AI+ Nurse

Hours: 8 / Access Length: 12 Months / Delivery: Online, Self-Paced
Online Hours: 8
Retail Price: $195.00

Course Overview:

Designed to help nursing professionals leverage patient-centric AI for enhanced outcomes, this course provides a comprehensive foundation in everything from AI fundamentals to real-world healthcare applications. Participants will gain practical insights to support data-driven clinical and operational decisions, ensuring they can navigate modern medical technology with confidence. Ultimately, this program empowers nurses to achieve clinical excellence by seamlessly integrating AI tools into their daily practice to improve the standard of patient care.

Recommended 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.

Course Outline:

Lesson 1: What is AI for Nurses?

• What is AI for Nurses?
• Where AI Shows Up in Nursing
• Case Study: Improving Patient Safety and Nursing Efficiency with AI at Riverside Medical Center
• Hands-on: Using Nurse AI for Clinical Data Visualization in Postoperative Nursing Care

Lesson 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

Lesson 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

Lesson 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

Lesson 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

Lesson 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

Lesson 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

Lesson 8: Capstone Project

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

All necessary course materials are included.

System Requirements:

Internet Connectivity Requirements:

• Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.

NOTE: While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system.  This is due to the potential high download and upload latency of cellular connections.   Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.

Hardware Requirements:

• CPU: 1 GHz or higher
• RAM: 4 GB or higher
• Resolution: 1280 x 720 or higher.  1920x1080 resolution is recommended for the best experience.
• Speakers / Headphones
• Microphone for Webinar or Live Online sessions.

Operating System Requirements:

• Windows 7 or higher.
• Mac OSX 10 or higher.
• Latest Chrome OS
• Latest Linux Distributions
  

NOTE: While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.

Web Browser Requirements:

• Latest Google Chrome is recommended for the best experience.
• Latest Mozilla FireFox
• Latest Microsoft Edge
• Latest Apple Safari

Basic Software Requirements (These are recommendations of software to use):

• Office suite software (Microsoft Office, OpenOffice, or LibreOffice)
• PDF reader program (Adobe Reader, FoxIt)
• Courses may require other software that is described in the above course outline.