A Clinical Practice Guide for New Jersey Nurses and Physicians
AI Is Already in Your Workflow
Artificial intelligence is not a future concern. It is shaping clinical practice today. Early warning and sepsis algorithms, readmission risk tools, smart documentation features, AI-assisted clinical decision support, and remote monitoring systems are already embedded in electronic health records and patient care workflows across New Jersey. Many clinicians use these tools daily without fully recognizing them as AI. The ANA Code of Ethics (2025), Provision 7.5, affirms that machine learning and AI are already integral to healthcare, and that their appropriate application should support the profession’s core values rather than supplant clinical judgment.
Practical Benefits
When implemented thoughtfully, AI can meaningfully strengthen care. Research shows that AI-assisted monitoring enables earlier detection of physiological deterioration, reducing complications and hospital stays. Predictive analytics support timely intervention before a patient’s condition worsens. Automated documentation tools return time to direct patient care. Virtual communication tools improve health literacy and medication adherence, supporting better post-discharge outcomes. A 2025 systematic review in Frontiers in Medicine found that AI integration in nursing settings was associated with improved patient outcomes, greater operational efficiency, reduced cognitive load, and increased job satisfaction among nurses (Wei et al., 2025).
Where Clinicians Must Exercise Caution
Algorithmic bias is the most urgent patient safety concern in clinical AI. Research has demonstrated that widely used hospital AI algorithms have systematically underestimated illness severity in Black patients, compounding existing disparities. A systematic review in Ethnicity and Health found that racial and ethnic bias in AI health algorithms remains widespread (Hussain et al., 2025). Automation bias, or the tendency to defer to tools that are not well understood, poses an equally serious risk. The ANA is unequivocal: AI does not replace a nurse’s judgment, critical thinking, or assessment skills (Provision 1.1). Clinicians must also verify that AI tools comply with HIPAA and institutional privacy standards, and must never input patient data into unauthorized applications.
Clinical guidance: When an algorithm’s recommendation conflicts with your assessment of the patient in front of you, trust your assessment and escalate accordingly. Document your clinical reasoning independently of any AI-generated flag or suggestion.
From Awareness to Leadership
Nurses are not merely end users of AI. They are essential to its safe and equitable implementation. The National League for Nursing AI Vision Statement (2025) calls on nurses to participate in shared governance, bias mitigation, and lifecycle accountability for AI systems. Concrete steps include asking how AI tools on your unit are built and monitored for bias, participating in AI implementation committees, advocating for formal AI literacy training, informing patients when AI contributes to their care, and reporting AI-related errors through your institution’s safety event reporting system. Engaging with NJSNA and national professional organizations to shape AI policy is equally important. If nurses and clinicians are not at the governance table, decisions about AI will be made without the perspective of those closest to the patient.
The Bottom Line
AI is already present in nursing and medicine, shaping triage decisions, discharge planning, staffing models, and clinical documentation on units across New Jersey. The real question is whether nurses and clinicians recognize it, understand it, and engage with it wisely. Nursing has always adapted to innovation, from aseptic technique to electronic health records. With informed, engaged clinicians actively shaping AI governance, this technology can reduce burden, improve outcomes, and extend compassionate, equitable care. That outcome depends on nurses and physicians remaining in control of how AI is used, for every patient, regardless of background, and in every clinical setting across New Jersey.
References
American Nurses Association. (2022). The ethical use of artificial intelligence in nursing practice [Position statement]. ANA Center for Ethics and Human Rights.
American Nurses Association. (2025). Code of ethics for nurses with interpretive statements (Provisions 1.1, 4.2, 7.5). American Nurses Publishing.
Hassanein, S., et al. (2025). Artificial intelligence in nursing: An integrative review of clinical and operational impacts. Frontiers in Digital Health, 7, Article 1552372.
Hussain, S. A., Bresnahan, M., & Zhuang, J. (2025). The bias algorithm: How AI in healthcare exacerbates ethnic and racial disparities. Ethnicity and Health, 30(2), 197–214.
National League for Nursing. (2025). NLN AI vision statement: Nursing education in the age of artificial intelligence.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
Wei, Q., et al. (2025). The integration of AI in nursing: Addressing current applications, challenges, and future directions. Frontiers in Medicine, 12, Article 1545420.
