Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
J Biomed Inform. 2020 Feb;102:103305. doi: 10.1016/j.jbi.2019.103305. Epub 2019 Oct 14.
Chatbots are able to provide support to patients suffering from very different conditions. Patients with chronic diseases or comorbidities could benefit the most from chatbots which can keep track of their condition, provide specific information, encourage adherence to medication, etc. To perform these functions, chatbots need a suitable underlying software architecture. In this paper, we introduce a chatbot architecture for chronic patient support grounded on three pillars: scalability by means of microservices, standard data sharing models through HL7 FHIR and standard conversation modeling using AIML. We also propose an innovative automation mechanism to convert FHIR resources into AIML files, thus facilitating the interaction and data gathering of medical and personal information that ends up in patient health records. To align the way people interact with each other using messaging platforms with the chatbot architecture, we propose these very same channels for the chatbot-patient interaction, paying special attention to security and privacy issues. Finally, we present a monitored-data study performed in different chronic diseases, and we present a prototype implementation tailored for one specific chronic disease, psoriasis, showing how this new architecture allows the change, the addition or the improvement of different parts of the chatbot in a dynamic and flexible way, providing a substantial improvement in the development of chatbots used as virtual assistants for chronic patients.
聊天机器人能够为患有各种不同疾病的患者提供支持。患有慢性病或合并症的患者可以从聊天机器人中受益最多，因为这些机器人可以跟踪他们的病情，提供特定信息，鼓励他们按时服药等。为了执行这些功能，聊天机器人需要一个合适的基础软件架构。在本文中，我们介绍了一种基于三个支柱的用于慢性患者支持的聊天机器人架构：通过微服务实现可扩展性，通过 HL7 FHIR 实现标准数据共享模型，以及通过 AIML 实现标准对话建模。我们还提出了一种创新的自动化机制，将 FHIR 资源转换为 AIML 文件，从而促进了医疗和个人信息的交互和数据收集，最终进入患者的健康记录。为了使人们使用消息传递平台进行交互的方式与聊天机器人架构相匹配，我们为聊天机器人-患者交互提出了相同的渠道，特别关注安全性和隐私问题。最后，我们展示了在不同慢性病中进行的监测数据研究，并展示了针对一种特定慢性病（银屑病）的原型实现，展示了这种新架构如何以动态和灵活的方式改变、添加或改进聊天机器人的不同部分，为作为慢性病患者虚拟助手的聊天机器人的开发提供了实质性的改进。
