Nursing and artificial intelligence now meet at almost every point in a shift. AI drafts the charting after an assessment, and it helps decide which patient gets a call first. When we first covered this topic in 2024, most of the evidence came from reviews describing what AI might do for nurses. Two years later, there are full-shift time studies, large hospital deployments, and published ethics guidance from the American Nurses Association.
This updated article covers what the research shows about AI in nursing today. It looks at where the tools save time, where they add work, and how nurses can adopt them safely. It also explains how AI fits into remote patient monitoring (RPM), where nurses already review large volumes of home health data. In the following article, you’ll learn:
- Why nursing workforce pressures are pushing health systems toward AI
- Five areas where AI supports nurses today, and the evidence behind each
- The limits and risks nurses should plan for
- What the ANA says about the ethical use of AI
- How AI and nurses work together in RPM programs
Nursing and Artificial Intelligence
The case for nursing and artificial intelligence starts with the workforce. The 2024 National Nursing Workforce Study from the National Council of State Boards of Nursing (NCSBN) surveyed about 800,000 nurses. More than 138,000 nurses had left the workforce since 2022. Almost 40% said they plan to leave or retire within five years.
Burnout sits behind much of that. Among nurses planning to leave for reasons other than retirement, 41.5% named stress and burnout. Short staffing and heavy workloads came up again and again.
Documentation is a large share of that workload. In long-term care, nurses can spend up to a third of their working time on it. Every minute of charting is a minute away from the patient.
AI tools aim at two of these pressures directly. One is the time nurses spend writing things down. The other is the work of sorting which patients need attention first.
5 Ways AI Supports Nurses Today
Nursing and artificial intelligence come together in many kinds of tools. These five areas have the clearest evidence or the widest use.
1) Documentation and Ambient Listening
Ambient AI listens to a nurse-patient conversation or a nurse’s spoken notes, then drafts the documentation. The nurse reviews and signs it. This is the most studied use of AI in nursing right now.
A 2026 study in the Journal of Medical Internet Research followed 52 registered nurses across 14 long-term care facilities in Germany. Researchers observed 770 hours of full shifts, before and after nurses started using a mobile AI speech assistant. Documentation time per morning shift dropped by an adjusted 14.8 minutes. That is a 27% reduction.
The fine print matters. A JMIR commentary on the study noted that time spent reviewing entries went up after the tool arrived. Nurses liked the documentation system more, yet overall workplace satisfaction stayed flat. The tool’s developer also ran the study. So AI shifts documentation from typing to checking. Checking still takes nurse time.
Nursing documentation is also harder to automate than a physician note. Nurses chart in structured flowsheets across the whole shift. A tool has to fill discrete fields correctly, over and over, for every assessment.
A 2025 study in Nursing: Research and Reviews points the same way. At a regional hospital in Japan, an AI tool drafted nursing care summaries and nurses revised them. Summary creation time fell by about 40%.
2) Early Warning for Patient Deterioration
Predictive models scan vital signs, lab results, and chart data to flag patients at risk of getting worse. The best-known example puts nurses at the center.
Kaiser Permanente studied its Advance Alert Monitor program across 19 hospitals, with results in the New England Journal of Medicine. When the model flagged a high-risk patient, a team of remote nurses reviewed the chart. They passed concerns to the hospital’s rapid-response team. Thirty-day mortality after an alert was lower in the intervention group, with an adjusted relative risk of 0.84.
The model never acted alone. Trained nurses screened each alert before it reached the bedside. That kept noise away from floor nurses and put clinical judgment between the algorithm and the patient.
Results vary widely from tool to tool. A 2021 external validation in JAMA Internal Medicine tested a widely used proprietary sepsis model at one academic medical center. The model missed 67% of sepsis cases. It also fired alerts on 18% of all hospitalized patients. Alert volume and accuracy are separate questions, and each tool needs testing on the patients it will serve.
3) Alert Triage in Remote Patient Monitoring
Remote patient monitoring sends a steady flow of readings from home devices to care teams. Blood pressure, weight, glucose, and oxygen readings can arrive daily for every enrolled patient.
AI can sort those readings by clinical priority. It might flag a weight trend that suggests fluid retention in a patient with heart failure. It might catch a run of high blood pressure readings over several days. An RPM nurse then decides what happens next, whether that is a call, a medication review with the provider, or patient education. Our guide to AI in RPM walks through more examples.
The lesson from hospital early warning carries over to the home. Alerts work best when a nurse reviews them, and models should be checked against the program’s own patients.
4) Staffing and Workload Forecasting
This article first covered a 2024 review in the Journal of Medicine, Surgery, and Public Health. That review pointed to predictive staffing as a way to improve nurse work-life balance. Models can forecast admissions and patient acuity. Managers can then build schedules before a surge arrives.
Evidence here is less mature than for documentation. Most published work describes models and pilots, with fewer studies of nurse outcomes. Staffing AI is best treated as decision support for managers. Nurses should help shape how it is used, which matches the ANA’s call for nurse involvement in AI decisions.
5) Handoffs and Care Coordination
AI summaries can pull a patient’s recent vitals, medications, and notes into a short handoff draft. This can help at shift change. It can also help when a remote nurse passes a concern to a provider. The Japanese summary study above is one example. As with documentation, the nurse checks the draft before anyone acts on it.
Where AI Falls Short in Nursing
Studies of nursing and artificial intelligence show real gains. They also point to limits that care teams should plan for.
- Review time. Every AI draft needs a nurse to check it. If review takes longer than writing, the tool has missed its purpose.
