Hindelang Michael, Sitaru Sebastian, Zink Alexander
Department of Dermatology and Allergy, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Pettenkofer School of Public Health, Munich, Germany.
JMIR Med Inform. 2024 Aug 29;12:e56628. doi: 10.2196/56628.
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.
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.
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).
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.
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.
PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero.
人工智能和聊天机器人技术在医疗保健领域的整合因其改善患者护理和简化病史采集的潜力而备受关注。作为人工智能驱动的对话代理，聊天机器人为彻底改变病史采集提供了机会，因此有必要全面审视它们对医疗实践的影响。
本系统评价旨在评估聊天机器人在病史采集中的作用、有效性、可用性和患者接受度。它还探讨了整合到临床实践中的潜在挑战和未来机遇。
系统检索包括PubMed、Embase、MEDLINE（通过Ovid）、CENTRAL、Scopus和开放科学，并涵盖截至2024年7月的研究。所审查研究的纳入和排除标准基于PICOS（参与者、干预措施、对照、结局和研究设计）框架。研究人群包括使用医疗保健聊天机器人进行病史采集的个体。干预措施侧重于旨在促进病史采集的聊天机器人。感兴趣的结局是基于聊天机器人的病史采集的可行性、接受度和可用性。未报告这些结局的研究被排除。除会议论文外，所有研究设计均符合纳入标准。仅考虑英文研究。对研究持续时间没有具体限制。关键检索词包括“聊天机器人*”、“对话代理*”、“虚拟助手”、“人工智能聊天机器人”、“病史”和“病史采集”。观察性研究的质量使用STROBE（加强流行病学观察性研究报告）标准进行分类（例如样本量、设计、数据收集和随访）。RoB 2（偏倚风险）工具评估随机对照试验（RCT）中的偏倚领域和程度。
该评价纳入了15项观察性研究和3项RCT，并综合了来自不同医学领域和人群的证据。聊天机器人通过有针对性的询问和数据检索系统地收集信息，提高了患者的参与度和满意度。结果表明，聊天机器人在病史采集中具有巨大潜力，并且通过全天候自动数据收集可以提高医疗保健系统的效率和可及性。偏倚评估显示，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项偏倚风险低，1项偏倚风险高。
本系统评价为使用聊天机器人进行病史采集的潜在益处和挑战提供了重要见解。纳入的研究表明，聊天机器人可以提高患者参与度、简化数据收集并改善医疗保健决策。为了有效整合到临床实践中，设计用户友好的界面、确保强大的数据安全性以及保持患者与医生之间的共情互动至关重要。未来的研究应专注于完善聊天机器人算法、提高其情商，并将其应用扩展到不同的医疗保健环境，以实现其在现代医学中的全部潜力。
PROSPERO CRD42023410312；www.crd.york.ac.uk/prospero。
