AI in Healthcare Policy and Innovation #254011

Upon completion of this course, participants will be able to:

• Understand the Fundamentals: Define key AI concepts (Machine Learning, Deep Learning, NLP, GenAI) and their current and prospective applications in healthcare.

• Navigate the Regulatory Landscape: Analyze the existing and emerging policy and regulatory frameworks for AI in healthcare across different jurisdictions (e.g., FDA, EU AI Act, WHO guidelines).

• Evaluate Ethical Considerations: Identify and critically assess core ethical issues, including algorithmic bias, health equity, data privacy (HIPAA/GDPR), transparency, and accountability.

• Develop Implementation Strategies: Formulate strategies for the successful integration, validation, and scaling of AI solutions within clinical workflows and health systems.

• Foster Innovation: Understand the innovation lifecycle, from ideation and funding to procurement and commercialization, within the constraints of healthcare policy.

• Communicate Effectively: Articulate the opportunities, risks, and requirements of AI in healthcare to diverse audiences, including clinicians, executives, policymakers, and patients.

This course is designed for a multidisciplinary audience involved in shaping, managing, and implementing healthcare services and technology:

• Healthcare Policy Makers & Regulators: Government officials, public servants, and agency staff responsible for health technology assessment and policy development.

• Healthcare Executives & Administrators: Hospital CEOs, CTOs, CIOs, and department heads making strategic decisions about technology adoption and investment.

• Clinicians & Medical Researchers: Doctors, nurses, and researchers who interact with AI tools and want to understand their implications for practice and patient care.

• Health Insurance & Payer Professionals: Individuals involved in reimbursement strategies, health economics, and outcomes research for new AI-driven interventions.

• Health Tech Entrepreneurs & Investors: Founders, developers, and VCs in the digital health space who need to navigate the complex healthcare regulatory environment.

• Medical Legal & Ethics Professionals: Lawyers, ethicists, and compliance officers specializing in healthcare technology and data governance.

• Patient Advocacy Group Leaders: Representatives who wish to understand how AI will impact patient rights, access, and care quality.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of AI in Healthcare

• AM: The AI Revolution: From Hype to Reality

  • Key Concepts: AI, ML, Deep Learning, Generative AI.

  • Overview of AI Applications: Medical imaging, drug discovery, predictive analytics, robotic surgery, virtual health assistants, administrative automation.

  • Case Study: Deep dive into a successful AI diagnostic tool.

• PM: The Data Ecosystem

  • The Fuel of AI: Electronic Health Records (EHRs), genomic data, wearables, and IoT.

  • Data Governance, Privacy, and Security: HIPAA, GDPR, and de-identification techniques.

  • Workshop: Evaluating a dataset for bias and usability.

Day 2: The Policy and Regulatory Framework

• AM: Navigating the Regulatory Maze

  • Software as a Medical Device (SaMD): FDA approval processes (510(k), De Novo, Pre-Cert).

  • International Perspectives: EU AI Act (Risk-Based Approach), Health Canada, and other global regulators.

  • Reimbursement Strategies: CPT codes, value-based pricing, and demonstrating ROI to payers.

• PM: Policy for Public Health

  • The role of public health agencies (CDC, WHO) in guiding AI use.

  • National AI Strategies and their impact on healthcare innovation.

  • Group Discussion: Drafting a policy brief for a specific AI application.

Day 3: Ethics, Equity, and Responsible Innovation

• AM: The Algorithm is Not Neutral

  • Understanding Algorithmic Bias: Sources, real-world examples, and mitigation strategies.

  • Ensuring Health Equity: Preventing AI from exacerbating existing disparities.

  • Transparency and Explainability (XAI): The “black box” problem and why it matters in medicine.

• PM: Accountability and Human-in-the-Loop

  • Legal Liability: Who is responsible when an AI system fails?

  • Informed Consent in the Age of AI.

  • Workshop: Conducting an ethical risk assessment for an AI tool.

Day 4: Implementation and Scaling Innovation

• AM: From Pilot to Practice

  • Integration into Clinical Workflows: Change management and clinician buy-in.

  • Validating and Monitoring AI Performance: Continuous evaluation and drift detection.

  • Interoperability Challenges: Making AI tools work with existing EHR systems.

• PM: The Business of AI in Healthcare

  • Procurement Strategies for Health Systems.

  • Funding and Venture Capital Landscape.

  • Case Study: A health system executive presents a real-world implementation challenge.

Day 5: The Future and Strategic Leadership

• AM: Emerging Trends and Frontiers

  • Generative AI in Clinical Documentation and Patient Communication.

  • AI for Pandemic Preparedness and Public Health Surveillance.

  • The Future of AI-Driven Personalized Medicine.

• PM: Capstone Session: Developing an AI Strategy

  • Participants work in cross-functional teams (e.g., a clinician, an administrator, a policy maker) to develop a comprehensive proposal for adopting a specific AI solution.

  • Team presentations and feedback from course instructors.

  • Course Wrap-Up: Key Takeaways and Building a Professional Network.