Healthcare AI Ethics and Governance Practice Course

Overview

Healthcare AI Ethics and Governance Practice Course is a five-day intermediate course for healthcare leaders, clinicians, health-data professionals, compliance teams, and AI project owners, who leave with a Healthcare AI Ethics Review Pack. Participants examine patient autonomy, consent, privacy, bias, explainability, accountability, validation, and lifecycle oversight through practical review tools. The course helps multidisciplinary teams make defensible decisions about AI use in care. Agile Leaders Training Center provides training in healthcare AI ethics and governance practice.

Who Should Attend

• Teams responsible for approving or overseeing AI use in patient care
• Teams responsible for clinical quality, safety, and risk decisions
• Teams responsible for health data access, consent, and privacy controls
• Teams responsible for procuring, validating, or monitoring AI solutions
• Teams responsible for ethics, compliance, assurance, and incident escalation

The course assumes participants contribute to healthcare technology decisions and leaves out model coding, medical diagnosis, legal advice, certification, and engineering implementation.

Departments and Industries

The course supports ethical AI governance across healthcare delivery, public health, research, insurance, life sciences, and digital health.

• Clinical governance, quality, and patient safety
• Health information, data governance, and cybersecurity
• Compliance, risk, legal liaison, and internal audit
• Digital health, innovation, procurement, and project management
• Hospitals, laboratories, insurers, research organizations, and technology providers

Learning Objectives

By the end of this course, participants will be able to:

• Analyze healthcare AI use cases for ethical risks and affected stakeholders
• Apply autonomy, consent, privacy, equity, and safety principles
• Evaluate bias, explainability, validation, and human oversight evidence
• Build an accountability matrix for decisions, incidents, and escalation
• Prioritize controls across procurement, deployment, monitoring, and change
• Use a Healthcare AI Ethics Review Pack for governance decisions

Course Agenda

Day 1: Ethical Foundations and Use Cases

• Healthcare AI Use Case and Intended Purpose Map
• Patient, Clinician, Organization, and Community Stakeholders
• Human Autonomy and Clinical Decision Boundaries
• Benefit, Harm, Safety, and Public Interest Assessment
• Healthcare AI Ethics Impact Assessment Canvas

Day 2: Data Rights, Consent, and Privacy

• Health Data Lifecycle and Purpose Limitation Map
• Informed Consent and Patient Communication Review
• Privacy, Confidentiality, Access, and Secondary Use
• Data Provenance, Quality, and Representation Questions
• Consent and Data-Use Control Matrix

Day 3: Bias, Equity, and Explainability

• Bias Sources Across Data, Design, and Workflow
• Population Representation and Subgroup Performance Review
• Equity Impact and Accessibility Considerations
• Transparency, Explainability, and User Information
• Bias and Explainability Evidence Checklist

Day 4: Accountability, Validation, and Oversight

• Human-AI Team Roles and Decision Authority
• Clinical Validation and Local Acceptance Evidence
• Vendor Claims, Limitations, and Documentation Review
• Accountability, Approval, and Escalation Matrix
• Ethics Committee and Multidisciplinary Review Path

Day 5: Lifecycle Governance Practice

• Performance, Safety, Equity, and Drift Monitoring Plan
• Incident Reporting, Investigation, and Patient Redress
• Model Change and Reapproval Decision Criteria
• Suggested Exercise: Conduct an Ethics Review Meeting
• Capstone Exercise: Healthcare AI Ethics Review Pack

Practical Exercises

The course uses suggested activities to turn ethical principles into repeatable governance decisions.

• Suggested activity: assess a healthcare AI use case for stakeholders, benefits, harms, autonomy, and decision boundaries
• Suggested activity: review a data and consent scenario for privacy, representation, purpose, and patient communication
• Suggested activity: challenge bias, explainability, and validation evidence from a fictional vendor submission
• Suggested activity: assemble an ethics review pack with controls, owners, monitoring indicators, escalation, and reapproval criteria

FAQs

Who suits healthcare AI ethics and governance training, and what does it assume?

Healthcare AI ethics and governance training suits people who approve, use, procure, validate, monitor, or assure AI-supported healthcare services. It assumes familiarity with healthcare workflows or governance responsibilities, but not programming or model development.

How does healthcare AI ethics training differ from technical machine learning training?

Healthcare AI ethics training focuses on patient rights, risk, evidence, accountability, oversight, and lifecycle controls. Technical machine learning training focuses on data preparation, algorithms, coding, model tuning, and deployment engineering.

What should a healthcare AI ethics impact assessment cover?

It should define intended use, affected groups, benefits, possible harms, autonomy, consent, privacy, bias, explainability, safety evidence, human oversight, accountability, monitoring, escalation, and conditions for suspension or change.

How should bias be reviewed in healthcare AI governance?

Reviewers should examine data provenance and representation, subgroup performance, workflow effects, access barriers, error consequences, mitigation evidence, local context, monitoring indicators, and a clear response when inequitable outcomes appear.

Conclusion

Participants take back a Healthcare AI Ethics Review Pack linking use-case purpose, stakeholder impacts, consent and data rights, bias and explainability evidence, validation, accountability, monitoring, incidents, and change decisions. It supports structured multidisciplinary review while preserving human responsibility for patient-impacting decisions.