Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence

Hanna von Gerich; Hans Moen; Lorraine J. Block; Charlene H. Chu; Haley DeForest; Mollie Hobensack; Martin Michalowski; James Mitchell; Raji Nibber; Mary Anne Olalia; Lisiane Pruinelli; Charlene E. Ronquillo; Maxim Topaz; Laura Maria Peltonen

doi:10.1016/j.ijnurstu.2021.104153

Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence

Hanna von Gerich, Hans Moen, Lorraine J. Block, Charlene H. Chu, Haley DeForest, Mollie Hobensack, Martin Michalowski, James Mitchell, Raji Nibber, Mary Anne Olalia, Lisiane Pruinelli, Charlene E. Ronquillo, Maxim Topaz, Laura Maria Peltonen

Academics (Nursing)

Research output: Contribution to journal › Review article › peer-review

54 Scopus citations

Abstract

Background: Research on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare. Objectives: To synthesize currently available state-of the-art research in artificial intelligence -based technologies applied in nursing practice. Design: Scoping review Methods: PubMed, CINAHL, Web of Science and IEEE Xplore were searched for relevant articles with queries that combine names and terms related to nursing, artificial intelligence and machine learning methods. Included studies focused on developing or validating artificial intelligence -based technologies with a clear description of their impacts on nursing. We excluded non-experimental studies and research targeted at robotics, nursing management and technologies used in nursing research and education. Results: A total of 7610 articles published between January 2010 and March 2021 were revealed, with 93 articles included in this review. Most studies explored the technology development (n = 55, 59.1%) and formation (testing) (n = 28, 30.1%) phases, followed by implementation (n = 9, 9.7%) and operational (n = 1, 1.1%) phases. The vast majority (73.1%) of studies provided evidence with a descriptive design (level VI) while only a small portion (4.3%) were randomised controlled trials (level II). The study aims, settings and methods were poorly described in the articles, and discussion of ethical considerations were lacking in 36.6% of studies. Additionally, one-third of papers (33.3%) were reported without the involvement of nurses. Conclusions: Contemporary research on applications of artificial intelligence -based technologies in nursing mainly cover the earlier stages of technology development, leaving scarce evidence of the impact of these technologies and implementation aspects into practice. The content of research reported is varied. Therefore, guidelines on research reporting and implementing artificial intelligence -based technologies in nursing are needed. Furthermore, integrating basic knowledge of artificial intelligence -related technologies and their applications in nursing education is imperative, and interventions to increase the inclusion of nurses throughout the technology research and development process is needed.

Original language	English (US)
Article number	104153
Journal	International Journal of Nursing Studies
Volume	127
DOIs	https://doi.org/10.1016/j.ijnurstu.2021.104153
State	Published - Mar 2022

Bibliographical note

Funding Information:
This work was supported by The Finnish Nursing Education Foundation sr., the Academy of Finland (315376), and the Brocher Fondation. The Brocher Foundation's mission is to encourage research on the ethical, legal and social implications of new medical technologies. Its main activities are to host visiting researchers and to organize symposia, workshops and summer or winter academies. More information on the Brocher Foundation program is available at www.brocher.ch Ms. Hobensack is supported by the National Institute for Nursing Research training grant Reducing Health Disparities through Informatics (RHeaDI) (T32NR007969) as a predoctoral trainee.

Funding Information:
This article was a collaboration within a nursing informatics network and was conducted in multi-professional teams. All the authors have connection to nursing informatics, as well as backgrounds as registered nurses, public health nurses and/or computer engineering. Author contributions: Conceptualization HvG, LMP, MT, LP, CER, HM, MM; Data curation HvG, LMP, MAO, CER, MH, LP, RN, JM, LB, MM, HM; Formal analysis HvG, LMP, HM; Funding acquisition LMP, RN, LP, CC, CER, MT, HD; Investigation HvG; Methodology HvG, LMP, LP, MT, CER, MAO; Project administration HvG, LMP; Resources; Software used was Covidence by University of Toronto access by CC, library services used University of Turku, University of British Columbia, University of Minnesota, University of Keele, Columbia University; Supervision LMP; Visualization HvG, LMP; Roles/Writing - original draft HvG, LMP; Writing - review & editing HvG, LMP, MT, LP, CER, HM, MH, MAO, MM, CC, HD, RN, JM, LB. This work was supported by The Finnish Nursing Education Foundation sr. the Academy of Finland (315376), and the Brocher Fondation. The Brocher Foundation's mission is to encourage research on the ethical, legal and social implications of new medical technologies. Its main activities are to host visiting researchers and to organize symposia, workshops and summer or winter academies. More information on the Brocher Foundation program is available at www.brocher.ch Ms. Hobensack is supported by the National Institute for Nursing Research training grant Reducing Health Disparities through Informatics (RHeaDI) (T32NR007969) as a predoctoral trainee.

