Agentic AI in Healthcare Training explores how autonomous AI agents can support healthcare operations, clinical workflows, and administrative processes while maintaining safety, compliance, and ethical standards. This course explains how agentic AI systems can plan, reason, and act within healthcare environments to improve efficiency, decision support, and patient experience without replacing clinical judgment.

• Understand the role of Agentic AI in healthcare operations and clinical workflows
• Develop AI agents for healthcare data processing and decision support
• Integrate healthcare datasets, APIs, and AI-driven automation workflows
• Apply AI agents for patient engagement, reporting, and administrative automation
• Understand ethical, privacy, and compliance considerations in healthcare AI
• Explore real-world healthcare AI use cases and implementation strategies

• Healthcare professionals and administrators
• Health IT and digital health teams
• Hospital operations and compliance teams
• Healthcare consultants and solution architects
• Professionals exploring AI in healthcare

• Basic understanding of artificial intelligence concepts
• Familiarity with healthcare workflows or systems
• No programming or advanced AI background required

• Understanding agentic AI applications in healthcare
• Designing healthcare-focused AI agent workflows
• Applying agentic AI to clinical and administrative use cases
• Managing risk, compliance, and data privacy
• Evaluating ethical and responsible AI adoption

By the end of this training, participants will be able to understand, design, and evaluate agentic AI solutions for healthcare environments. Learners will gain the ability to identify suitable healthcare use cases, design compliant AI agent workflows, and apply agentic AI responsibly to support healthcare delivery and operations.

Introduction to Agentic AI in Healthcare
• What is agentic AI and why it matters in healthcare
• Differences between traditional healthcare AI and agentic systems
• Opportunities and limitations in clinical environments

Healthcare Data, Context, and Constraints
• Structured and unstructured healthcare data
• Clinical context awareness and decision boundaries
• Avoiding over-automation in patient care

Designing Healthcare AI Agents
• Defining agent roles and objectives
• Clinical vs administrative agent responsibilities
• Safety-first agent design principles

Healthcare Use Cases and Scenarios
• Patient scheduling and care coordination
• Clinical documentation and summarization
• Operational and administrative automation

Agentic AI for Clinical Decision Support
• Supporting clinicians without replacing judgment
• Information retrieval and guideline assistance
• Reducing cognitive load for healthcare staff

Compliance, Ethics, and Regulatory Considerations
• Patient privacy and data security
• Bias, accuracy, and explainability
• Responsible AI governance in healthcare

Human-in-the-Loop Healthcare Agents
• Clinician oversight and approval workflows
• Escalation and exception handling
• Trust and accountability mechanisms

Hands-on Case Studies and Simulations
• Real-world healthcare scenarios
• Designing safe agentic AI workflows
• Participant exercises and feedback

• Fundamentals of Agentic AI in healthcare
• Healthcare workflow automation use cases
• AI agent integration with healthcare systems and datasets
• Prompt engineering and decision-support workflows
• Data privacy, ethics, and compliance considerations
• Practical healthcare AI agent implementation exercise

• Participation in case study exercises
• Healthcare agent workflow design assignment
• Knowledge-based assessment

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Agentic AI in Healthcare Training, recognizing their understanding of safe and responsible AI agent applications in healthcare.