- Alert fatigue. Poorly tuned models create false alarms. Over time, nurses learn to tune them out.
- Uneven performance. Speech recognition can perform differently across accents, dialects, and clinical terms. A predictive model trained on one population may not fit another.
- Privacy and security. AI tools that touch patient data need the same HIPAA safeguards as any clinical system. Vendors that handle protected health information also need business associate agreements.
- Accountability. The nurse remains responsible for the care decision, even when software made the first suggestion.
Nursing Ethics and AI: What the ANA Says
The American Nurses Association addresses nursing and artificial intelligence in its position statement, The Ethical Use of Artificial Intelligence in Nursing Practice. The statement places caring and compassion at the center of the nurse-patient relationship. AI should support a nurse’s knowledge and skill, and the nurse stays in charge of the decision.
The ANA is direct about accountability. Nurses remain accountable for their practice even when a system or technology fails. That principle shapes how AI should be deployed. A nurse needs enough insight into a tool to know when to question it.
The position statement asks nurses to pay attention to four areas:
- How AI tools are developed, tested, and validated
- Whether the data and algorithms treat patient groups fairly
- How patient data are protected and how consent is explained to patients
- How AI policy and regulation are written, with nurses contributing
The 2025 revision of the ANA Code of Ethics for Nurses also speaks to machine learning and AI. For nurse leaders, the message is consistent. Bring nurses into AI decisions early, from selection through evaluation.
How to Bring AI Into Nursing Workflows Safely
Health systems and RPM programs planning nursing and artificial intelligence projects can use a short checklist. Each step draws on the studies and guidance above.
- Start with a nurse-defined problem. Pick one pain point, such as flowsheet time or alert volume, and measure it before go-live.
- Validate locally. Test the model on your own patients. Check how many true problems it catches and how many alerts it creates.
- Keep a nurse in the loop. Route every alert and draft through a nurse before any clinical action.
- Measure the full picture. Track documentation time, review time, alert burden, safety events, and nurse satisfaction together.
- Train and listen. Give nurses protected training time and a clear way to report problems with the tool.
- Protect patient data. Confirm HIPAA safeguards, access controls, and business associate agreements with every vendor.
Clear RPM workflows make each of these steps easier to apply in home monitoring programs.
Nursing and Artificial Intelligence in Remote Patient Monitoring
Remote monitoring is a natural fit for nursing and artificial intelligence. RPM nurses already work from data rather than a bedside view. They review readings, call patients, coach on medications, and escalate to providers. AI can help order that work so the most urgent patients get attention first.
Any AI layer depends on the data underneath it. Missed readings, pairing failures, and manual uploads leave gaps. Gaps in the data limit what any model can detect. Cellular-connected devices send readings automatically, with no syncing, no apps, and no Wi-Fi for the patient to manage. Nurses and the software they use get a more complete record to work from.
This is where Tenovi fits. Tenovi provides remote monitoring infrastructure with 60+ devices. Standalone cellular devices carry their own SIMs, and Bluetooth devices connect through the Tenovi Gateway. One API delivers readings to a partner’s platform, and Tenovi handles fulfillment, provisioning, and support. Care teams and their software partners decide how to apply AI to that data. Nurses stay in charge of patient care.
Nursing and Artificial Intelligence FAQs
This section answers common questions about nursing and artificial intelligence. It covers how nurses use AI today and what research shows about time savings. It also covers the main risks, the ANA’s position, and how AI supports remote patient monitoring nurses.
1) How is artificial intelligence used in nursing today?
Nurses use AI most often for documentation, early warning alerts, and alert triage. Ambient tools draft notes and flowsheet entries from spoken input. Predictive models flag patients at risk of decline. In remote patient monitoring, AI can sort home readings so nurses see the most urgent patients first.
2) Will AI replace nurses?
No. Current AI tools draft, sort, and predict, while nurses make the care decisions. The ANA holds that technology supports nursing knowledge and skill, and nurses stay accountable for patient care. The strongest results so far, such as Kaiser Permanente’s early warning program, paired models with nurse review.
3) Does AI documentation actually save nurses time?
Early evidence says yes, with caveats. A 2026 JMIR study in German long-term care found a 27% drop in documentation time per morning shift. Review time went up, though, and overall workplace satisfaction did not change. Programs should measure both writing time and review time.
4) What does the ANA say about AI in nursing practice?
The ANA position statement says AI should support the core values of nursing, including caring and compassion. Nurses remain accountable even when technology fails. The ANA asks nurses to look at how tools are tested, whether data are fair, how patient data are protected, and how AI policy is written.
5) How does AI help remote patient monitoring nurses?
AI helps RPM nurses sort large volumes of home readings by clinical priority. It can flag trends such as rising blood pressure or sudden weight gain. The nurse reviews each flag and decides on outreach or escalation. Complete, automatic data from cellular-connected devices makes those flags more useful.
Understanding Nursing and Artificial Intelligence
Nursing and artificial intelligence have moved from early promise into daily practice. The best evidence today supports AI for documentation and early warning, with nurses reviewing every output. Documentation tools can cut charting time, though review work grows. Early warning works best when trained nurses screen alerts. Weak models create noise that pulls nurses away from patients. The ANA’s guidance keeps the focus on caring, accountability, and fair use of data. For RPM programs, AI is only as useful as the readings it receives and the nurses who act on them.
Tenovi provides the remote monitoring infrastructure that RPM software companies, care management companies, telehealth platforms, and health systems build on. That includes cellular-connected devices, the Tenovi Gateway, one API, and fulfillment, provisioning, and support. Contact us for a free demo and consultation to see how Tenovi can support your care teams.
