The Journal of School Nursing2025, Vol. 41(1) 3–4© The Author(s) 2024Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/10598405241277456journals.sagepub.com/home/jsn
Have you ever found yourself scrolling
on a phone or computer and had Google or Amazon “suggest” a book, movie, or product for you? When you create an email or text message, does your device “suggest” a word based on the previous words you have entered? These are examples of artificial intelligence (AI) in action—already in heavy use in commercial products and increasingly being used in healthcare to improve efficiency and patient outcomes.
AI allows a computer to “perform tasks that traditionally require human intelligence” (Shepherd & Griesheimer, 2024, p. 15). It is driven by machine learning—an iterative process that gives a computer a combination of data and instructions that allow it to “systematically recognize and label patterns in the data” (Chemtob, 2023; Walker et al., 2023, Table 1). Machine learning continuously builds on previous data and pattern recognition and uses that “learning” to develop additional predictions (Walker et al., 2023). AI and machine learning are made possible by Big Data—huge, fast-moving, and complex datasets that as a whole, are beyond the capacity of the human mind to process and utilize (Warren, 2017).
AI brings tremendous benefits by reducing the “noise” in Big Data, supporting human work, and allowing the analysis of the complexity of human behavior and function—huge benefits in the delivery of healthcare. AI also brings hazards that can cause harm to patients. The instructions and algorithms used in AI are initiated by humans—and come with the risks of human frailty including bias related to social indicators of health in the design, development, and deployment of AI processes (American Medical Association [AMA], 2023, 2024) as well as the prioritization of machine versus the human relationship essential to nursing care (American Nurses Association [ANA], 2022).
The American Nurses Association has addressed the growing integration of AI into healthcare through a position statement that focuses on its ethical use (ANA, 2022). ANA advocates for the importance of human interactions and relationships that are central to nursing care, encouraging a proactive response of nurses to emphasize “agency, caring, and influence over how technology is developed and applied” (ANA, 2022, p. 1). It breaks down the implications of AI in the application of nursing skills and judgment; the methods and application of AI, its relationship to social justice and equity, the use of data and informatics, and regulatory concerns (ANA, 2022). AI is already driving healthcare with an expectation for exponential growth in its application and impact.
School nurses stand at the intersection of the health and education sectors, and thus have a long history of implementing the newest health technologies to benefit their students and communities (Johnson, 2019). They are adept at navigating ethical practice in an educational setting; understanding and mitigating bias and injustice; and assuring equitable access to care (National Association of School Nurses [NASN], 2022; 2023). School nurses must now anticipate the use of AI in healthcare and be ready to assure proper implementation and safeguards. Most importantly, they must maintain the trust inherent in the nurse–patient relationship. To that end, the National Association of School Nurses (NASN) and individual school nurses must partner to (1) educate themselves on AI use in school health; (2) provide strong oversight in the development of algorithms for electronic documentation systems used in schools; and (3) promote equity and limit bias by assuring that all students are included in nationally standardized datasets such as NASN’s Every Student Counts!—the Big Data of child health (Stanislo, 2023). The NASN is encouraged to develop its own position statement on the use of AI in schools, support nurse education in the use of AI, and lead producers of school health documentation systems in assuring ethical and unbiased AI use.
School nurses are the leaders in school health ethics and care. We must be proactive in our efforts to influence how AI is integrated into school health data and documentation systems. The issue is not IF, but WHEN AI is used in school nursing. We cannot wait for others to speak for us but must take the lead on the appropriate application of AI in the school health setting.
University of Washington School of Nursing Seattle, WA, USA
Kathleen H. Johnson: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Writing – original draft; Writing – review & editing.
Kathleen H. Johnson https://orcid.org/0000-0002-1890-250X
American Medical Association (2023). AMA issues new principles for AI development, deployment & use. https://www.ama-assn.org/press-center/press-releases/ama-issues-new-principles-aidevelopment-deployment-use
American Medical Association (2024). AMA future of health: The emerging landscape of augmented intelligence in health care. https://www.ama-assn.org/practice-management/digital/amafuture-health-emerging-landscape-augmented-intelligence-healthcare#:∼:text=Bias%20may%20exacerbate%20existing%20social,when%20AI%20is%20being%20used
American Nurses Association (2022). The ethical use of artificial intelligence in nursing practice. https://www.nursingworld.org/∼48f653/globalassets/practiceandpolicy/nursing-excellence/anaposition-statements/the-ethical-use-of-artificial-intelligence-innursing-practice_bod-approved-12_20_22.pdf
Chemtob, D. (2023). Forbes Daily: Artificial intelligence and what you need to know. https://www.forbes.com/sites/daniellechemtob/2023/07/20/forbes-daily-artificial-intelligence-and-what-you-needto-know/?sh=6bd94ee2ba31
Johnson, K. H. (2019). Power of the past, celebrate the present, force of the future part 6: NASN the next 50 years. NASN School Nurse, 34(4), 217–222. https://doi.org/10.1177/1942602X19851237
National Association of School Nurses (2022). School nursing: Scope and standards of practice (4th ed.). NASN.
National Association of School Nurses (2023). Safe, supportive, equitable schools [Position Statement]. https://www.nasn.org/nasnresources/professional-practice-documents/position-statements/ps-safe
Shepherd, J., & Griesheimer, D. (2024). FAQs: AI and prompt engineering. American Nurse, 19(6), 14–19. https://doi.org/10.51256/ANJ062414
Stanislo, K. J. (2023). Revisiting the national school health data set: Every student counts! NASN School Nurse, 38(1), 26–30. https://doi.org/10.1177/1942602X221137519
Walker, R., Dillard-Wright, J., & Iradukunda, F. (2023). Algorithmic bias in artificial intelligence is a problem—and the root issue is power. Nursing Outlook, 71(5), 102023. https://doi.org/10.1016/j.outlook.2023.102023
Warren, J. J. (2017). A big data primer. In C. W. Delaney, C. A. Weaver, J. J. Warren, T. R. Clancy, & R. L. Simpson (Eds.), Big data-enabled nursing (pp. 33–57). Springer.
Correction (September 2024): Article updated to correct the article type from “Letter to the Editor” to “Guest Editorial”.
