AI+ Nurse™

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

At a Glance: Course + Exam Overview

Category	AI Professional AI Specialization AI Healthcare All Courses English Language Nurse Practitioner
Program Name	AI+ Nurse™
Duration	Instructor-Led: 1 Day Self-Paced: 8 hours of content
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.
Exam Format	50 questions, 70% passing, 90 Minutes

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.

Certification 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

Industry Opportunities

AI Nursing Practice Consultant
Guide hospitals and care facilities in adopting AI tools to improve patient monitoring, workflow efficiency, and quality of care.
Clinical AI Nursing Coordinator
Manage the implementation of AI-powered nursing systems to streamline daily tasks, minimize errors, and improve patient safety.
AI Patient Care Data Specialist
Utilize AI models to interpret nursing and patient care data, predict patient needs, and support evidence-based nursing practices.
Healthcare Operations AI Manager
Lead initiatives to integrate AI in nursing operations, optimizing resource allocation and enhancing patient-care delivery systems.
Chief Nursing AI Officer (CNAIO)
Direct organizational AI adoption in nursing, driving innovation, workforce empowerment, and patient-centered digital transformation.

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.

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.

Exam Details

Duration

90 Minutes

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

Exam Blueprint

What is AI for Nurses?	7%
AI for Documentation, Workflow, and Data Literacy	15%
Predictive AI and Patient Safety	15%
Generative AI in Nursing	15%
Ethics, Safety, and Advocacy in AI Integration	12%
Evaluating and Selecting AI Tools	12%
Implementing AI and Leading Change on the Unit	12%
Capstone Project - Designing a Personal AI-in-Nursing Impact Plan	12%

Course Price: USD $190.00

Self-Paced Online

Purchase Self-Paced Course

Instructor-Led (Live Virtual/Classroom)

Core AI Tools Covered

Python

Scikit-learn

Keras

Jupyter Notebooks

Matplotlib

Power BI