Your browser doesn't support javascript.
Leveraging conversational technology to answer common COVID-19 questions.
McKillop, Mollie; South, Brett R; Preininger, Anita; Mason, Mitch; Jackson, Gretchen Purcell.
  • McKillop M; IBM Watson Health, Cambridge, Massachusetts, USA.
  • South BR; IBM Watson Health, Cambridge, Massachusetts, USA.
  • Preininger A; IBM Watson Health, Cambridge, Massachusetts, USA.
  • Mason M; IBM Watson Health, Cambridge, Massachusetts, USA.
  • Jackson GP; IBM Watson Health, Cambridge, Massachusetts, USA.
J Am Med Inform Assoc ; 28(4): 850-855, 2021 03 18.
Article in English | MEDLINE | ID: covidwho-1066365
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Health Education / Communication / COVID-19 Limits: Humans Language: English Journal: J Am Med Inform Assoc Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Jamia

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Health Education / Communication / COVID-19 Limits: Humans Language: English Journal: J Am Med Inform Assoc Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Jamia