ArticleJournal of the American Medical Informatics Association : JAMIA2021
Leveraging conversational technology to answer common COVID-19 questions.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07073898 (Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial), which is not on this map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial
Who cites it
11 citing papers in PubMed.
- Use of Artificial Intelligence in Public Health Education for Pandemic Preparedness and Response.Annals of global health · 2026Review
- Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities.Life (Basel, Switzerland) · 2024Review
- Article
- Evaluation framework for conversational agents with artificial intelligence in health interventions: a systematic scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Development of a novel drug information provision system for Kampo medicine using natural language processing technology.BMC medical informatics and decision making · 2023Article
- Design Validation of a Relational Agent by COVID-19 Patients: Mixed Methods Study.JMIR human factors · 2023Article
- User Experience of COVID-19 Chatbots: Scoping Review.Journal of medical Internet research · 2022Article
- A Mobile Application-Based Relational Agent as a Health Professional for COVID-19 Patients: Design, Approach, and Implications.International journal of environmental research and public health · 2022Article
- Artificial Intelligence Applications in Health Care Practice: Scoping Review.Journal of medical Internet research · 2022Article
- The Development and Use of Chatbots in Public Health: Scoping Review.JMIR human factors · 2022Article
- Patients and consumers (and the data they generate): an underutilized resource.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapidly evolving science about the Coronavirus Disease 2019 (COVID-19) pandemic created unprecedented health information needs and dramatic changes in policies globally. We describe a platform, Watson Assistant (WA), which has been used to develop conversational agents to deliver COVID-19 related information. We characterized the diverse use cases and implementations during the early pandemic and measured adoption through a number of users, messages sent, and conversational turns (ie, pairs of interactions between users and agents). Thirty-seven institutions in 9 countries deployed COVID-19 conversational agents with WA between March 30 and August 10, 2020, including 24 governmental agencies, 7 employers, 5 provider organizations, and 1 health plan. Over 6.8 million messages were delivered through the platform. The mean number of conversational turns per session ranged between 1.9 and 3.5. Our experience demonstrates that conversational technologies can be rapidly deployed for pandemic response and are adopted globally by a wide range of users.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.