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

Article Article scientifique Article The development of a chatbot technology to disseminate post-COVID-19 information : descriptive implementation study eng 2023-06-05 2023-06-05 author Nehme Mayssam 0000-0001-9189-6495 Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland. 840942 author Schneider Franck 0009-0008-5884-1343 Department of Communication, Geneva University Hospitals, Geneva, Switzerland. 909730 author Perrin Anne 0000-0002-2023-3157 Division of General Pediatrics, Geneva University Hospitals, Geneva, Switzerland. 633095 author Yu Wing Sum 0009-0005-4465-2590 Department of Communication, Geneva University Hospitals, Geneva, Switzerland. 781398 author Schmitt Simon 0009-0001-9473-3138 Department of Communication, Geneva University Hospitals, Geneva, Switzerland. author Violot Guillemette 0000-0001-9017-1193 Department of Communication, Geneva University Hospitals, Geneva, Switzerland. author Ducrot Aurelie 0009-0004-0494-6677 Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland. 974306 Département de médecine (DEMED) author Tissandier Frédérique 0009-0006-5427-1748 Department of Communication, Geneva University Hospitals, Geneva, Switzerland. author Posfay Barbe Klara 0000-0001-9464-5704 Division of General Pediatrics, Geneva University Hospitals, Geneva, Switzerland.,Faculty of Medicine, University of Geneva, Geneva, Switzerland. 39230 Département de Pédiatrie, Gynécologie et Obstétrique (PETRI) author Guessous Idris 0000-0002-0491-6089 Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland.,Faculty of Medicine, University of Geneva, Geneva, Switzerland. 760565 Médecine de premier recours hospitalo-universitaire (MPRHU) e43113 10.2196/43113 37195688 1438-8871 PMC10246662 https://www.jmir.org/2023/1/e43113 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 Dewey 613 Dewey 618 58 Département de pédiatrie, gynécologie et obstétrique 223 201 Institut de médecine de premier recours 22A9 false 515 Recherche en infectiologie pédiatrique et en pédiatrie générale 853 1313 Médecine de 1er recours 1020 JMIR. Journal of medical internet research 25 48b84577-7680-4093-9912-a62802915240 Article (Published version) Public Article-scientifique-from-unpaywall.pdf 346429 application/pdf CC BY-4.0 d9886af7520c8b9a5f21251aeb68a1b470ecd1cb50451f2d271deccd0d3f65e1 9683675c-9781-4b5a-b4aa-ac5d3b38e846 Supplemental data Public jmir_v25i1e43113_app1.docx 14875 application/vnd.openxmlformats-officedocument.wordprocessingml.document a7ab06b9df948688d1cc59b274b6023e2eb13ae471ea6b40509bcf848e94f096 openAccess Faculté de médecine / Section de médecine clinique / Département de pédiatrie, gynécologie et obstétrique Faculté de médecine / Section de médecine clinique / Département de santé et médecine communautaires / Institut de médecine de premier recours

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The development of a chatbot technology to disseminate post-COVID-19 information : descriptive implementation study	JMIR. Journal of medical internet research			2023	85	275

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Ducrot, Aurelie