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AI+ Nurse™

# 1102

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

$195.00

High-Quality Video, E-book & Audiobook

Modules Quizzes

AI Mentor

Access for Tablet & Phone

Online Proctored Exam with One Free Retake

Hands-on Practices

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

Exam Blueprint

Modules	Percentage
What is AI for Nurses?	8
AI for Documentation, Workflow, and Data Literacy	16
Predictive AI and Patient Safety	16
Generative AI and Nursing Education	15
Ethics, Safety, and Advocacy in AI Integration	15
Evaluating and Selecting AI Tools	15
Implementing AI and Leading Change on the Unit	15

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.

Hands-on

Practical experience through real-world exercises, case studies, and applied learning.

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 Will You Learn?

Certification Modules

Module 1 - What is AI for Nurses?

1.1 Understanding AI Basics in a Nursing Context

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 and Nursing Education

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

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