Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Sep 30, 2022
Date Accepted: Apr 13, 2023
Date Submitted to PubMed: May 17, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
RAFAEL: An interactive digital platform utilizing Chatbot technology for information dissemination and exchange on post-COVID condition in children and adults
ABSTRACT
Background:
Post-COVID condition 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 have created an essential need in information and disease management. This has become even more critical in a time of abundant online misinformation and potential misleading of patients and healthcare professionals.
Objective:
The RAFAEL platform is an ecosystem created to address the information and management of post-COVID condition, integrating online information, webinars and Chatbot technology to answer to a large number of individuals in a time-limited and resources-limited setting. This paper describes the development and deployment of the RAFAEL platform and Chatbot in addressing post-COVID condition in children and adults.
Methods:
The RAFAEL study takes place in Geneva, Switzerland, led by primary care, pediatric and communication experts in collaboration with patients. The RAFAEL platform and Chatbot are available online, and all users are considered participants to this study. The development phase started in December 2020 and consisted of developing the concept, backend and frontend developments as well as beta testing. This was followed by the deployment, communication and partnership phases, promoting the use of the platform and Chatbot in the French speaking world. The specific strategy behind the RAFAEL Chatbot balances an accessible interactive approach with medical safety, aiming to relay correct and verified information in the management of post-COVID condition. The use of the Chatbot and the answers provided are monitored by community moderators and healthcare professionals, creating a safe fallback for users.
Results:
To date, the Chatbot has had 23’438 interactions, with 71.4% matching rate and 72.7% positive feedback rate. Overall, 4’549 unique users interacted with the Chatbot with 5.1 interactions on average per user, and 6’836 stories triggered. Use of the Chatbot and RAFAEL platform was additionally driven by the monthly thematic webinars as well as communication campaigns, with an average of 250 participants at each webinar between January and June 2022. User queries included questions about post-COVID symptoms (66.5%), of which fatigue was the most predominant query (22.9% of symptoms-related stories). Additional queries included questions about consultations (6.5%), treatment (6.2%), and general information (6.3%).
Conclusions:
The RAFAEL Chatbot is to our knowledge the first Chatbot developed to address post-COVID condition in children and adults. Its innovation lies in the use of a scalable tool to disseminate verified information in a time- and resources-limited environment. Additionally, the use of machine-learning helps professionals gain knowledge about a new condition while 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.
Citation
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Copyright
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