Publisher Copyright:
© 2021 The Author

Keywords

AI
Artificial intelligence
Nursing
Nursing informatics
Scoping review

PubMed: MeSH publication types

Journal Article
Review

Access

10.1016/j.ijnurstu.2021.104153

OpenUrl availability

Full text

Cite this

von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M. A., Pruinelli, L., Ronquillo, C. E., Topaz, M., & Peltonen, L. M. (2022). Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies, 127, Article 104153. https://doi.org/10.1016/j.ijnurstu.2021.104153

von Gerich, H, Moen, H, Block, LJ, Chu, CH, DeForest, H, Hobensack, M, Michalowski, M, Mitchell, J, Nibber, R, Olalia, MA, Pruinelli, L, Ronquillo, CE, Topaz, M & Peltonen, LM 2022, 'Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence', International Journal of Nursing Studies, vol. 127, 104153. https://doi.org/10.1016/j.ijnurstu.2021.104153

@article{355b1e06ba93420288dea449a48ad0bf,

title = "Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence",

abstract = "Background: Research on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare. Objectives: To synthesize currently available state-of the-art research in artificial intelligence -based technologies applied in nursing practice. Design: Scoping review Methods: PubMed, CINAHL, Web of Science and IEEE Xplore were searched for relevant articles with queries that combine names and terms related to nursing, artificial intelligence and machine learning methods. Included studies focused on developing or validating artificial intelligence -based technologies with a clear description of their impacts on nursing. We excluded non-experimental studies and research targeted at robotics, nursing management and technologies used in nursing research and education. Results: A total of 7610 articles published between January 2010 and March 2021 were revealed, with 93 articles included in this review. Most studies explored the technology development (n = 55, 59.1%) and formation (testing) (n = 28, 30.1%) phases, followed by implementation (n = 9, 9.7%) and operational (n = 1, 1.1%) phases. The vast majority (73.1%) of studies provided evidence with a descriptive design (level VI) while only a small portion (4.3%) were randomised controlled trials (level II). The study aims, settings and methods were poorly described in the articles, and discussion of ethical considerations were lacking in 36.6% of studies. Additionally, one-third of papers (33.3%) were reported without the involvement of nurses. Conclusions: Contemporary research on applications of artificial intelligence -based technologies in nursing mainly cover the earlier stages of technology development, leaving scarce evidence of the impact of these technologies and implementation aspects into practice. The content of research reported is varied. Therefore, guidelines on research reporting and implementing artificial intelligence -based technologies in nursing are needed. Furthermore, integrating basic knowledge of artificial intelligence -related technologies and their applications in nursing education is imperative, and interventions to increase the inclusion of nurses throughout the technology research and development process is needed.",

keywords = "AI, Artificial intelligence, Nursing, Nursing informatics, Scoping review",

author = "{von Gerich}, Hanna and Hans Moen and Block, {Lorraine J.} and Chu, {Charlene H.} and Haley DeForest and Mollie Hobensack and Martin Michalowski and James Mitchell and Raji Nibber and Olalia, {Mary Anne} and Lisiane Pruinelli and Ronquillo, {Charlene E.} and Maxim Topaz and Peltonen, {Laura Maria}",

