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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
Enroll Now
AI+ Nurse™
Self-Paced Online
USD $ 195.00
Instructor-Led Online
USD $ 1110.00

At a Glance: Course + Exam Overview

Our training approach is human‑centred and outcomes‑driven. We focus on what learners can apply confidently.

Category
AI Healthcare
Nurse Practitioner
AI Professional
AI Specialization
All Courses
English
Language
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
Exam details not available.

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. 1.1 What is AI for Nurses?
  2. 1.2 Where AI Shows Up in Nursing
  3. 1.3 Case Study: Improving Patient Safety and Nursing Efficiency with AI at Riverside Medical Center
  4. 1.4 Hands-on: Using Nurse AI for Clinical Data Visualization in Postoperative Nursing Care

Module 2: AI for Documentation, Workflow, and Data Literacy

  1. 2.1 Introduction to Natural Language Processing
  2. 2.2 Workflow Automation: Transforming Nursing Practice
  3. 2.3 Beginner’s Guide to Data Literacy in Nursing
  4. 2.4 Legal & Compliance Basics in Nursing AI Documentation
  5. 2.5 Case Study: Integrating AI and Workflow Automation at Massachusetts General Hospital (MGH)
  6. 2.6 Hands-On Exercise: Using the ChatGPT Registered Nurse Tool in Clinical Documentation and Patient Education

Module 3: Predictive AI and Patient Safety

  1. 3.1 Understanding Predictive Models
  2. 3.2 Alert Fatigue and Trust
  3. 3.3 Simulation Activity: Responding to Real-Time Deterioration Alerts
  4. 3.4 Collaborating Across Teams
  5. 3.5 Bias in Predictions
  6. 3.6 Case Study
  7. 3.7 Hands-on Activity: Interpreting Predictive Alerts with ChatGPT

Module 4: Generative AI in Nursing

  1. 4.1 Introduction to Generative AI in Nursing
  2. 4.2 Large Language Models (LLMs) for Nurses
  3. 4.3 Creating Patient Education Materials with AI
  4. 4.4 Ensuring Safe and Ethical Use of AI
  5. 4.5 Case Study
  6. 4.6 Hands-On Activity: Exploring AI-Powered Differential Diagnosis with Symptoma

Module 5: Ethics, Safety, and Advocacy in AI Integration

  1. 5.1 Bias, Fairness, and Inclusion
  2. 5.2 Informed Consent and Transparency
  3. 5.3 Nurse Advocacy and Professional Responsibilities
  4. 5.4 Creating an Ethics Checklist
  5. 5.5 Stakeholder Feedback Techniques
  6. 5.6 Legal and Regulatory Considerations
  7. 5.7 Psychological and Social Implications
  8. 5.8 Case Study: Addressing Racial Bias in Healthcare Algorithms (Optum Algorithm Case).
  9. 5.9 Hands-on: Uncovering Bias in Diabetes Risk Prediction: A Fairness Audit Using Aequitas

Module 6: Evaluating and Selecting AI Tools

  1. 6.1 Understanding Performance Metrics
  2. 6.2 Vendor Red Flags
  3. 6.3 Nurse Role in Selection
  4. 6.4 Evaluation Templates and Checklists
  5. 6.5 Use Cases: AI in Clinical Decision-Making
  6. 6.6 Case Study: Using AI to Enhance Real-Time Clinical Decision-Making at UAB Medicine with MIC Sickbay
  7. 6.7 Hands-on: Evaluating AI Diagnostic Model Performance Using Confusion Matrix Metrics

Module 7: Implementing AI and Leading Change on the Unit

  1. 7.1 Building Buy-In: Promoting AI as an Ally, Not a Competitor
  2. 7.2 Change Management Essentials
  3. 7.3 Creating an AI Playbook: A Comprehensive Roadmap for Sustainable Success
  4. 7.4 Monitoring Quality Improvement: Leveraging AI Metrics for Continuous Enhancement
  5. 7.5 Error Reporting and Safety Protocols: Ensuring Safe and Reliable AI Integration
  6. 7.6 Hands-On Activity: Calculating Clinical Risk Scores and Visualization with ChatGPT

Module 8: Capstone Project

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

Finish the course and get certified

certificate

Industry Opportunities

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AI Nursing Practice Consultant

Guide hospitals and care facilities in adopting AI tools to improve patient monitoring, workflow efficiency, and quality of care.

Opportunity Image

Clinical AI Nursing Coordinator

Manage the implementation of AI-powered nursing systems to streamline daily tasks, minimize errors, and improve patient safety.

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AI Patient Care Data Specialist

Utilize AI models to interpret nursing and patient care data, predict patient needs, and support evidence-based nursing practices.

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Healthcare Operations AI Manager

Lead initiatives to integrate AI in nursing operations, optimizing resource allocation and enhancing patient-care delivery systems.

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Chief Nursing AI Officer (CNAIO)

Direct organizational AI adoption in nursing, driving innovation, workforce empowerment, and patient-centered digital transformation.

Frequently Asked Questions

Can I apply what I learn in this course to real-world scenarios immediately?
Yes, you’ll gain practical skills through nursing-focused case studies and projects, ready to apply AI tools in patient care.
What makes this course different from other Healthcare and AI courses?
It combines nursing practice with hands-on AI training, focusing on workflow efficiency, patient monitoring, and care delivery.
What type of projects will I work on?
You’ll work on AI-powered patient monitoring, EHR documentation, predictive alerts, and workflow optimization tailored to nursing.
How is the course structured to ensure I actually learn the skills?
The course blends expert-led lessons, interactive modules, and case-based nursing simulations for strong practical learning.
How does this course prepare me for the job market?
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

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%
Self-Paced Online

Self-Paced: 8 hours of content

USD $ 195.00
Purchase Self-Paced Course
Instructor-Led Online

Instructor-Led: 1 day (live or virtual)

USD $ 1110.00
Purchase Instructor-Led Course

Core AI Tools Covered

Python

Python

Scikit-learn

Scikit-learn

Keras

Keras

Jupyter Notebooks

Jupyter Notebooks

Matplotlib

Matplotlib

Power BI

Power BI