By the end of this course, delegates will be able to:
- Grasp the core principles of Artificial Intelligence, understanding its transformative role in healthcare and the strategic value it brings to nursing leadership.
- Apply AI concepts to nursing leadership, identifying real-world opportunities to leverage AI for clinical decision-making, operational efficiency, and improved patient outcomes.
- Critically assess case studies, analyzing both successful and unsuccessful AI implementations to draw actionable lessons for future planning.
- Navigate ethical, legal, and data governance challenges, ensuring responsible, compliant, and patient-centered integration of AI in clinical settings.
- Design strategic AI integration plans, including staff development, change management processes, and measurable impact evaluation tools.
- Strengthen leadership readiness for the digital age, by exploring emerging AI trends, cultivating a forward-thinking digital culture, and promoting collaboration with IT and innovation stakeholders.
DAY 1- Foundations of AI in Healthcare
Understanding AI Basics & Healthcare Relevance
- What is AI? ML, NLP, Predictive Analytics overview
- History and evolution of AI in healthcare
- Relevance of AI to nursing leadership roles
- Global trends: AI innovations in hospitals and care delivery
Activity:Group discussion on current AI perceptions in participants’ institution
DAY 2 – Practical Applications of AI in Nursing
Clinical and Operational Use Cases
- AI-powered patient monitoring and early warning systems
- Predictive analytics in patient outcomes
- Virtual nursing assistants and chatbots
- Staffing optimization and workflow automation
Activity:Case review of AI-enabled hospital units
DAY 3 – Case Studies and Implementation Lessons
Success Stories and Barriers
- Case Study 1: AI for fall risk prediction
- Case Study 2: AI-driven decision support in ICU
- Case Study 3: Population health management via AI
- Success factors and common pitfalls
Activity:Group project – “Diagnose the failure” scenario analysis
DAY 4 – Ethics, Privacy & Risk
Leading Ethically in an AI World
- Ethical challenges: bias, fairness, transparency
- AI and patient consent – what changes?
- Legal frameworks and data governance (GDPR, HIPAA)
- The role of nurse leaders in safeguarding ethics
Activity:Interactive ethics simulation (role-play a data breach crisis)
DAY 5 – Strategic Planning for AI Integration
From Vision to Execution
- Developing an AI roadmap for nursing departments
- Selecting and evaluating AI tools
- Budgeting, partnerships, and vendor evaluation
- KPIs for AI performance
Activity:Create an AI strategy blueprint for your organization
DAY 6 – Leading Change & Building Engagement
Preparing the Workforce for AI
- Change management principles in digital transformation
- Building a culture of innovation
- Communication strategies and training for nurses
- Handling resistance and ensuring inclusivity
Activity:Workshop: Designing a staff engagement plan
DAY 7 – Future Trends & Executive Readiness
Staying Ahead in an AI-driven Future
- The future of nursing with AI and automation
- AI literacy for nurse executives
- Collaborating with data scientists & IT
- Self-assessment: AI readiness diagnostic
Activity:Panel discussion with healthcare AI experts + Personal Action Plan
