Future-proof your Nursing Career with 

"AI in Healthcare"!

You’re joining a course designed to be as flexible as you are, so whether you’re on your way to work, out for a walk, or fitting in a quick workout, you can easily learn on the go. This course explores the role of artificial intelligence in healthcare, from practical applications in patient care to its broader impact on the healthcare system. Think of it as your personal podcast-style journey through one of the most transformative fields in modern medicine. Created by nurses for Nurses!

Each lesson includes a downloadable PDF with additional details, perfect for those moments when you want to dig a little deeper. You’ll also have the chance to test your understanding with optional quizzes to reinforce key concepts. And while the course is free, for those looking to add a credential to their resume, there’s an affordable, verifiable e-certificate option available at the end.

So, let’s dive in!

Course Objectives

  1. Knowledge Objectives:

    • Equip healthcare professionals with actionable and foundational knowledge about Artificial Intelligence.

    • Emphasize AI's clinical, operational, and leadership applications.

    • Demonstrate how AI can augment roles across the spectrum of nursing—from bedside care to decision-making processes.

    • Provide understanding of basic AI frameworks.

    • Develop practical skills in utilizing AI tools for daily tasks.

    • Explore how AI impacts healthcare at systemic levels.

    • Enable healthcare workers to translate AI concepts into practice fluently.

    • Help understand the potential and limitations of AI within their settings.

  2. Perspective Objectives:

    • Challenge and expand participants' perspectives about AI.

    • Navigate both opportunities and concerns arising from AI adoption.

    • Encourage viewing AI as an enabler of human capacity, not a replacement.

    • Highlight how AI allows for better care, more time for empathy, and innovative solutions to challenges.

    • Teach participants to recognize and mitigate biases.

    • Foster critical thinking about ethical AI implementation.

    • Encourage proactive advocacy for technology that serves patient and caregiver needs.

    • Foster a global and diverse perspective on healthcare technology access.

    • Emphasize the importance of inclusive AI development.

  3. Values Objectives:

    • Strengthen participants' grounding in core healthcare values—empathy, ethics, collaboration, and patient-centered care.

    • Ensure technology aligns with principles of humanity, transparency, and safety.

    • Encourage approaching AI through an ethical lens.

    • Advocate for fairness, data privacy, and inclusivity in AI practices.

    • Nurture compassion as a fundamental part of caregiving.

    • Emphasize AI as a tool to amplify human values, not erode them.

    • Maintain commitment to health equity through responsible AI use.

  4. Integration and Action Objectives:

    • Blend knowledge, broadened perspectives, and strong values into actionable initiatives.

    • Equip participants to understand and adopt AI in their professional roles.

    • Prepare participants to lead others through AI transitions.

    • Develop AI-literate leaders who can inspire and guide teams.

    • Encourage thoughtful implementation of AI.

    • Promote ownership of technological changes while preserving compassionate patient care.

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PDF 1

Basic AI in Healthcare

PDF 2

How AI Empowers the Nursing Process

PDF 3

AI Applications in Healthcare

PDF 4

Navigating Ethical AI Issues in Healthcare

Glossary of 30 Common AI in Healthcare Terms and Concepts

  1. Artificial Intelligence (AI): The simulation of human intelligence in machines, enabling them to perform tasks that typically require human cognition, such as learning, problem-solving, and decision-making.

  2. Machine Learning (ML): A subset of AI focused on creating algorithms that enable machines to learn from and make predictions based on data.

  3. Deep Learning: A type of machine learning that uses neural networks with multiple layers to analyze complex data, often used in imaging and speech recognition.

  4. Neural Networks: A series of algorithms that mimic the human brain, used to recognize patterns in data for various AI applications.

  5. Natural Language Processing (NLP): A branch of AI that enables computers to understand, interpret, and generate human language.

  6. Predictive Analytics: The use of AI to analyze current and historical data to make predictions about future outcomes, often used for patient risk assessment.

  7. Electronic Health Records (EHR): Digital versions of patients' paper charts that store medical histories, treatments, and diagnoses, often enhanced by AI for better data management.

  8. Computer Vision: An AI technology that allows computers to interpret and make decisions based on visual data, commonly used in medical imaging.

  9. Robotics: AI-driven machines that can perform complex tasks in healthcare, such as surgery or physical rehabilitation.

  10. Chatbots: AI programs designed to simulate conversation with users, often used in healthcare for patient engagement and answering common medical questions.

  11. Virtual Health Assistants: AI-powered tools that provide health-related information, reminders, and support to patients.

  12. Data Privacy: The protection of patient data to ensure confidentiality and compliance with regulations like HIPAA or GDPR.

  13. Explainable AI (XAI): AI systems designed to provide transparency in their decision-making process, making it easier for healthcare professionals to understand and trust AI outputs.

  14. Interoperability: The ability of different healthcare systems and technologies to communicate and exchange data effectively, critical for AI integration.

  15. FHIR (Fast Healthcare Interoperability Resources): A standard for exchanging healthcare information electronically, helping to improve interoperability in AI applications.

  16. Bias in AI: The presence of prejudiced or skewed outcomes due to biased data used to train AI models, which can lead to disparities in healthcare.

  17. Ethical AI: The practice of developing and deploying AI in a way that ensures fairness, accountability, and transparency, particularly important in healthcare.

  18. Clinical Decision Support System (CDSS): AI-based tools that assist healthcare professionals in making evidence-based clinical decisions.

  19. Remote Patient Monitoring (RPM): The use of AI and connected devices to monitor patient health metrics outside traditional clinical settings.

  20. Telemedicine: The use of technology, including AI, to provide healthcare services remotely, often enhancing accessibility and efficiency.

  21. Algorithm: A set of rules or instructions that an AI system follows to solve a problem or make a decision.

  22. Big Data: Extremely large datasets that can be analyzed by AI to reveal patterns, trends, and associations, especially relating to human health.

  23. Wearable Devices: Electronic devices worn by patients to monitor health metrics in real time, often integrated with AI for data analysis.

  24. Workflow Automation: The use of AI to automate repetitive administrative tasks, allowing healthcare professionals to focus more on patient care.

  25. Sentiment Analysis: An NLP technique used to determine the sentiment or emotional tone of text, which can be applied to patient feedback and healthcare communication.

  26. Image Recognition: The use of AI to identify objects, patterns, or features in images, often used in radiology to detect abnormalities.

  27. Data Silos: Isolated data storage systems that do not communicate with each other, hindering AI's ability to access comprehensive information for analysis.

  28. Reinforcement Learning: A type of machine learning in which an algorithm learns by interacting with an environment to maximize a reward, applicable in optimizing healthcare workflows.

  29. Human-in-the-Loop (HITL): A system design where human judgment is integrated into AI processes to ensure safety, reliability, and patient-centered care.

  30. Palliative Care AI Tools: AI systems designed to assist in end-of-life care by predicting symptoms and optimizing interventions to improve patient comfort.

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