AI+ Nurse™ - eLearning (exam included)
275,00 EUR
- 15 hours
AI+ Nurse™ is a role-based certification designed to empower nursing professionals, healthcare educators, and clinical informatics specialists with skills to integrate AI into nursing practice. The course bridges nursing fundamentals with AI capabilities to enhance patient monitoring, decision support, workflow automation, and data-driven nursing care.
Key Features
Language
Course and material in English
Level
Beginner level
Access
1 year access to the platform 24/7
6 hours of video lessons & multimedia
15 hours of study time recommendation
Material
Video, PDF Material, audio eBook, Podcasts, quizzes and assessments.
Tools You’ll Explore
Python, Scikit-learn, Keras, Jupyter Notebooks, Matplotlib, PowerBI
Exam
Online Proctored Exam with One Free Retake
Certificate
Certification of completion included
Why This Course Matters
Enable nurses to use AI tools to improve patient outcomes, early warning systems, and proactive care
Learning Outcomes
At the end of this course, you will be able to:
Integrate AI tools
into nursing workflows to enhance patient monitoring, diagnostics, and care coordination.
Apply data-driven insights
to make informed clinical and operational decisions that improve patient outcomes.
Understand AI fundamentals
including machine learning, predictive analytics, and natural language processing (NLP) in a healthcare context.
Leverage AI in patient management
for tasks such as early risk detection, triage, and personalized care planning.
Collaborate effectively
with AI systems and interdisciplinary teams to optimize care delivery.
Evaluate and implement AI tools
safely within clinical settings, ensuring accuracy, transparency, and reliability.
Address ethical, legal, and regulatory considerations
related to AI in healthcare, including data privacy and bias mitigation.
Lead innovation initiatives
by promoting AI adoption and digital transformation in nursing practice.
Course timeline
Understanding AI for Doctors
Lesson 1
- Explore how AI is transforming modern nursing practice.
- Discover where and how AI is applied in patient care and hospital operations.
- Analyze a case study on improving patient safety and nursing efficiency at Riverside Medical Center.
- Hands-on activity: Use Nurse AI tools for clinical data visualization in postoperative care.
AI for Documentation, Workflow, and Data Literacy
Lesson 2
- Learn the basics of Natural Language Processing (NLP) and its use in healthcare documentation.
- Understand workflow automation and how AI enhances nursing efficiency.
- Build data literacy skills to interpret and manage clinical information effectively.
- Explore the legal and compliance aspects of AI documentation in nursing.
- Case study: Integrating AI workflow automation at Massachusetts General Hospital (MGH).
- Hands-on: Use the ChatGPT RN tool for clinical documentation and patient education.
Predictive AI and Patient Safety
Lesson 3
- Understand how predictive models support early risk detection and patient monitoring.
- Examine the challenges of alert fatigue and establishing trust in AI systems.
- Participate in simulations to respond to real-time deterioration alerts.
- Learn how interdisciplinary teams collaborate around AI insights.
- Discuss bias in predictive algorithms and its impact on patient outcomes.
- Hands-on: Interpret predictive alerts with ChatGPT for clinical decision support.
Generative AI and Nursing Education
Lesson 4
- Get introduced to Generative AI and its role in modern nursing.
- Understand how large language models (LLMs) support nursing tasks and education.
- Create patient education materials using AI tools.
- Learn safe and ethical practices for AI use in healthcare.
- Case study and hands-on: Use AI-powered tools like Symptoma for differential diagnosis.
Ethics, Safety, and Advocacy in AI Integration
Lesson 5
- Explore fairness, inclusion, and bias mitigation in AI systems.
- Understand transparency, informed consent, and ethical communication with patients.
- Strengthen advocacy skills and uphold professional responsibilities in AI use.
- Develop an ethics checklist for responsible AI adoption.
- Study legal, regulatory, and social implications of AI in healthcare.
- Case study: Addressing racial bias in healthcare algorithms (Optum case).
- Hands-on: Conduct a fairness audit using Aequitas for diabetes risk prediction.
Evaluating and Selecting AI Tools
Lesson 6
- Learn to interpret AI performance metrics for clinical reliability.
- Identify vendor red flags and questions to ask before adopting AI solutions.
- Understand the nurse’s role in evaluating and selecting AI technologies.
- Use evaluation templates and checklists for decision-making.
- Case study: Enhancing real-time clinical decisions at UAB Medicine with MIC Sickbay.
- Hands-on: Evaluate AI diagnostic performance using confusion matrix metrics.
Implementing AI and Leading Change on the Unit
Lesson 7
- Build team buy-in and position AI as a supportive tool, not a replacement.
- Master change management strategies for AI adoption.
- Create an AI playbook for sustainable, scalable implementation.
- Monitor quality improvement through AI-driven performance metrics.
- Develop error reporting and safety protocols for AI-integrated workflows.
- Hands-on: Calculate and visualize clinical risk scores with ChatGPT.
Blending Compassion with Intelligent Care
- AI-Enhanced Patient Care: Empower nurses to harness AI tools that improve patient outcomes and safety.
- Informed Decision-Making: Develop the ability to use data-driven insights for smarter clinical and operational choices.
- Holistic AI Knowledge: Gain a full understanding of AI — from core concepts to practical applications in healthcare.
- AI-Driven Clinical Excellence: Equip nursing professionals to confidently apply AI in everyday patient care and clinical workflows.
Who Should Enroll in this Program?
Registered nurses wanting to bring AI into patient care
Nursing students aiming for future-ready skills
Healthcare administrators seeking to optimize nursing operations
Clinical informatics specialists working with EHRs & data analytics
Nurse educators preparing curriculum for AI-informed nursing practice
More Details
Pre-requisites
- Fundamental knowledge of medical concepts, clinical workflows, and patient care
- Awareness of healthcare systems and familiarity with electronic health records (EHRs)
- Basic understanding of data handling, statistics, or medical research
- An openness to learning AI concepts and tools in a clinical context
Exam Details
- Duration: 90 minutes
- Passing :70% (35/50)
- Format: 50 multiple-choice/multiple-response questions
- Delivery Method: Online via proctored exam platform (flexible scheduling)
- Language: English
Licensing and accreditation
This course is offered by AVC according to Partner Program Agreement and complies with the License Agreement requirements.
Equity Policy
AVC does not provide accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.
Frequently Asked Questions
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