note = "Funding Information: This work was supported by The Finnish Nursing Education Foundation sr., the Academy of Finland (315376), and the Brocher Fondation. The Brocher Foundation's mission is to encourage research on the ethical, legal and social implications of new medical technologies. Its main activities are to host visiting researchers and to organize symposia, workshops and summer or winter academies. More information on the Brocher Foundation program is available at www.brocher.ch Ms. Hobensack is supported by the National Institute for Nursing Research training grant Reducing Health Disparities through Informatics (RHeaDI) (T32NR007969) as a predoctoral trainee. Funding Information: This article was a collaboration within a nursing informatics network and was conducted in multi-professional teams. All the authors have connection to nursing informatics, as well as backgrounds as registered nurses, public health nurses and/or computer engineering. Author contributions: Conceptualization HvG, LMP, MT, LP, CER, HM, MM; Data curation HvG, LMP, MAO, CER, MH, LP, RN, JM, LB, MM, HM; Formal analysis HvG, LMP, HM; Funding acquisition LMP, RN, LP, CC, CER, MT, HD; Investigation HvG; Methodology HvG, LMP, LP, MT, CER, MAO; Project administration HvG, LMP; Resources; Software used was Covidence by University of Toronto access by CC, library services used University of Turku, University of British Columbia, University of Minnesota, University of Keele, Columbia University; Supervision LMP; Visualization HvG, LMP; Roles/Writing - original draft HvG, LMP; Writing - review & editing HvG, LMP, MT, LP, CER, HM, MH, MAO, MM, CC, HD, RN, JM, LB. This work was supported by The Finnish Nursing Education Foundation sr. the Academy of Finland (315376), and the Brocher Fondation. The Brocher Foundation's mission is to encourage research on the ethical, legal and social implications of new medical technologies. Its main activities are to host visiting researchers and to organize symposia, workshops and summer or winter academies. More information on the Brocher Foundation program is available at www.brocher.ch Ms. Hobensack is supported by the National Institute for Nursing Research training grant Reducing Health Disparities through Informatics (RHeaDI) (T32NR007969) as a predoctoral trainee. Publisher Copyright: {\textcopyright} 2021 The Author",

year = "2022",

month = mar,

doi = "10.1016/j.ijnurstu.2021.104153",

language = "English (US)",

volume = "127",

journal = "International Journal of Nursing Studies",

issn = "0020-7489",

publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Artificial Intelligence -based technologies in nursing

T2 - A scoping literature review of the evidence

AU - von Gerich, Hanna

AU - Moen, Hans

AU - Block, Lorraine J.

AU - Chu, Charlene H.

AU - DeForest, Haley

AU - Hobensack, Mollie

AU - Michalowski, Martin

AU - Mitchell, James

AU - Nibber, Raji

AU - Olalia, Mary Anne

AU - Pruinelli, Lisiane

AU - Ronquillo, Charlene E.

AU - Topaz, Maxim

AU - Peltonen, Laura Maria

N1 - Funding Information: This work was supported by The Finnish Nursing Education Foundation sr., the Academy of Finland (315376), and the Brocher Fondation. The Brocher Foundation's mission is to encourage research on the ethical, legal and social implications of new medical technologies. Its main activities are to host visiting researchers and to organize symposia, workshops and summer or winter academies. More information on the Brocher Foundation program is available at www.brocher.ch Ms. Hobensack is supported by the National Institute for Nursing Research training grant Reducing Health Disparities through Informatics (RHeaDI) (T32NR007969) as a predoctoral trainee. Funding Information: This article was a collaboration within a nursing informatics network and was conducted in multi-professional teams. All the authors have connection to nursing informatics, as well as backgrounds as registered nurses, public health nurses and/or computer engineering. Author contributions: Conceptualization HvG, LMP, MT, LP, CER, HM, MM; Data curation HvG, LMP, MAO, CER, MH, LP, RN, JM, LB, MM, HM; Formal analysis HvG, LMP, HM; Funding acquisition LMP, RN, LP, CC, CER, MT, HD; Investigation HvG; Methodology HvG, LMP, LP, MT, CER, MAO; Project administration HvG, LMP; Resources; Software used was Covidence by University of Toronto access by CC, library services used University of Turku, University of British Columbia, University of Minnesota, University of Keele, Columbia University; Supervision LMP; Visualization HvG, LMP; Roles/Writing - original draft HvG, LMP; Writing - review & editing HvG, LMP, MT, LP, CER, HM, MH, MAO, MM, CC, HD, RN, JM, LB. This work was supported by The Finnish Nursing Education Foundation sr. the Academy of Finland (315376), and the Brocher Fondation. The Brocher Foundation's mission is to encourage research on the ethical, legal and social implications of new medical technologies. Its main activities are to host visiting researchers and to organize symposia, workshops and summer or winter academies. More information on the Brocher Foundation program is available at www.brocher.ch Ms. Hobensack is supported by the National Institute for Nursing Research training grant Reducing Health Disparities through Informatics (RHeaDI) (T32NR007969) as a predoctoral trainee. Publisher Copyright: © 2021 The Author

