AI in Nursing: How Artificial Intelligence is Transforming Nursing Practice (2025)

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What Is AI in Nursing?

AI in nursing is the application of artificial intelligence technologies to support nursing workflows and clinical practice, encompassing ambient documentation that captures assessments automatically, predictive analytics for early detection of patient deterioration, intelligent medication verification systems, smart staffing and patient assignment algorithms, and clinical decision support tools—enabling nurses to reduce administrative burden by 30-40%, improve patient safety through earlier intervention, enhance job satisfaction, and redirect time toward therapeutic relationships and direct care delivery.

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How Does AI in Nursing Work?

AI nursing applications operate through specialized systems designed for nursing workflows:

1. Documentation Automation: AI listens to nurse-patient interactions during assessments and care delivery, automatically extracting clinical findings, vital signs, patient responses, and care activities—generating structured nursing notes that require only brief review, eliminating post-care charting burden.
2. Predictive Analytics: Machine learning models continuously analyze patient data including vital signs, lab results, medication administration, and nursing assessments—identifying patterns that predict deterioration, sepsis, falls, or other adverse events hours before traditional criteria trigger, enabling proactive nursing intervention.
3. Clinical Decision Support: AI systems provide context-aware alerts and recommendations during medication administration, care planning, and patient monitoring—filtering out irrelevant warnings while highlighting truly significant safety concerns based on individual patient factors and clinical context.
4. Workflow Optimization: Intelligent algorithms analyze patient acuity, nurse expertise, geographic efficiency, and continuity factors to optimize patient assignments—while predictive models forecast unit census and staffing needs, enabling proactive schedule adjustments that prevent understaffing crises.
5. Care Coordination: AI synthesizes information across shifts and providers to generate comprehensive handoff reports, care summaries, and transition documentation—ensuring critical information flows seamlessly across care transitions without requiring extensive manual compilation.
6. Real-Time Monitoring: Computer vision and sensor integration enable AI to continuously monitor patient status, detecting changes in mobility, agitation, or fall risk—alerting nurses to patients requiring immediate attention based on behavioral and physiological patterns.
7. Continuous Learning: AI nursing tools learn from usage patterns and outcomes, improving accuracy and relevance over time—adapting to unit-specific workflows, documentation preferences, and clinical priorities through ongoing refinement based on nurse feedback and validation.

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Introduction

Nurses face unprecedented challenges: staffing shortages, increasing patient acuity, and documentation requirements that consume hours of every shift. According to NSI Nursing Solutions 2024 National Healthcare Retention Report, the average hospital nursing turnover rate reached 18.4%, with each nurse departure costing approximately $52,000 in recruitment, onboarding, and lost productivity—creating a crisis that threatens care quality and organizational sustainability.

AI offers practical solutions to these challenges, automating routine tasks and providing decision support that enhances nursing care. Research from HIMSS 2024 shows that healthcare organizations implementing AI nursing tools report 30-40% reduction in documentation time, 25-30% improvement in nurse satisfaction scores, and 15-20% decrease in voluntary turnover—demonstrating measurable impact on both workforce wellbeing and financial performance.

Cause-effect relationship: When nurses gain 30-60 minutes per shift through AI documentation automation, this time redirected to direct patient care results in improved patient satisfaction scores (10-15% increase), reduced hospital-acquired conditions (15-20% decrease in falls and pressure injuries), and enhanced nurse retention—creating a positive cycle where better working conditions attract and retain qualified nursing staff (AACN 2024).

This guide explores how AI is transforming nursing practice with real-world examples, proven benefits, and practical guidance for nurses considering AI tools. Whether you’re a bedside nurse, nurse manager, or nursing informatics specialist, understanding AI’s role in nursing helps you leverage these tools to improve care and reduce burden.

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Understanding AI in Nursing

Current State of AI Adoption

AI adoption in nursing is accelerating rapidly. Healthcare organizations are implementing AI tools specifically designed for nursing workflows, recognizing that nurses represent the largest healthcare workforce and face significant documentation and administrative burden that contributes to the ongoing staffing crisis.

According to HIMSS 2024 Healthcare Technology Survey, 42% of hospitals have implemented or are actively piloting AI-powered nursing documentation tools, while 38% are deploying predictive analytics for patient monitoring—representing significant increases from just 18% and 22% respectively in 2022. This rapid adoption reflects both technological maturity and urgent workforce needs.

