The Development of a Chatbot Technology to Disseminate Post-COVID-19 Information: Descriptive Implementation Study

Area of Science:

• Digital Health
• Artificial Intelligence in Healthcare
• Public Health

Background:

• Post-COVID-19, or long COVID, affects millions globally with symptoms like fatigue and neurocognitive issues.
• The lack of clear information on long COVID prevalence, pathophysiology, and management necessitates reliable resources.
• Misinformation online poses risks to patients and healthcare providers managing this condition.

Purpose of the Study:

• To describe the development and deployment of the RAFAEL platform and chatbot.
• To address the information and management needs of individuals with post-COVID-19.
• To provide a scalable solution for disseminating verified long COVID information.

Main Methods:

• The RAFAEL platform integrates online information, webinars, and a chatbot.
• Development involved concept creation, backend/frontend development, and beta testing with a focus on medical safety.
• Deployment included partnerships and communication strategies, with continuous monitoring by professionals.

Main Results:

• The RAFAEL chatbot has had over 30,000 interactions with a high matching and positive feedback rate.
• 5,807 unique users interacted with the chatbot, averaging 5.1 interactions each.
• Top user queries concerned post-COVID-19 symptoms (fatigue), consultations, and treatment.

Conclusions:

• The RAFAEL chatbot is a novel, scalable tool for disseminating verified post-COVID-19 information.
• It addresses patient concerns and aids professional learning in a resource-limited setting.
• Lessons learned can inform applications for managing other chronic conditions.