Course Overview
TOPThe AI+ Nurse™ empowers nurses to integrate artificial intelligence into clinical workflows, patient care, and healthcare management. Explore AI-driven tools for patient monitoring, diagnostics, and treatment support. The course enhances understanding of how AI improves healthcare efficiency, accuracy, and decision-making. It bridges nursing expertise with emerging healthcare technologies for better patient outcomes.
Scheduled Classes
TOP| 01/22/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 02/19/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 03/19/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 04/16/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 05/21/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 06/18/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOP- Understand AI applications in nursing and patient care
- Use AI tools for clinical decision support, diagnostics, and patient monitoring
- Apply AI for improving healthcare workflow efficiency and treatment accuracy
- Evaluate ethical, legal, and privacy aspects of AI in nursing practice
- Integrate AI-driven solutions to enhance patient experience and care delivery
Outline
TOPWhat is AI for Nurses?
- Understanding AI Basics in a Nursing Context
- 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
AI for Documentation, Workflow, and Data Literacy
- Introduction to Natural Language Processing
- Workflow Automation: Transforming Nursing Practice
- Beginner’s Guide to Data Literacy in Nursing
- Legal & Compliance Basics in Nursing AI Documentation
- Case Study: Integrating AI and Workflow Automation at Massachusetts General Hospital (MGH)
- Hands-On Exercise: Using the ChatGPT Registered Nurse Tool in Clinical Documentation and Patient Education
Predictive AI and Patient Safety
- Understanding Predictive Models
- Alert Fatigue and Trust
- Simulation Activity: Responding to Real-Time Deterioration Alerts
- Collaborating Across Teams
- Bias in Predictions
- Case Study
- Hands-on Activity: Interpreting Predictive Alerts with ChatGPT
Generative AI and Nursing Education
- Introduction to Generative AI in Nursing
- Large Language Models (LLMs) for Nurses
- Creating Patient Education Materials with AI
- Ensuring Safe and Ethical Use of AI
- Case Study
- Hands-On Activity: Exploring AI-Powered Differential Diagnosis with Symptoma
Ethics, Safety, and Advocacy in AI Integration
- Bias, Fairness, and Inclusion
- Informed Consent and Transparency
- Nurse Advocacy and Professional Responsibilities
- Creating an Ethics Checklist
- Stakeholder Feedback Techniques
- Legal and Regulatory Considerations
- Psychological and Social Implications
- Case Study: Addressing Racial Bias in Healthcare Algorithms (Optum Algorithm Case).
- Hands-on: Uncovering Bias in Diabetes Risk Prediction: A Fairness Audit Using Aequitas
Evaluating and Selecting AI Tools
- Understanding Performance Metrics
- Vendor Red Flags
- Nurse Role in Selection
- Evaluation Templates and Checklists
- Use Cases: AI in Clinical Decision-Making
- Case Study: Using AI to Enhance Real-Time Clinical Decision-Making at UAB Medicine with MIC Sickbay
- Hands-on: Evaluating AI Diagnostic Model Performance Using Confusion Matrix Metrics
Implementing AI and Leading Change on the Unit
- Building Buy-In: Promoting AI as an Ally, Not a Competitor
- Change Management Essentials
- Creating an AI Playbook: A Comprehensive Roadmap for Sustainable Success
- Monitoring Quality Improvement: Leveraging AI Metrics for Continuous Enhancement
- Error Reporting and Safety Protocols: Ensuring Safe and Reliable AI Integration
- Hands-On Activity: Calculating Clinical Risk Scores and Visualization with ChatGPT
Prerequisites
TOPRequired
- Understanding of clinical practices and patient care
- Experience with electronic health records and medical devices
- Understanding data analysis and interpretation in healthcare
- Basic AI and Machine Learning Knowledge of algorithms and predictive modeling
- Ability to make data-driven healthcare decisions
Who Should Attend
TOP- Healthcare Professionals
