Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative.

health services research information technology leadership management nurse roles policy politics technology workforce issues

Journal

Journal of advanced nursing
ISSN: 1365-2648
Titre abrégé: J Adv Nurs
Pays: England
ID NLM: 7609811

Informations de publication

Date de publication:
Sep 2021
Historique:
received: 04 02 2021
accepted: 21 03 2021
pubmed: 19 5 2021
medline: 18 8 2021
entrez: 18 5 2021
Statut: ppublish

Résumé

To develop a consensus paper on the central points of an international invitational think-tank on nursing and artificial intelligence (AI). We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3-day invitational think tank in autumn 2019. Activities included a pre-event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities. Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice. There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems. We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.

Identifiants

pubmed: 34003504
doi: 10.1111/jan.14855
pmc: PMC7612744
mid: EMS144965
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3707-3717

Subventions

Organisme : Wellcome Trust
ID : 213660/Z/18/Z
Pays : United Kingdom
Organisme : Fondation Brocher
Organisme : Wellcome Trust
ID : 213660
Pays : United Kingdom
Organisme : Leverhulme Centre for the Future of Intelligence
Organisme : Natural Sciences and Engineering Research Council (NSERC)
ID : 2020-05246
Organisme : Wellcome Trust
Pays : United Kingdom

Informations de copyright

© 2021 The Authors. Journal of Advanced Nursing published by John Wiley & Sons Ltd.

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Auteurs

Charlene Esteban Ronquillo (CE)

Daphne Cockwell School of Nursing, Faculty of Community Services, Ryerson University, Toronto, ON, Canada.
School of Nursing, Faculty of Health and Social Development, University of British Columbia Okanagan, Kelowna, BC, Canada.
International Medical Informatics Association, Student and Emerging Professionals Special Interest Group.

Laura-Maria Peltonen (LM)

International Medical Informatics Association, Student and Emerging Professionals Special Interest Group.
Department of Nursing Science, University of Turku, Turku, Finland.

Lisiane Pruinelli (L)

School of Nursing, University of Minnesota, Minneapolis, MN, USA.

Charlene H Chu (CH)

Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.

Suzanne Bakken (S)

School of Nursing, Department of Biomedical Informatics, Data Science Institute, Columbia University, New York, NY, USA.
Precision in Symptom Self-Management (PriSSM) Center, Reducing Health Disparities Through Informatics Training Program (RHeaDI), Columbia University, New York, NY, USA.

Ana Beduschi (A)

Law School, University of Exeter, Exeter, UK.

Kenrick Cato (K)

School of Nursing, Department of Biomedical Informatics, Data Science Institute, Columbia University, New York, NY, USA.

Nicholas Hardiker (N)

School of Human & Health Sciences, University of Huddersfield, Huddersfield, UK.

Alain Junger (A)

Nursing Direction, Nursing Information System Unit, Centre Hospitalier Universitaire Vaudois (CHUV) Lausanne, Lausanne, Switzerland.

Martin Michalowski (M)

School of Nursing, University of Minnesota, Minneapolis, MN, USA.

Rune Nyrup (R)

Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.

Samira Rahimi (S)

Department of Family Medicine, McGill University, Lady Davis Institute for Medical Research of Jewish General Hospital, Mila Quebec Artificial Intelligence Institute, Montreal, QC, Canada.

Donald Nigel Reed (DN)

College of Medicine and Health, University of Exeter, Exeter, UK.

Tapio Salakoski (T)

Department of Mathematics and Statistics, University of Turku, Turku, Finland.

Sanna Salanterä (S)

Department of Nursing Science, University of Turku and Turku University Hospital, Turku, Finland.

Nancy Walton (N)

Daphne Cockwell School of Nursing, Faculty of Community Services, Ryerson University, Toronto, ON, Canada.
Research Ethics Board, Women's College Hospital, Toronto, ON, Canada.
Health Canada and Public Health Agency of Canada's Research Ethics Board, Toronto, ON, Canada.

Patrick Weber (P)

NICE Computing SA, Lausanne, Switzerland.
European Federation for Medical Informatics (EFMI).

Thomas Wiegand (T)

ITU/WHO Focus Group on Artificial Intelligence for Health (FG-AI4H).
Fraunhofer Heinrich Hertz Institute, Berlin, Germany.
Berlin Institute of Technology, Berlin, Germany.

Maxim Topaz (M)

International Medical Informatics Association, Student and Emerging Professionals Special Interest Group.
School of Nursing, Department of Biomedical Informatics, Data Science Institute, Columbia University, New York, NY, USA.

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