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

Charlene Esteban Ronquillo; Laura-Maria Peltonen; Lisiane Pruinelli; Charlene H Chu; Suzanne Bakken; Ana Beduschi; Kenrick Cato; Nicholas Hardiker; Alain Junger; Martin Michalowski; Rune Nyrup; Samira Rahimi; Donald Nigel Reed; Tapio Salakoski; Sanna Salanterä; Nancy Walton; Patrick Weber; Thomas Wiegand; Maxim Topaz

doi:10.1111/jan.14855

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

J Adv Nurs. 2021 Sep;77(9):3707-3717. doi: 10.1111/jan.14855. Epub 2021 May 18.

Authors

Charlene Esteban Ronquillo^{1

2

3}, Laura-Maria Peltonen^{3

4}, Lisiane Pruinelli⁵, Charlene H Chu⁶, Suzanne Bakken^{7

8}, Ana Beduschi⁹, Kenrick Cato⁷, Nicholas Hardiker¹⁰, Alain Junger¹¹, Martin Michalowski⁵, Rune Nyrup¹², Samira Rahimi¹³, Donald Nigel Reed¹⁴, Tapio Salakoski¹⁵, Sanna Salanterä¹⁶, Nancy Walton^{1

17

18}, Patrick Weber^{19

20}, Thomas Wiegand^{21

22

23}, Maxim Topaz^{3

7}

Affiliations

¹ Daphne Cockwell School of Nursing, Faculty of Community Services, Ryerson University, Toronto, ON, Canada.
² School of Nursing, Faculty of Health and Social Development, University of British Columbia Okanagan, Kelowna, BC, Canada.
³ International Medical Informatics Association, Student and Emerging Professionals Special Interest Group.
⁴ Department of Nursing Science, University of Turku, Turku, Finland.
⁵ School of Nursing, University of Minnesota, Minneapolis, MN, USA.
⁶ Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.
⁷ School of Nursing, Department of Biomedical Informatics, Data Science Institute, Columbia University, New York, NY, USA.
⁸ Precision in Symptom Self-Management (PriSSM) Center, Reducing Health Disparities Through Informatics Training Program (RHeaDI), Columbia University, New York, NY, USA.
⁹ Law School, University of Exeter, Exeter, UK.
¹⁰ School of Human & Health Sciences, University of Huddersfield, Huddersfield, UK.
¹¹ Nursing Direction, Nursing Information System Unit, Centre Hospitalier Universitaire Vaudois (CHUV) Lausanne, Lausanne, Switzerland.
¹² Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
¹³ Department of Family Medicine, McGill University, Lady Davis Institute for Medical Research of Jewish General Hospital, Mila Quebec Artificial Intelligence Institute, Montreal, QC, Canada.
¹⁴ College of Medicine and Health, University of Exeter, Exeter, UK.
¹⁵ Department of Mathematics and Statistics, University of Turku, Turku, Finland.
¹⁶ Department of Nursing Science, University of Turku and Turku University Hospital, Turku, Finland.
¹⁷ Research Ethics Board, Women's College Hospital, Toronto, ON, Canada.
¹⁸ Health Canada and Public Health Agency of Canada's Research Ethics Board, Toronto, ON, Canada.
¹⁹ NICE Computing SA, Lausanne, Switzerland.
²⁰ European Federation for Medical Informatics (EFMI).
²¹ ITU/WHO Focus Group on Artificial Intelligence for Health (FG-AI4H).
²² Fraunhofer Heinrich Hertz Institute, Berlin, Germany.
²³ Berlin Institute of Technology, Berlin, Germany.

Abstract

Aim: To develop a consensus paper on the central points of an international invitational think-tank on nursing and artificial intelligence (AI).

Methods: 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.

Implications for nursing: 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.

Conclusion: 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.

Impact: 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.

Keywords: health services research; information technology; leadership; management; nurse roles; policy; politics; technology; workforce issues.

MeSH terms

Artificial Intelligence*
Humans
Leadership*
Technology

Abstract

MeSH terms

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