Scientific article
Open access

The development of a chatbot technology to disseminate post-COVID-19 information : descriptive implementation study

Published inJMIR. Journal of medical internet research, vol. 25, e43113
Publication date2023-06-05
First online date2023-06-05

Background: 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.

Objective: 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.

Methods: 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.

Results: 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%).

Conclusions: 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.

  • COVID-19
  • PASC
  • Caregiver
  • Chatbot
  • Children
  • Communication
  • Conversational agent
  • Digital surveillance
  • Disease management
  • Dissemination
  • Information
  • Long COVID
  • Medical technology
  • Online platform
  • Pediatric
  • Postacute sequelae of SARS-CoV-2
  • Post–COVID-19
  • Adult
  • Child
  • Humans
  • Post-Acute COVID-19 Syndrome
  • Ecosystem
  • Health Personnel / psychology
Citation (ISO format)
NEHME, Mayssam et al. The development of a chatbot technology to disseminate post-COVID-19 information : descriptive implementation study. In: JMIR. Journal of medical internet research, 2023, vol. 25, p. e43113. doi: 10.2196/43113
Main files (1)
Article (Published version)
Secondary files (1)
ISSN of the journal1438-8871

Technical informations

Creation01/17/2024 10:14:25 AM
First validation05/06/2024 3:29:52 PM
Update time05/06/2024 3:29:52 PM
Status update05/06/2024 3:29:52 PM
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