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AI Professional

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

All prices are in NZD, ex GST (15%).

At a Glance: Course + Exam Overview

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

🤝 You’re never on your own — Parasol Concierge Support provides integration guidance and expert VA assistance so you can focus on mastering skills while we handle the setup details.

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

  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

  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

  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

  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

  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

  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

  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

  1. 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%

Instructor-Led (Live Virtual/Classroom)

Request Virtual Training

TECHNOLOGIES USED

Python
Python
Scikit-learn
Scikit-learn
Keras
Keras
Jupyter Notebooks
Jupyter Notebooks
Matplotlib
Matplotlib
Power BI
Power BI