AI+ Nurse™

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

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

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