AI+ Nurse™ eLearning

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

Module 1: What is AI for Nurses?

• 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

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

• 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

Module 3: Predictive AI and Patient Safety

• 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

Module 4: Generative AI in Nursing

• 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

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

• 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

Module 6: Evaluating and Selecting AI Tools

• 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

Module 7: Implementing AI and Leading Change on the Unit

• 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

Module 8: Capstone Project

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

Tools you will explore

• Python
• Scikit-learn
• Keras
• Jupyter Notebooks
• Matplotlib
• Power BI

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Self study materials are available for 365 days.

Instructor-led OR Self-paced course + Official exam + Digital badge