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

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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.

Related collections

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Dissecting racial bias in an algorithm used to manage the health of populations

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Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

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Author and article information

Journal

Journal ID (nlm-journal-id): 7609811

Journal ID (nlm-ta): J Adv Nurs

Journal ID (iso-abbrev): J Adv Nurs

Title: Journal of advanced nursing

ISSN (Print): 0309-2402

ISSN (Electronic): 1365-2648

Publication date Nihms-submitted: 13 May 2022

Publication date (Print): 01 September 2021

Publication date (Electronic): 18 May 2021

Publication date PMC-release: 19 May 2022

Volume: 77

Issue: 9

Pages: 3707-3717

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

Author notes

Correspondence Charlene Ronquillo, Daphne Cockwell School of Nursing, Ryerson University, 350 Victoria Street, Toronto, ON, Canada M5B 1Z5. cronquillo@ 123456ryerson.ca

Charlene Esteban Ronquillo@ceronqu

Lisiane Pruinelli@LPruinelli

Charlene H. Chu@charchuRN

Ana Beduschi@ana_beduschi

Kenrick Cato@kenrickcato

Nicholas Hardiker@nickhardiker

Samira Rahimi@RahimiSamira

Thomas Wiegand@wiegand_t

Maxim Topaz@MaxTopaz