Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China.
Center of medical informatics, Peking University, Beijing, China.
BMC Public Health. 2024 Aug 21;24(1):2266. doi: 10.1186/s12889-024-19667-4.
Chatbots can provide immediate assistance tailored to patients' needs, making them suitable for sustained accompanying interventions. Nevertheless, there is currently no evidence regarding their acceptability by hypertensive patients and the factors influencing the acceptability in the real-world. Existing evaluation scales often focus solely on the technology itself, overlooking the patients' perspective. Utilizing mixed methods can offer a more comprehensive exploration of influencing factors, laying the groundwork for the future integration of artificial intelligence in chronic disease management practices.
The mixed methods will provide a holistic view to understand the effectiveness and acceptability of the intervention. Participants will either receive the standard primary health care or obtain a chatbot speaker. The speaker can provide timely reminders, on-demand consultations, personalized data recording, knowledge broadcasts, as well as entertainment features such as telling jokes. The quantitative part will be conducted as a quasi-randomized controlled trial in community in Beijing. And the convergent design will be adopted. When patients use the speaker for 1 month, scales will be used to measure patients' intention to use the speaker. At the same time, semi-structured interviews will be conducted to explore patients' feelings and influencing factors of using speakers. Data on socio-demography, physical examination, blood pressure, acceptability and self-management behavior will be collected at baseline, as well as 1,3,6, and 12 months later. Furthermore, the cloud database will continuously collect patients' interactions with the speaker. The primary outcome is the efficacy of the chatbot on blood pressure control. The secondary outcome includes the acceptability of the chatbot speaker and the changes of self-management behavior.
Artificial intelligence-based chatbot speaker not only caters to patients' self-management needs at home but also effectively organizes intricate and detailed knowledge system for patients with hypertension through a knowledge graph. Patients can promptly access information that aligns with their specific requirements, promoting proactive self-management and playing a crucial role in disease management. This study will serve as a foundation for the application of artificial intelligence technology in chronic disease management, paving the way for further exploration on enhancing the communicative impact of artificial intelligence technology.
Biomedical Ethics Committee of Peking University: IRB00001052-21106, 2021/10/14; Clinical Trials: ChiCTR2100050578, 2021/08/29.
聊天机器人可以根据患者的需求提供即时帮助,使其成为持续伴随干预的理想选择。然而,目前尚无关于其在高血压患者中可接受性的证据,也没有关于现实世界中影响可接受性的因素的证据。现有的评估量表通常仅关注技术本身,而忽略了患者的观点。采用混合方法可以更全面地探讨影响因素,为未来人工智能在慢性病管理实践中的整合奠定基础。
混合方法将提供一个整体视角,以了解干预措施的有效性和可接受性。参与者将接受标准初级保健或获得聊天机器人扬声器。扬声器可以提供及时提醒、按需咨询、个性化数据记录、知识广播以及娱乐功能,如讲笑话。定量部分将在北京社区进行准随机对照试验,并采用收敛设计。当患者使用扬声器 1 个月时,将使用量表测量患者使用扬声器的意愿。同时,进行半结构化访谈,以探讨患者使用扬声器的感受和影响因素。基线时收集社会人口统计学、体检、血压、可接受性和自我管理行为的数据,以及 1、3、6 和 12 个月后。此外,云数据库将持续收集患者与扬声器的交互数据。主要结局是聊天机器人对血压控制的疗效。次要结局包括聊天机器人扬声器的可接受性和自我管理行为的变化。
基于人工智能的聊天机器人扬声器不仅满足了患者在家自我管理的需求,还通过知识图谱有效组织了复杂详细的高血压患者知识体系。患者可以及时获取符合其特定需求的信息,促进主动自我管理,在疾病管理中发挥关键作用。本研究将为人工智能技术在慢性病管理中的应用奠定基础,为进一步探索增强人工智能技术的交流效果铺平道路。
北京大学医学伦理委员会:IRB00001052-21106,2021/10/14;临床试验:ChiCTR2100050578,2021/08/29。