AI+ Nurse™

Target group

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.

Course structure

Module 1: What is AI for Nurses?

1. 1.1 What is AI for Nurses?
2. 1.2 Where AI Shows Up in Nursing
3. 1.3 Case Study: Improving Patient Safety and Nursing Efficiency with AI at Riverside Medical Center
4. 1.4 Hands-on: Using Nurse AI for Clinical Data Visualization in Postoperative Nursing Care

Module 2: AI for Documentation, Workflow, and Data Literacy

1. 2.1 Introduction to Natural Language Processing
2. 2.2 Workflow Automation: Transforming Nursing Practice
3. 2.3 Beginner’s Guide to Data Literacy in Nursing
4. 2.4 Legal & Compliance Basics in Nursing AI Documentation
5. 2.5 Case Study: Integrating AI and Workflow Automation at Massachusetts General Hospital (MGH)
6. 2.6 Hands-On Exercise: Using the ChatGPT Registered Nurse Tool in Clinical Documentation and Patient Education

Module 3: Predictive AI and Patient Safety

1. 3.1 Understanding Predictive Models
2. 3.2 Alert Fatigue and Trust
3. 3.3 Simulation Activity: Responding to Real-Time Deterioration Alerts
4. 3.4 Collaborating Across Teams
5. 3.5 Bias in Predictions
6. 3.6 Case Study
7. 3.7 Hands-on Activity: Interpreting Predictive Alerts with ChatGPT

Module 4: Generative AI in Nursing

1. 4.1 Introduction to Generative AI in Nursing
2. 4.2 Large Language Models (LLMs) for Nurses
3. 4.3 Creating Patient Education Materials with AI
4. 4.4 Ensuring Safe and Ethical Use of AI
5. 4.5 Case Study
6. 4.6 Hands-On Activity: Exploring AI-Powered Differential Diagnosis with Symptoma

Module 5: Ethics, Safety, and Advocacy in AI Integration

1. 5.1 Bias, Fairness, and Inclusion
2. 5.2 Informed Consent and Transparency
3. 5.3 Nurse Advocacy and Professional Responsibilities
4. 5.4 Creating an Ethics Checklist
5. 5.5 Stakeholder Feedback Techniques
6. 5.6 Legal and Regulatory Considerations
7. 5.7 Psychological and Social Implications
8. 5.8 Case Study: Addressing Racial Bias in Healthcare Algorithms (Optum Algorithm Case).
9. 5.9 Hands-on: Uncovering Bias in Diabetes Risk Prediction: A Fairness Audit Using Aequitas

Module 6: Evaluating and Selecting AI Tools

1. 6.1 Understanding Performance Metrics
2. 6.2 Vendor Red Flags
3. 6.3 Nurse Role in Selection
4. 6.4 Evaluation Templates and Checklists
5. 6.5 Use Cases: AI in Clinical Decision-Making
6. 6.6 Case Study: Using AI to Enhance Real-Time Clinical Decision-Making at UAB Medicine with MIC Sickbay
7. 6.7 Hands-on: Evaluating AI Diagnostic Model Performance Using Confusion Matrix Metrics

Module 7: Implementing AI and Leading Change on the Unit

1. 7.1 Building Buy-In: Promoting AI as an Ally, Not a Competitor
2. 7.2 Change Management Essentials
3. 7.3 Creating an AI Playbook: A Comprehensive Roadmap for Sustainable Success
4. 7.4 Monitoring Quality Improvement: Leveraging AI Metrics for Continuous Enhancement
5. 7.5 Error Reporting and Safety Protocols: Ensuring Safe and Reliable AI Integration
6. 7.6 Hands-On Activity: Calculating Clinical Risk Scores and Visualization with ChatGPT

Module 8: Capstone Project

1. 1. Capstone Project – Designing a Personal AI-in-Nursing Impact Plan

Certification

50 questions, 70% passing, 90 minutes, online proctored exam