Adoption drivers include: Nursing shortage creating need for efficiency tools that extend nursing capacity, documentation burden contributing to burnout and turnover (cited by 68% of departing nurses as primary factor), patient safety focus requiring better early warning systems for deterioration detection, value-based care demanding improved outcomes tracking and preventive interventions, and technology advances making AI more accessible, accurate, and affordable for healthcare organizations.

While physician-focused AI tools have received more attention, nursing-specific applications are growing rapidly as organizations recognize the ROI of supporting the largest segment of their clinical workforce through intelligent automation.

Types of AI Tools for Nurses

Documentation AI automates nursing documentation including assessments, care plans, and handoff notes. These tools reduce time spent charting by 50-60% while improving documentation completeness—capturing clinical details that might otherwise be omitted during busy shifts.

Clinical decision support provides alerts, reminders, and recommendations to support nursing judgment. Examples include deterioration prediction models identifying patients at risk hours before clinical decline, medication safety alerts considering patient-specific factors, and care protocol guidance ensuring evidence-based practice implementation.

Workflow optimization uses AI for patient assignment, staffing prediction, and task prioritization—helping nurses and managers allocate resources effectively. Integration with Epic, Cerner, and other major EHR systems enables these tools to work seamlessly within existing workflows.

Patient monitoring employs AI to continuously analyze vital signs and other data, alerting nurses to concerning trends before they become critical—reducing response time to patient deterioration by 30-45 minutes on average (AACN 2024 study).

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AI Documentation for Nurses

Documentation consumes a significant portion of nursing time—studies suggest nurses spend 25-35% of their shift on documentation tasks, equating to 2-3 hours per 12-hour shift. According to NSI Nursing Solutions 2024, excessive documentation burden is cited as the primary driver of nurse dissatisfaction and turnover. AI documentation tools are transforming this burden.

Nursing Assessment Documentation

Ambient AI documentation can capture nursing assessments from natural conversation during patient interactions. Rather than completing checkboxes and flowsheets after care, nurses can document while providing care—maintaining focus on the patient rather than the computer.

How it works: AI listens as the nurse performs assessment and interacts with patient, extracts relevant clinical findings from conversation (vital signs, pain levels, mobility status, skin integrity, etc.), structures information into appropriate documentation format matching unit protocols, populates assessment flowsheets and narrative fields in the EHR system, and presents documentation for nurse review and signature within minutes of completing assessment.

Benefits for nursing assessment: 50-60% reduced time at computer workstations, more complete documentation of findings including patient-reported symptoms, better capture of psychosocial and emotional factors during conversation, documentation completed in real-time rather than end of shift when details fade, and improved presence during patient interactions leading to stronger therapeutic relationships.

Cause-effect: Real-time documentation capture during assessments leads to more accurate and complete nursing records, which results in better care coordination across shifts (20-30% reduction in information gaps during handoff), improved patient safety (15-20% fewer adverse events from missing information), and enhanced billing support for nursing-sensitive outcomes.

Care Plan Generation

AI can assist with care plan development by analyzing patient data and suggesting appropriate nursing diagnoses, interventions, and outcomes. This doesn’t replace nursing judgment but accelerates the care planning process—particularly valuable for new graduates or nurses floating to unfamiliar units.

AI care planning support includes: Suggested nursing diagnoses based on assessment data aligned with current clinical presentation, recommended evidence-based interventions from nursing research and best practices, outcome criteria aligned with patient goals and realistic timeframes, automatic updates as patient condition changes or goals are achieved, and integration with standardized nursing languages (NANDA-I, NIC, NOC) ensuring consistent terminology.

Organizations implementing AI care planning report 40-50% reduction in time spent developing and updating care plans, while improving plan quality and individualization to patient needs.

Shift Handoff Documentation

Effective handoff communication is critical for patient safety—The Joint Commission identifies communication failures during handoff as a leading cause of sentinel events. AI can generate comprehensive handoff reports by synthesizing information from the shift, ensuring nothing important is missed during transitions.

AI-assisted handoff includes: Automatic summary of significant events during shift (code blues, rapid responses, new orders, family meetings), current status of all active orders and pending tasks requiring follow-up, recent vital sign trends and concerning patterns with contextual significance, medication changes and administration status including PRN effectiveness, family communication updates and discharge planning progress, and anticipated needs for upcoming shift based on care trajectory.

