Human Connection Creates the Power Behind AI in Nursing

For their systematic review of the current evidence for multifaceted applications of AI in nursing care, the international team of researchers evaluated 11 studies that met their inclusion criteria. The studies were diverse in terms of design, sample sizes, populations, and AI algorithms used, with classification and regression tree (https://www.coursera.org/articles/decision-tree-machine-learning) (CART), deep learning (https://www.coursera.org/articles/what-is-deep-learning), and unsupervised classification (https://www.coursera.org/resources/ai-terms#:~:text=world%20is%20unstructured.-,Unsupervised%20learning,with%20Unsupervised%20Learning%20Courses.,-Voice%20recognition) as the top three types.

Across the studies, the researchers found that nurses are using AI in six common ways:

• Risk identification, such as using CART to identify risk for readmission
• Health assessment, such as using deep learning to identify dermatologic conditions
• Patient classification, such as using unsupervised classification to identify patient subgroups with different clinical trajectories
• Research development, such as creating AI tools for home healthcare assistance
• Care delivery and medical records, such as integrating AI in systems to increase efficiency and effectiveness
• Nursing care plan development, such as using support vector machines (https://deepgram.com/ai-glossary/support-vector-machines-svm) and wearables to measure and predict patients’ care needs (e.g., physical function, symptoms, medication adherence)

Calling AI “a critical turning point for the profession,” the authors highlighted nurses’ critical role in those six key areas and any others to come in the future.

“This perspective is not about AI taking the place of nurses; instead, it’s about a working partnership where AI helps nurses provide personalized, ethical, and caring health care,” they wrote (https://doi.org/10.2147%2FJMDH.S459946). 

At the heart of this partnership will be nurses’ active involvement in shaping how AI is developed and used. Their deep understanding of patients, the context of care, and their clinical instincts will be essential in making sure AI algorithms are focused on what truly matters to patients.

The researchers identified specific opportunities for nurses to contribute:

• Integrating AI-powered tools that predict potential problems
• Suggesting best practices for AI use in nursing
• Protecting patient privacy
• Preventing bias in AI algorithms
• Creating ethical frameworks that ensure fairness, accountability, and human oversight
• Openly communicating about AI with their patients (https://voice.ons.org/news-and-views/09-2024/what-does-chatgpt-say-about-emerging-trends-in-ai-and-other-oncology-nursing)
• Engaging in continuous learning and adapting

Finally, the researchers said that leaders and organizations should encourage a culture of innovation within the nursing field, equip nurses with the skills to understand and use AI effectively, and establish strong feedback channels.

“This personalized approach sees AI as a tool that helps nurses do their jobs better, not as a replacement, allowing them to provide more holistic and effective care while maintaining their irreplaceable role in making decisions and advocating for patients,” the authors concluded (https://doi.org/10.2147%2FJMDH.S459946). “AI tools should be designed and implemented with the needs of both nurses and patients in mind, seamlessly integrating into existing workflows without compromising the human connection that remains irreplaceable in healthcare delivery.”