Program Objectives:

• Enable healthcare leaders to drive AI innovation responsibly

• Bridge the gap between data science and legal compliance

• Mitigate risks of privacy violations in AI-powered environments

• Support creation of transparent and auditable AI workflows

• Promote long-term digital trust in healthcare institutions

What you will learn?

Week 1: Foundations of HIPAA and AI in Healthcare

Module 1: HIPAA Essentials for Decision-Makers

• Chapter 1.1: Overview of HIPAA Regulations and Structure

• Chapter 1.2: Key Definitions: PHI, Covered Entities, Business Associates

• Chapter 1.3: Privacy Rule vs. Security Rule

Module 2: Introduction to AI in Healthcare

• Chapter 2.1: What is AI? Types of AI in Healthcare

• Chapter 2.2: Use Cases – Diagnostics, Workflow Automation, Predictive Analytics

• Chapter 2.3: Intersections of AI and Protected Health Information (PHI)

Week 2: Compliance and Risk Management in AI-Enabled Systems

Module 3: HIPAA Compliance Challenges with AI

• Chapter 3.1: Data Collection, Storage, and Consent

• Chapter 3.2: De-identification and Re-identification Risks

• Chapter 3.3: AI Model Training and Data Sharing Compliance

Module 4: Risk Assessment and Governance

• Chapter 4.1: Performing a HIPAA Risk Analysis with AI Systems

• Chapter 4.2: Role-Based Access, Audit Trails, and Monitoring

• Chapter 4.3: Vendor Management and Business Associate Agreements (BAAs)

Week 3: Strategic Integration and Leadership Planning

Module 5: Implementing AI in HIPAA-Compliant Workflows

• Chapter 5.1: AI Vendor Vetting and Evaluation

• Chapter 5.2: Policy Frameworks and Staff Training

• Chapter 5.3: Incident Response and Breach Reporting Procedures

Module 6: Future-Proofing Healthcare with AI

• Chapter 6.1: Policy Trends – ONC, HHS, EU AI Act Implications

• Chapter 6.2: AI Explainability and Ethical Considerations

• Chapter 6.3: Building a Leadership Strategy for AI in Healthcare