This ensures consistent, thorough handoffs even during busy shifts when nurses might otherwise rush through report. Healthcare organizations report 35-40% reduction in handoff-related information gaps and 20-25% decrease in follow-up clarification questions after implementing AI handoff tools.

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AI Clinical Decision Support for Nurses

Early Warning Systems

AI-powered early warning systems analyze patient data continuously to predict deterioration before it becomes clinically obvious. These systems alert nurses to patients who may need intervention, enabling earlier response that significantly impacts outcomes.

Examples include: Sepsis prediction models that identify patients developing sepsis 4-8 hours before traditional criteria (qSOFA, SIRS) would trigger, enabling earlier antibiotic administration when most effective; cardiac arrest prediction alerting to patients at risk 6-12 hours in advance based on subtle vital sign patterns; respiratory failure early warning detecting declining oxygenation trends before critical hypoxemia; and fall risk prediction identifying patients needing enhanced precautions based on mobility patterns, medication effects, and cognitive status.

Unlike simple threshold alerts (heart rate over 100, blood pressure below 90), AI systems recognize complex patterns across multiple variables—reducing false alarms by 50-60% while catching 85-90% of deteriorating patients earlier than traditional monitoring (AACN 2024 Multi-Center Study).

Cause-effect: Earlier detection of patient deterioration through AI early warning systems leads to timely clinical interventions, which results in 20-30% reduction in ICU transfers from medical-surgical units, 15-25% decrease in rapid response team activations, and improved survival rates for conditions like sepsis where time-to-treatment directly impacts mortality.

Medication Safety

AI enhances medication safety beyond traditional alerts. Intelligent systems consider patient-specific factors to provide relevant warnings without alert fatigue—the phenomenon where nurses override alerts because most are clinically insignificant.

AI medication safety features: Context-aware drug interaction warnings considering patient’s full clinical picture (renal function, liver function, current symptoms), dosing recommendations based on weight, renal function, hepatic function, and age with automatic calculations, allergy cross-sensitivity alerts for related medications beyond simple drug class matching, timing optimization for medication administration considering drug half-lives and interaction windows, and high-alert medication verification support with double-check protocols and dosing range validation.

The key improvement over traditional systems is relevance—AI learns which alerts are clinically significant in specific contexts, reducing alert overrides from 90-95% (typical for basic systems) to 40-50% while maintaining or improving safety outcomes. This means nurses receive fewer, more meaningful alerts that warrant clinical attention.

Pressure Injury Prevention

AI can predict which patients are at highest risk for pressure injuries by analyzing factors beyond traditional Braden scores. This enables targeted prevention for patients most likely to benefit from intensive interventions.

Predictive models consider mobility patterns from continuous monitoring, perfusion status and hemodynamic stability, nutritional markers and protein status, length of stay projections and sedation requirements, and historical skin integrity patterns to identify high-risk patients. Healthcare organizations implementing AI pressure injury prediction report 30-40% reduction in hospital-acquired pressure injuries through targeted prevention protocols.

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AI Workflow Optimization

Patient Assignment

AI-powered patient assignment tools optimize nurse-patient matching based on multiple factors that impact care quality and nurse satisfaction: patient acuity across multiple dimensions (clinical complexity, cognitive status, physical needs), nurse experience levels and specialty certifications, current workload balance across the team, geographic efficiency minimizing walking distance, and continuity of care when therapeutically beneficial.

Benefits of AI assignment: More balanced workloads across nursing staff (20-30% reduction in workload variance), appropriate matching of complex patients to experienced nurses improving outcomes, reduced time for charge nurses to create assignments (from 30-45 minutes to 5-10 minutes), improved continuity when possible enhancing patient satisfaction, and consideration of real-time acuity changes enabling dynamic reassignment during shifts.

Hospitals using AI patient assignment report 15-20% improvement in nurse satisfaction with assignments and 10-15% reduction in patient safety events attributed to assignment mismatch.

Staffing Prediction

AI predicts patient volumes and acuity to support proactive staffing decisions. Rather than reacting to shortages, nurse managers can anticipate needs and adjust schedules 24-72 hours in advance—reducing crisis staffing and improving nurse work-life balance.

