How AI Can Transform Nursing Practice

AI lets a computer system or robot learn, reason, communicate, and make decisions at or beyond human level. In nursing it's already here: schools use chatbots to run mock patient interactions, and developers are building robots meant to work alongside staff in clinical settings. Despite the fear around it, AI health technologies are designed to support nurses and students and improve patient care, not replace anyone.

Electronic medical records set the stage. About 96% of general acute care hospitals and 88% of office-based clinics already use them, and many of those systems carry some AI capability. Digitizing patient data is what makes the next wave of tools possible, because AI depends on data, information, and knowledge, the building blocks of informatics, to produce reliable insights.

That foundation creates health insights that were impossible on paper, from an individual's risk for a specific condition to population-level risk after an event like a disease outbreak. These tools will only get more sophisticated. The catch is that confusion still surrounds how and when to use them. Not all AI is welcome on the floor, the learning curve is steep, especially for educators, and some nurses understandably resist new technology.

AI's role in healthcare

Several subsets power clinical AI: machine learning, deep learning, predictive analysis, and remote patient monitoring. Each improves care by analyzing large data sets to sharpen provider decisions.

The applications are already concrete. Google Cloud partnered with Mayo Clinic to build patient-care tools that pull together information scattered across formats and locations, saving clinician time and helping predict and head off health problems. Chatbots and social robots are spreading too, accompanying nurses in practice and providing conversation and companionship for older adults in long-term care.

In nursing specifically, AI helps students and staff make better clinical decisions, answering a patient's questions about a condition, a dressing change, or a medication, particularly in the inpatient setting.

AI in nursing

AI is starting to reshape the nursing role in earnest. Two use cases stand out:

A triage system for the emergency room, such as KATE, gives ER nurses AI support to sort patients fast and route higher-risk cases to the right care quickly. An algorithm like the Rothman Index reads EHR data in real time to flag a patient's deteriorating condition. Both are clinical decision support tools that surface real-time information at the bedside. AI is also being used to detect patients at risk for falls and readmission.

Expect this to grow as the field maps where AI tools genuinely support quality care.

The concerns

The fear is real, and some of it is justified. Resistance often comes simply from changing the status quo, but educators have raised concrete worries about AI's effect on their workload and their role as teachers.

Speed is another problem. These tools develop faster than our understanding of their benefits and unintended consequences, which means policy and regulation have to catch up before the technology can be used safely at scale. Data risks matter too: HIPAA breaches and security gaps are genuine threats, and consumer-facing health AI needs to meet the same reliability and accuracy bar as the tools adopted inside healthcare. Solid training and clear guidance go a long way toward easing the fear.

The future of AI in nursing

Moving forward means the profession has to focus on what's possible rather than only on what's changing. A few steps make the integration work:

1. Educators build their own knowledge and comfort with AI basics through courses and workshops.
2. Programs add nursing informatics to the curriculum, covering data and technological literacy, systems thinking, critical thinking, genomics, AI algorithms, the ethics of AI, and the analysis and implications of big data.
3. Leadership incentivizes educators to drive these curriculum changes.
4. Informatics and digital health competencies get built into every area of nursing education.
5. Nurses serve as co-designers of AI health tools.
6. Educators get familiar with the TIGER Nursing Informatics Competencies Model (2009), built to integrate technology into nursing curricula.

The potential is broad but demands critical evaluation, and it will take time for AI health tools to blend fully into nursing programs. Used right, they strengthen clinical decision-making and enhance teaching and learning. The line that can't move: these tools exist to support and assist, never to replace the critical thinking of the nurse.

Sources: Health IT, Adoption of Electronic Health Records by Hospital Service Type 2019-2021 (2023); Buchanan C et al., Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review (2021); Gagne J, The State of Artificial Intelligence in Nursing Education (2023); Health IT, Office-based Physician Electronic Health Record Adoption (2023).