What’s Included?
High-Quality Video, E-book & Audiobook
Modules Quizzes
AI Mentor
Access for Tablet & Phone
Online Proctored Exam with One Free Retake
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.
Skills You’ll Gain
- AI in Nursing Practice
- Workflow Automation
- Clinical Data Literacy
- Predictive Modeling for Patient Safety
- Generative AI in Nursing Education
- AI-Powered Documentation
- Real-Time Clinical Decision Support
- Bias and Fairness in Healthcare AI
- Evaluation of AI Tools
- Change Management and Leadership in AI Integration
Self Study Materials Included
Additional Resources
Supplementary references and list of tools to deepen knowledge and practical application.
Audiobooks
Listen and learn anytime with convenient audio-based knowledge sharing.
E-Books
Comprehensive digital guides offering in-depth knowledge and learning support.
Module Wise Quizzes
Interactive assessments to reinforce learning and test conceptual clarity.
Podcasts
Insightful audio sessions featuring expert discussions and real-world cases.
Videos
Engaging visual content to enhance understanding and learning experience.
Tools You’ll Master
Python
Scikit-learn
Keras
Jupyter Notebooks
Matplotlib
Power BI
What You’ll Learn
AI Fundamentals for Nursing
Gain essential knowledge of artificial intelligence technologies and their application in nursing practice.
Enhancing Patient Care with AI
Learn how AI can optimize workflows and improve decision-making to enhance patient care.
Data Analytics and Machine Learning
Understand the role of data analytics and machine learning in clinical settings to drive better outcomes.
Ethical Considerations in AI
Explore ethical challenges and considerations when leveraging AI tools in nursing to ensure responsible use and patient well-being.
Course Modules
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
Frequently Asked Questions
Yes, you’ll gain practical skills through nursing-focused case studies and projects, ready to apply AI tools in patient care.
It combines nursing practice with hands-on AI training, focusing on workflow efficiency, patient monitoring, and care delivery.
You’ll work on AI-powered patient monitoring, EHR documentation, predictive alerts, and workflow optimization tailored to nursing.
The course blends expert-led lessons, interactive modules, and case-based nursing simulations for strong practical learning.
It builds in-demand AI nursing skills with real-world projects and prepares you for roles in AI-driven healthcare.