Predictive staffing considers: Historical patterns by day of week, season, and local events; scheduled admissions, surgeries, and procedures impacting unit census; current census and anticipated discharges based on length of stay patterns; regional factors like flu prevalence affecting admission rates; and special circumstances requiring adjusted staffing such as high-acuity patient influx.

Organizations using AI staffing prediction report 15-25% reduced overtime costs, 20-30% improvement in nurse-to-patient ratios during high-volume periods, and improved staff satisfaction from more predictable schedules and reduced last-minute call-ins (NSI Nursing Solutions 2024).

Task Prioritization

AI can help nurses prioritize competing demands by analyzing which tasks are most time-sensitive and which patients need attention first. This is particularly valuable during busy shifts when everything feels urgent and nurses must make rapid triage decisions.

Intelligent prioritization considers medication timing windows and critical administration times, overdue assessments and required documentation, pending critical lab results requiring follow-up, patient call requests and pain management needs, and clinical status changes flagged by monitoring systems—suggesting optimal task sequencing that balances urgency, efficiency, and patient safety.

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Benefits of AI for Nursing Practice

Time Savings

The most immediate benefit of nursing AI is time savings, particularly from documentation automation. According to HIMSS 2024 Nursing Technology Survey, nurses using AI documentation tools report recovering 30-60 minutes per shift—time that can be redirected to patient care, education, or even finishing shifts on time rather than staying late for charting.

Time saved through: Automated assessment documentation during care delivery (20-30 minutes per shift), AI-generated handoff reports eliminating manual compilation (10-15 minutes per handoff), streamlined care plan updates with intelligent suggestions (5-10 minutes per patient), reduced duplicate documentation through smart data population (10-15 minutes), and faster completion of routine charting tasks with predictive text and auto-population features.

For the average hospital nurse working three 12-hour shifts weekly, this represents 90-180 minutes of reclaimed time per week—equivalent to 78-156 hours annually that can be redirected toward therapeutic nursing activities, professional development, or work-life balance.

Reduced Documentation Burden

Beyond time savings, AI reduces the cognitive burden of documentation. Instead of remembering everything to chart later, nurses can focus on care knowing documentation is being captured in real-time. This reduces mental load, particularly during high-acuity situations requiring intense clinical focus.

Nurses report leaving shifts feeling less mentally exhausted when AI handles documentation, contributing to reduced burnout and improved job satisfaction. According to NSI Nursing Solutions 2024, organizations implementing comprehensive AI documentation solutions see 15-20% reduction in nurse turnover attributed to improved work environment and reduced administrative burden.

Cause-effect: Reducing documentation burden through AI automation decreases nurse stress and cognitive overload, which leads to improved clinical focus during patient care and reduced end-of-shift exhaustion—resulting in better retention rates (15-20% turnover reduction) that save healthcare organizations $780,000-$1,040,000 annually per 100 nurses at $52,000 per turnover.

Improved Patient Safety

AI contributes to patient safety through multiple mechanisms that complement nursing expertise: early warning systems catching deterioration 4-8 hours sooner enabling timely intervention, medication safety tools preventing errors through intelligent verification and dosing support, more complete documentation supporting care continuity across shifts and providers, reduced cognitive load allowing nurses to focus on clinical judgment rather than administrative tasks, and better handoff communication reducing information gaps that contribute to adverse events.

Organizations implementing nursing AI report measurable improvements in safety outcomes including 15-25% reduced falls (through enhanced risk prediction and targeted prevention), 20-30% fewer medication errors (via smart verification and dosing support), 25-35% earlier sepsis detection and treatment initiation, and 30-40% reduction in hospital-acquired pressure injuries through predictive risk stratification.

Enhanced Job Satisfaction

When administrative burden decreases, nurses can spend more time on the aspects of nursing that drew them to the profession—direct patient care, therapeutic relationships, health education, and clinical problem-solving rather than screen time and checkbox documentation.

Satisfaction improvements include: More time at bedside with patients building rapport (30-60 additional minutes per shift), less end-of-shift charting catch-up enabling on-time departure, reduced frustration with repetitive documentation and computer navigation, greater sense of providing quality nursing care and making meaningful impact, and improved work-life balance from finishing shifts on schedule without taking work home.

According to AACN 2024 Nurse Satisfaction Survey, nurses using AI documentation tools report 25-35% higher job satisfaction scores, 30-40% improvement in work-life balance ratings, and 40-50% reduced intent to leave nursing—critical metrics given the ongoing workforce crisis threatening healthcare sustainability.

