AI+ Nurse™

Blending Human Touch with AI Intelligence

This course is designed around patient-centric AI care, enabling nurses to leverage AI technologies to enhance patient outcomes. It develops data-driven decision-making skills by providing practical insights for informed clinical and operational choices. Learners gain a comprehensive understanding of AI, covering foundational concepts through to real-world healthcare applications. With a focus on clinical excellence, the course empowers nurses to confidently integrate AI into their daily healthcare practice.

Enrollment Fee: 

MUR 5,980

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AI+ Nurse™

What You'll Learn

1.1 What is AI for Nurses?
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

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

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

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

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

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

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

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

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% (35/50)

Format:

50 multiple-choice/multiple-response questions

Delivery Method:

Online via proctored exam platform (flexible scheduling)

Unlock Self-Paced Online Learning

  • Unlock Self-Paced Online Learning
  • Access learning anytime, anywhere, with built-in quizzes to measure progress.
  • Enrollment Fee: MUR 5,980
Enroll Now