AI+ Nurse

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.

Course outline

1. 1

   Lesson 1: What is AI for Nurses?

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

   Lesson 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
3. 3

   Lesson 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
4. 4

   Lesson 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
5. 5

   Lesson 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
6. 6

   Lesson 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
7. 7

   Lesson 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
8. 8

   Lesson 8: Capstone Project

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