AI+ Nurse™ – Course Outline
Program Overview
AI+ Nurse™ is a forward-looking program designed to equip nursing professionals with the knowledge and practical skills to integrate artificial intelligence (AI) into modern healthcare environments. The course explores how AI enhances clinical decision-making, patient monitoring, diagnostics, and administrative efficiency while maintaining ethical, safe, and patient-centered care.
Participants will develop a strong foundation in AI concepts, healthcare data systems, and real-world applications such as predictive analytics, intelligent triage, and digital health technologies. Emphasis is placed on responsible AI use, data privacy, and regulatory compliance within clinical practice. By the end of the program, learners will be prepared to collaborate effectively with interdisciplinary teams, utilize AI-powered tools, and contribute to improved patient outcomes and healthcare innovation.
Course Objectives
- Understand core AI concepts and their relevance to nursing
- Apply AI tools in clinical workflows and patient care
- Interpret AI-generated insights to support decision-making
- Ensure ethical, legal, and safe use of AI technologies
- Improve patient outcomes through data-driven approaches
Assessment Methods
- Quizzes and knowledge assessments
- Case study evaluations
- Practical exercises and simulations
- Capstone project and presentation
Target Audience
- Registered Nurses (RNs)
- Nursing students
- Allied healthcare professionals
- Clinical and healthcare administrators
Course Duration
- Instructor-Led: 1 day (live or virtual)
- Self-Paced: 8 hours of content
Certification
Participants will be awarded an AI+ Nurse™ Certification upon successful completion.
Course Modules
Module 1: Introduction to AI in Nursing
- Fundamentals of Artificial Intelligence in Healthcare
- Applications of AI in Nursing Practice
- Case Study: AI-Driven Patient Safety and Efficiency
- Practical: AI-Based Clinical Data Visualization in Postoperative Care
Module 2: AI for Documentation, Workflow & Data Literacy
- Introduction to Natural Language Processing (NLP)
- Workflow Automation in Nursing Practice
- Fundamentals of Data Literacy for Nurses
- Legal and Compliance Considerations in AI Documentation
- Case Study: AI Integration in Hospital Workflow Systems
- Practical: AI-Assisted Clinical Documentation and Patient Education
Module 3: Predictive AI and Patient Safety
- Introduction to Predictive Analytics in Healthcare
- Managing Alert Fatigue and Building Trust
- Simulation: Responding to Patient Deterioration Alerts
- Interdisciplinary Collaboration with AI Systems
- Understanding Bias in Predictive Models
- Case Study Analysis
- Practical: Interpreting Predictive Alerts
Module 4: Generative AI in Nursing
- Overview of Generative AI Applications
- Large Language Models (LLMs) in Clinical Practice
- Developing Patient Education Materials Using AI
- Ethical and Safe Use of Generative AI
- Case Study
- Practical: AI-Assisted Clinical Decision Support
Module 5: Ethics, Safety, and Advocacy
- Bias, Fairness, and Inclusion in AI
- Informed Consent and Transparency
- Nursing Advocacy in AI Adoption
- Developing an Ethical AI Checklist
- Stakeholder Engagement and Feedback
- Legal and Regulatory Frameworks
- Psychological and Social Implications of AI
- Case Study: Bias in Healthcare Algorithms
- Practical: Conducting a Fairness Audit
Module 6: Evaluating and Selecting AI Tools
- Understanding AI Performance Metrics
- Identifying Vendor Risks and Red Flags
- Role of Nurses in Technology Selection
- Evaluation Frameworks and Checklists
- Use Cases in Clinical Decision-Making
- Case Study: AI in Real-Time Clinical Monitoring
- Practical: Evaluating Model Performance
Module 7: Implementation and Leadership in AI
- Building Acceptance and Trust in AI Systems
- Change Management in Healthcare Settings
- Developing an AI Implementation Roadmap
- Monitoring Quality Improvement with AI Metrics
- Safety Protocols and Error Reporting
- Practical: Clinical Risk Analysis and Visualization
Module 8: Capstone Project
- Design and present a Personal AI-in-Nursing Impact Plan, demonstrating practical application of AI in a clinical or operational setting.
