AI+ Nurse™
AP 1102
Blending Human Touch with AI Intelligence- Patient-Centric AI Care: Designed for nurses to leverage AI for enhanced patient outcomes
- Data-Driven Decisions: Provides practical insights for informed clinical and operational choices
- Comprehensive AI Understanding: Covers AI fundamentals to real-world healthcare applications
- Clinical Excellence with AI: Empowers nurses to confidently integrate AI into daily healthcare practice
Why This Certification Matters
At a Glance: Course + Exam Overview
- Instructor-Led: 1 day (live or virtual)
- Self-Paced: 8 hours of content
Who Should Enroll?
Registered Nurses (RNs): Professionals seeking to integrate AI into daily patient care and clinical decision-making.
Nursing Students: Learners aiming to build future-ready skills in AI-driven healthcare practices.
Healthcare Administrators: Individuals looking to optimize nursing workflows and enhance patient care outcomes.
Clinical Informatics Specialists: Experts interested in applying AI to electronic health records and patient data analysis.
Nurse Educators & Trainers: Professionals preparing the next generation of nurses with AI-powered healthcare knowledge.
Job Roles & Industry Outlook
AI Nursing Practice Consultant
Guide hospitals and care facilities in adopting AI tools to improve patient monitoring, workflow efficiency, and quality of care.
Clinical AI Nursing Coordinator
Manage the implementation of AI-powered nursing systems to streamline daily tasks, minimize errors, and improve patient safety.
AI Patient Care Data Specialist
Utilize AI models to interpret nursing and patient care data, predict patient needs, and support evidence-based nursing practices.
Healthcare Operations AI Manager
Lead initiatives to integrate AI in nursing operations, optimizing resource allocation and enhancing patient-care delivery systems.
Chief Nursing AI Officer (CNAIO)
Direct organizational AI adoption in nursing, driving innovation, workforce empowerment, and patient-centered digital transformation.
Industry Growth: Advancing Nursing Practices with AI-Driven Solutions
- The AI‑Driven Virtual Nursing Assistants market is projected to rise from $1.41 billion in 2024 to $1.76 billion in 2025, demonstrating a compound annual growth rate (CAGR) of 24.5%. (Source: Business Research Company)
- Nurses will increasingly adopt AI to offload routine tasks and devote more time to direct patient care.
- Healthcare institutions are investing in AI-driven nursing tools to boost efficiency and reduce errors.
- Demand for nurses with AI and data skills is rising as hospitals shift toward predictive, personalized care models.
- AI integration in nursing education is accelerating, preparing future nurses for advanced, tech-enabled roles.
Skills You’ll Gain
- AI in Nursing Practice
- Workflow Automation
- Clinical Data Literacy
- Predictive Modeling for Patient Safety
- Generative AI in Nursing Education
- AI-Powered Documentation
- Real-Time Clinical Decision Support
- Bias and Fairness in Healthcare AI
- Evaluation of AI Tools
- Change Management and Leadership in AI Integration
What You'll Learn
- 1.1 What is AI for Nurses?
- 1.2 Where AI Shows Up in Nursing
- 1.3 Case Study: Improving Patient Safety and Nursing Efficiency with AI at Riverside Medical Center
- 1.4 Hands-on: Using Nurse AI for Clinical Data Visualization in Postoperative Nursing Care
- 2.1 Introduction to Natural Language Processing
- 2.2 Workflow Automation: Transforming Nursing Practice
- 2.3 Beginner’s Guide to Data Literacy in Nursing
- 2.4 Legal & Compliance Basics in Nursing AI Documentation
- 2.5 Case Study: Integrating AI and Workflow Automation at Massachusetts General Hospital (MGH)
- 2.6 Hands-On Exercise: Using the ChatGPT Registered Nurse Tool in Clinical Documentation and Patient Education
- 3.1 Understanding Predictive Models
- 3.2 Alert Fatigue and Trust
- 3.3 Simulation Activity: Responding to Real-Time Deterioration Alerts
- 3.4 Collaborating Across Teams
- 3.5 Bias in Predictions
- 3.6 Case Study
- 3.7 Hands-on Activity: Interpreting Predictive Alerts with ChatGPT
- 4.1 Introduction to Generative AI in Nursing
- 4.2 Large Language Models (LLMs) for Nurses
- 4.3 Creating Patient Education Materials with AI
- 4.4 Ensuring Safe and Ethical Use of AI
- 4.5 Case Study
- 4.6 Hands-On Activity: Exploring AI-Powered Differential Diagnosis with Symptoma
- 5.1 Bias, Fairness, and Inclusion
- 5.2 Informed Consent and Transparency
- 5.3 Nurse Advocacy and Professional Responsibilities
- 5.4 Creating an Ethics Checklist
- 5.5 Stakeholder Feedback Techniques
- 5.6 Legal and Regulatory Considerations
- 5.7 Psychological and Social Implications
- 5.8 Case Study: Addressing Racial Bias in Healthcare Algorithms (Optum Algorithm Case).
- 5.9 Hands-on: Uncovering Bias in Diabetes Risk Prediction: A Fairness Audit Using Aequitas
- 6.1 Understanding Performance Metrics
- 6.2 Vendor Red Flags
- 6.3 Nurse Role in Selection
- 6.4 Evaluation Templates and Checklists
- 6.5 Use Cases: AI in Clinical Decision-Making
- 6.6 Case Study: Using AI to Enhance Real-Time Clinical Decision-Making at UAB Medicine with MIC Sickbay
- 6.7 Hands-on: Evaluating AI Diagnostic Model Performance Using Confusion Matrix Metrics
- 7.1 Building Buy-In: Promoting AI as an Ally, Not a Competitor
- 7.2 Change Management Essentials
- 7.3 Creating an AI Playbook: A Comprehensive Roadmap for Sustainable Success
- 7.4 Monitoring Quality Improvement: Leveraging AI Metrics for Continuous Enhancement
- 7.5 Error Reporting and Safety Protocols: Ensuring Safe and Reliable AI Integration
- 7.6 Hands-On Activity: Calculating Clinical Risk Scores and Visualization with ChatGPT
- 1. Capstone Project – Designing a Personal AI-in-Nursing Impact Plan
Tools You'll Explore
Python
Scikit-learn
Keras
Jupyter Notebooks
Matplotlib
Power BI
Prerequisites
- Basic Nursing Knowledge: Understanding of clinical practices and patient care.
- Familiarity with Healthcare Technology: Experience with electronic health records and medical devices.
- Introduction to Data Science: Understanding data analysis and interpretation in healthcare.
- Basic AI and Machine Learning Concepts: Knowledge of algorithms and predictive modeling.
- Critical Thinking and Problem Solving: Ability to make data-driven healthcare decisions.
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via proctored exam platform (flexible scheduling)
Exam Blueprint
- What is AI for Nurses? – 7%
- AI for Documentation, Workflow, and Data Literacy – 15%
- Predictive AI and Patient Safety – 15%
- Generative AI in Nursing – 15%
- Ethics, Safety, and Advocacy in AI Integration – 12%
- Evaluating and Selecting AI Tools – 12%
- Implementing AI and Leading Change on the Unit – 12%
- Capstone Project – Designing a Personal AI-in-Nursing Impact Plan - 12%
Choose the Format That Fits Your Schedule
What’s Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
Instructor-Led (Live Virtual/Classroom)
- 1 day of intensive training with live demos
- Real-time Q&A, peer collaboration, and hands-on
- Led by AI Certified Trainers and delivered through Authorized Training Partners
Self-Paced Online
- ~8 hours of on-demand video lessons, e-book, podcasts, and interactive hands-on
- Learn anywhere, anytime, with modular quizzes to track progress
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Get CertifiedFrequently Asked Questions
Yes, you’ll gain practical skills through nursing-focused case studies and projects, ready to apply AI tools in patient care.
It combines nursing practice with hands-on AI training, focusing on workflow efficiency, patient monitoring, and care delivery.
You’ll work on AI-powered patient monitoring, EHR documentation, predictive alerts, and workflow optimization tailored to nursing.
The course blends expert-led lessons, interactive modules, and case-based nursing simulations for strong practical learning.
It builds in-demand AI nursing skills with real-world projects and prepares you for roles in AI-driven healthcare.