Recommended 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:
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
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
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
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
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
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
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
Lesson 8: Capstone Project
- Capstone Project – Designing a Personal AI-in-Nursing Impact Plan
