGROUND: PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero.