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AI+ Nurse Practitioner™

AI+ Nurse Practitioner™
  • 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
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Certificate Code

AP 1102

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Basic nursing knowledge, Familiarity with healthcare technology, Critical thinking, Foundational AI and ML concepts, Problem solving skills
50 questions, 70% passing, 90 minutes, online proctored exam

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

AI Tools Covered

Python
Scikit-learn
Keras
Jupyter Notebooks
Matplotlib
Power BI
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