背景:后COVID疾病现在已经影响了数百万人,导致疲劳,神经认知症状和对日常生活的影响。围绕这种情况的知识的不确定性,包括其总体患病率,病理生理学,和管理,随着受影响的个人人数的增加,对信息和疾病管理产生了必要的需求。在大量在线错误信息和潜在误导患者和医疗保健专业人员的时代,这变得更加关键。
目标:RAFAEL平台是一个生态系统,旨在解决后COVID状况的信息和管理,整合在线信息,网络研讨会,和聊天机器人技术在时间有限和资源有限的环境中回答大量的个人。本文介绍了RAFAEL平台和聊天机器人的开发和部署,以解决儿童和成人的COVID后状况。
方法:RAFAEL研究在日内瓦进行,瑞士。RAFAEL平台和聊天机器人在线提供,所有的使用者都被认为是这项研究的参与者.开发阶段于2020年12月开始,包括开发概念,后端,和前端开发以及beta测试。RAFAEL聊天机器人背后的具体策略平衡了一种可访问的交互式方法与医疗安全,旨在在COVID后病情管理中传递正确和经过验证的信息。发展之后,在法语世界建立了伙伴关系和传播战略。社区主持人和医疗保健专业人员不断监控聊天机器人的使用和提供的答案,为用户创建安全的回退。
结果:迄今为止,RAFAEL聊天机器人有30488个交互,在提供反馈的251名用户中,匹配率n=6471(79.5%),正反馈率n=1795。总的来说,5807个独特用户与聊天机器人进行交互,平均每个用户进行5.1次交互,触发了8061个故事。聊天机器人和RAFAEL平台的使用还受到每月主题网络研讨会和宣传活动的推动。每个网络研讨会平均有250名参与者。用户查询包括有关COVID后症状的问题(n=5612;69.2%),其中疲劳是最主要的查询(n=1255;22.4%的症状相关故事)。其他查询包括有关咨询的问题(n=598;7.4%),治疗(n=527;6.5%),和一般信息(n=510;6.3%)。
结论:据我们所知,RAFAEL聊天机器人是为解决儿童和成人的COVID后状况而开发的第一个聊天机器人。它的创新在于使用可扩展的工具在时间和资源有限的环境中传播已验证的信息。此外,机器学习的使用可以帮助专业人员获得有关新情况的知识,同时解决患者的担忧。从RAFAEL聊天机器人中学到的经验教训将进一步鼓励参与式学习方法,并有可能应用于其他慢性病。
背景:
Post-COVID-19, or long COVID, has now affected millions of individuals, resulting in fatigue, neurocognitive symptoms, and an impact on daily life. The uncertainty of knowledge around this condition, including its overall prevalence, pathophysiology, and management, along with the growing numbers of affected individuals, has created an essential need for information and disease management. This has become even more critical in a time of abundant online misinformation and potential misleading of patients and health care professionals.
The RAFAEL platform is an ecosystem created to address the information about and management of post-COVID-19, integrating online information, webinars, and chatbot technology to answer a large number of individuals in a time- and resource-limited setting. This paper describes the development and deployment of the RAFAEL platform and chatbot in addressing post-COVID-19 in children and adults.
The RAFAEL
study took place in Geneva, Switzerland. The RAFAEL platform and chatbot were made available online, and all users were considered participants of this
study. The development phase started in December 2020 and included developing the concept, the backend, and the frontend, as well as beta testing. The specific strategy behind the RAFAEL chatbot balanced an accessible interactive approach with medical safety, aiming to relay correct and verified information for the management of post-COVID-19. Development was followed by deployment with the establishment of partnerships and communication strategies in the French-speaking world. The use of the chatbot and the answers provided were continuously monitored by community moderators and health care professionals, creating a safe fallback for users.
To date, the RAFAEL chatbot has had 30,488 interactions, with an 79.6% (6417/8061) matching rate and a 73.2% (n=1795) positive feedback rate out of the 2451 users who provided feedback. Overall, 5807 unique users interacted with the chatbot, with 5.1 interactions per user, on average, and 8061 stories triggered. The use of the RAFAEL chatbot and platform was additionally driven by the monthly thematic webinars as well as communication campaigns, with an average of 250 participants at each webinar. User queries included questions about post-COVID-19 symptoms (n=5612, 69.2%), of which fatigue was the most predominant query (n=1255, 22.4%) in symptoms-related stories. Additional queries included questions about consultations (n=598, 7.4%), treatment (n=527, 6.5%), and general information (n=510, 6.3%).
The RAFAEL chatbot is, to the best of our knowledge, the first chatbot developed to address post-COVID-19 in children and adults. Its innovation lies in the use of a scalable tool to disseminate verified information in a time- and resource-limited environment. Additionally, the use of machine learning could help professionals gain knowledge about a new condition, while concomitantly addressing patients\' concerns. Lessons learned from the RAFAEL chatbot will further encourage a participative approach to learning and could potentially be applied to other chronic conditions.