PY - 2022/3

Y1 - 2022/3

N2 - Background: Research on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare. Objectives: To synthesize currently available state-of the-art research in artificial intelligence -based technologies applied in nursing practice. Design: Scoping review Methods: PubMed, CINAHL, Web of Science and IEEE Xplore were searched for relevant articles with queries that combine names and terms related to nursing, artificial intelligence and machine learning methods. Included studies focused on developing or validating artificial intelligence -based technologies with a clear description of their impacts on nursing. We excluded non-experimental studies and research targeted at robotics, nursing management and technologies used in nursing research and education. Results: A total of 7610 articles published between January 2010 and March 2021 were revealed, with 93 articles included in this review. Most studies explored the technology development (n = 55, 59.1%) and formation (testing) (n = 28, 30.1%) phases, followed by implementation (n = 9, 9.7%) and operational (n = 1, 1.1%) phases. The vast majority (73.1%) of studies provided evidence with a descriptive design (level VI) while only a small portion (4.3%) were randomised controlled trials (level II). The study aims, settings and methods were poorly described in the articles, and discussion of ethical considerations were lacking in 36.6% of studies. Additionally, one-third of papers (33.3%) were reported without the involvement of nurses. Conclusions: Contemporary research on applications of artificial intelligence -based technologies in nursing mainly cover the earlier stages of technology development, leaving scarce evidence of the impact of these technologies and implementation aspects into practice. The content of research reported is varied. Therefore, guidelines on research reporting and implementing artificial intelligence -based technologies in nursing are needed. Furthermore, integrating basic knowledge of artificial intelligence -related technologies and their applications in nursing education is imperative, and interventions to increase the inclusion of nurses throughout the technology research and development process is needed.

AB - Background: Research on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare. Objectives: To synthesize currently available state-of the-art research in artificial intelligence -based technologies applied in nursing practice. Design: Scoping review Methods: PubMed, CINAHL, Web of Science and IEEE Xplore were searched for relevant articles with queries that combine names and terms related to nursing, artificial intelligence and machine learning methods. Included studies focused on developing or validating artificial intelligence -based technologies with a clear description of their impacts on nursing. We excluded non-experimental studies and research targeted at robotics, nursing management and technologies used in nursing research and education. Results: A total of 7610 articles published between January 2010 and March 2021 were revealed, with 93 articles included in this review. Most studies explored the technology development (n = 55, 59.1%) and formation (testing) (n = 28, 30.1%) phases, followed by implementation (n = 9, 9.7%) and operational (n = 1, 1.1%) phases. The vast majority (73.1%) of studies provided evidence with a descriptive design (level VI) while only a small portion (4.3%) were randomised controlled trials (level II). The study aims, settings and methods were poorly described in the articles, and discussion of ethical considerations were lacking in 36.6% of studies. Additionally, one-third of papers (33.3%) were reported without the involvement of nurses. Conclusions: Contemporary research on applications of artificial intelligence -based technologies in nursing mainly cover the earlier stages of technology development, leaving scarce evidence of the impact of these technologies and implementation aspects into practice. The content of research reported is varied. Therefore, guidelines on research reporting and implementing artificial intelligence -based technologies in nursing are needed. Furthermore, integrating basic knowledge of artificial intelligence -related technologies and their applications in nursing education is imperative, and interventions to increase the inclusion of nurses throughout the technology research and development process is needed.

KW - AI

KW - Artificial intelligence

KW - Nursing

KW - Nursing informatics

KW - Scoping review

UR - http://www.scopus.com/inward/record.url?scp=85123737956&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85123737956&partnerID=8YFLogxK

U2 - 10.1016/j.ijnurstu.2021.104153

DO - 10.1016/j.ijnurstu.2021.104153

M3 - Review article

C2 - 35092870

AN - SCOPUS:85123737956

SN - 0020-7489

VL - 127

JO - International Journal of Nursing Studies

JF - International Journal of Nursing Studies

M1 - 104153

ER -

Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence

Abstract

Bibliographical note

Keywords

PubMed: MeSH publication types

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this