AI+ Nurse Practitioner™
Formerly known as AI+ Nurse™ Blending Human Touch with AI Intelligence
Delivered by UpskillNexus, an AI CERTs® Authorized Training Partner.
About this certification
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
What's included
Package
Duration
- Instructor-Led: 1 day (live or virtual)
- Self-Paced: 8 hours of content
Exam format
Prerequisites
Course modules
Module 1: AI for Nurses
- 1.1 Understanding AI Basics in a Nursing Context
- 1.2 Where AI Shows Up in Nursing
- 1.3 AI Risks Nurses Must Recognize
- 1.4 AI as a Nursing Support Tool
Module 2: AI for Documentation, Workflow, and Data Literacy
- 2.1 AI in Nursing Documentation
- 2.2 Workflow Automation in Nursing Practice
- 2.3 Beginner’s Guide to Data Literacy in Nursing
- 2.4 Data Integrity and Documentation Safety
- 2.5 Communication and Translation Support
- 2.6 Real-World Case Studies: Documentation AI in Practice
Module 3: Predictive AI and Patient Safety
- 3.1 Understanding Predictive AI in Healthcare
- 3.2 Evaluating Alerts and Model Performance
- 3.3 Human-in-the-Loop Clinical Decision-Making
- 3.4 Interdisciplinary Response and Handoff Support
- 3.5 Bias and Equity in Predictive Models
- 3.6 Real-World Case Studies: Predictive AI in Practice
- 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 Safe Use of Generative AI
- 4.3 Patient Education and Communication Materials
- 4.4 Multilingual and Accessible Communication
- 4.5 Clinical Use Boundaries for Generative AI
- 4.6 Real-World Case Studies: Generative AI in Practice
Module 5: Ethics, Safety, and Advocacy in AI Integration
- 5.1 Bias, Fairness, and Inclusion
- 5.2 Informed Consent and Transparency
- 5.3 Privacy, Security, and Confidentiality
- 5.4 Regulatory Literacy for Nurses
- 5.5 Professional Responsibility and Accountability
Module 6: Evaluating and Selecting AI Tools
- 6.1 Understanding Performance Metrics
- 6.2 Predictive Tools vs. Generative Tools
- 6.3 Vendor Red Flags
- 6.4 The Nurse Practitioner’s Role in Tool Selection
Module 7: Implementing AI and Leading Change on the Unit
- 7.1 Building Buy-In
- 7.2 Change Management Essentials
- 7.3 Creating an AI Playbook: A Comprehensive Roadmap for Sustainable Success
- 7.4 Monitoring Quality Improvement
- 7.5 Error Reporting and Safety Protocols
- 7.6 Real-World Case Studies in AI Implementation and Change Leadership
Module 8: Capstone Project: Designing a Personal AI in Nursing Impact Plan
- 8.1 Capstone Project – Designing a Personal AI-in-Nursing Impact Plan
Optional Module: AI Agents for AI+ Nurses
- 1. What Are AI Agents?
- 2. How Does an AI Agent Work in Healthcare?
- 3. Core Characteristics of AI Agents
- 4. Importance of AI Agents (General + Nursing)
- 5. Significance of AI Agents in Nursing
- 6. Types of AI Agents?
- 7. Applications and Trends in Nursing
- 8. Case Study – AI Agents for Nursing Workflow & Sepsis
- 9. Hands-On Lab
Why this certification matters
AI in Patient Care:
Clinical Decision Support:
Workflow Optimization:
Ethical and Human-Centered Care:
Practical Simulations:
Who should enrol
Registered Nurses (RNs): Professionals seeking to integrate AI into daily patient care and clinical decision-making.
Nursing Students: Learners aiming to build future-ready skills in AI-driven healthcare practices.
Healthcare Administrators: Individuals looking to optimize nursing workflows and enhance patient care outcomes.
Clinical Informatics Specialists: Experts interested in applying AI to electronic health records and patient data analysis.
Nurse Educators & Trainers: Professionals preparing the next generation of nurses with AI-powered healthcare knowledge.
Tools you'll work with
Python
Scikit-learn
Keras
Jupyter Notebooks
Matplotlib
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