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Challenges and Considerations

Integration with Nursing Workflow

AI tools must fit into existing nursing workflows to be successful. Solutions that require significant behavior change or add steps often fail to gain adoption, regardless of their potential benefits. Nurses are pragmatic—they’ll embrace tools that genuinely help but will route around those that create additional burden.

Successful implementations involve nurses in design and testing from the beginning, integrate seamlessly with existing EHR systems rather than requiring separate logins, minimize additional devices or hardware beyond what nurses already use, and provide clear, immediate value without adding cognitive burden or workflow complexity.

Trust and Clinical Judgment

AI should support nursing judgment, not replace it. Nurses need to understand what AI tools do and don’t do, maintaining appropriate oversight rather than blindly trusting AI outputs or becoming dependent on automated recommendations for clinical decisions.

Building trust requires transparency about how AI makes recommendations and predictions, comprehensive education about AI capabilities and limitations in clinical context, clear processes for overriding AI suggestions when clinical judgment differs, ongoing validation that AI is performing as expected across diverse patient populations, and organizational culture that values human expertise while embracing appropriate automation.

Privacy and Security

AI tools handling patient data must meet HIPAA requirements and organizational security standards. Nurses should understand how patient information is used and protected by AI systems they use, including data storage, encryption, access controls, and retention policies.

Reputable AI vendors provide business associate agreements, undergo regular security audits, maintain SOC 2 Type II certification, and demonstrate compliance with healthcare data protection regulations—essential requirements for any AI tool used in clinical practice.

Equity Considerations

AI systems can perpetuate or amplify biases present in training data, potentially leading to disparate care recommendations or risk predictions across patient demographics. Organizations must monitor AI tools for equitable performance across patient populations and address any disparities identified.

This includes validating AI accuracy across age groups, ethnicities, socioeconomic backgrounds, and clinical presentations—ensuring the technology serves all patients equitably rather than optimizing for majority populations while underserving vulnerable groups.

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Getting Started with AI in Nursing

For Individual Nurses

If your organization is implementing AI tools, engage with the process actively. Participate in training sessions, provide honest feedback during pilots identifying what works and what doesn’t, and advocate for tools that genuinely help nursing practice rather than adding complexity. Your frontline perspective is essential for successful implementation—vendors and administrators need to hear from bedside nurses about real-world usability.

Stay informed about AI developments in nursing through professional organizations like ANA and specialty nursing societies, continuing education opportunities, and nursing informatics resources. Understanding AI helps you evaluate new tools critically and contribute meaningfully to implementation decisions affecting your practice.

For Nurse Leaders

When evaluating AI tools, prioritize solutions that address real pain points identified by frontline nurses through surveys, focus groups, and turnover exit interviews. Documentation burden is often the highest-impact starting point—tools like AI medical scribes that reduce charting time show immediate, measurable benefits that build support for broader AI adoption.

Implementation success factors: Engage nurses in vendor evaluation and selection through demo participation and pilot feedback, pilot thoroughly before broad rollout with representative nurse champions from each shift, measure outcomes that matter to nursing practice (time savings, satisfaction, retention) not just technical metrics, provide adequate training and ongoing support with super-users on each unit, and iterate based on frontline feedback adjusting workflows and configurations to optimize usability.

Calculate ROI comprehensively including turnover reduction ($52,000 per nurse retained), overtime reduction (15-25% decrease typical), improved patient satisfaction (HCAHPS scores), and safety outcomes (reduced adverse events)—demonstrating value to justify investment and secure organizational commitment.

For Organizations

Include nursing leadership in AI strategy discussions from the beginning—not as an afterthought. Nursing-focused AI investments often show strong ROI through reduced turnover ($52,000 per nurse retained), improved safety outcomes (fewer falls, pressure injuries, medication errors), and enhanced patient satisfaction (HCAHPS scores)—but only if tools genuinely support nursing practice rather than imposing additional burden.

Align AI investments with nursing workforce strategy, recognizing that technology enabling nurses to practice at top of license while reducing burnout is essential for recruitment and retention in the current labor market crisis.

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Frequently Asked Questions

Will AI replace nurses?

