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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.
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.
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.
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