No. AI will transform nursing practice but not replace nurses. Nursing requires human judgment, empathy, therapeutic presence, complex decision-making, and patient advocacy that AI cannot replicate. AI handles routine documentation tasks and provides decision support, enabling nurses to focus on the uniquely human aspects of care that require clinical expertise, emotional intelligence, and ethical reasoning. Studies consistently show AI augments nursing capabilities rather than replacing them.

How accurate is AI documentation for nursing?

Current AI documentation tools achieve 90-95% accuracy rates for nursing documentation, but nurse review remains essential. AI generates drafts that nurses verify and sign, maintaining professional accountability. Most nurses report making minor edits (correcting terminology, adding context) rather than substantial corrections. Accuracy improves as AI systems learn from nursing-specific language, unit workflows, and specialty terminology—often exceeding 95% after learning periods.

Do I need special training to use nursing AI tools?

Most nursing AI tools are designed for ease of use and require minimal training—typically 30-60 minute orientation sessions. Documentation AI often requires just a brief demonstration of how to activate recording and review generated notes. More complex clinical decision support tools may require understanding how to interpret AI recommendations and when to override them. Your organization should provide training appropriate to each tool with ongoing support from super-users and informatics staff.

What if AI makes a mistake that affects patient care?

Nurses remain responsible for clinical decisions and documentation accuracy regardless of how documentation is generated. AI provides support, but clinical judgment and oversight remain with the nurse—this is why review processes are essential before signing notes. AI suggestions should be verified against your clinical assessment before acting on them. Report AI errors through your organization’s channels (incident reporting, vendor feedback) so systems can be improved and patterns identified.

How do I advocate for AI tools at my organization?

Document the pain points AI could address through specific examples—time spent on documentation, safety concerns you’ve witnessed, workflow inefficiencies affecting care quality. Share research on AI benefits in nursing from peer-reviewed journals and professional organizations. Connect with nursing informatics colleagues and leadership to discuss opportunities. Propose pilots for specific high-impact applications with clear success metrics. Frame AI as a tool to support nursing practice, improve patient care, and address workforce challenges—emphasizing ROI through retention and safety improvements.

Does AI work for specialty nursing areas?

Yes. While early AI tools were general medical-surgical focused, specialty-specific AI is increasingly available for critical care, emergency nursing, perioperative nursing, and other specialties. These tools understand specialty terminology, workflows, and documentation requirements—generating ICU notes, surgical notes, emergency department documentation, and specialty assessments. Vendors increasingly customize AI for specialty needs through specialized training and configuration.

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Transform Nursing Documentation with AI

NoteV’s AI medical scribe works seamlessly for nursing practice—automatically capturing assessments, generating handoff reports, and reducing the documentation burden that keeps nurses from patients.

NoteV supports nursing practice:

• ✅ 50-60% reduction in documentation time—reclaim 30-60 minutes per shift
• ✅ Ambient documentation during patient interactions—no typing required
• ✅ Automated assessment capture and nursing note generation
• ✅ AI-generated shift handoff summaries ensuring complete information transfer
• ✅ Seamless integration with Epic, Cerner, athenahealth, and 40+ EHR systems
• ✅ More time for direct patient care and therapeutic relationships
• ✅ Improved work-life balance—finish shifts on time without charting at home

Join thousands of nurses who’ve transformed their practice and reclaimed their time with AI documentation.

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Related Resources

Continue exploring AI in healthcare:

• AI Healthcare Applications: AI in Healthcare Examples | Benefits of AI in Healthcare | Healthcare Automation | Document Automation
• AI Documentation: AI Medical Scribe Guide | Ambient AI Documentation | AI Medical Coding
• EHR Integration: What is an EHR? | AI-Enabled EHR Guide | Epic Integration | Cerner Integration
• Clinical Documentation: Progress Note Template | SOAP Note Template | Discharge Summary | ICU Note Template | Surgical Notes

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Disclaimer: This guide is provided for educational purposes. AI capabilities and implementations vary by vendor and healthcare setting. Nurses should follow their organization’s policies and maintain appropriate clinical oversight when using AI tools.

References: NSI Nursing Solutions National Healthcare Retention Report 2024 | HIMSS Healthcare Technology Survey 2024 | AACN Nurse Satisfaction Survey 2024 | American Nurses Association AI Position Statements | Joint Commission Sentinel Event Data 2024 | JAMA Network Open Nursing Documentation Studies 2024

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