Documentation
Guides, templates, and practical resources for organizations exploring AI implementation
Implementation Guide
The six-step implementation model below illustrates how organizations may apply HAIIS framework components in practice
Assess your AI use case
Identify the specific healthcare AI application, data types, and regulatory requirements
Select compliance pattern
Choose the appropriate architecture pattern based on your regulatory needs (HIPAA, GxP, FDA)
Map security controls
Apply the security control mapping system to your chosen cloud platform
Apply governance protocols
Implement data governance protocols for sensitive healthcare data management
Run risk assessment
Complete the AI risk assessment methodology for healthcare-specific risks
Deploy via playbook
Follow the implementation playbook for step-by-step deployment guidance
Documentation
Architecture Patterns
Compliance-by-design blueprints for common AI use cases
- Compliance Automation
- Remote Patient Monitoring
- Serverless Resilience
- Predictive analytics
Security Controls
Cross-cloud security mappings
- AWS security controls
- Azure security controls
- GCP security controls
Governance Templates
Data governance protocols
- Data classification frameworks
- Access control templates
- Audit and monitoring protocols
- Data lifecycle management
Risk Worksheets
Healthcare-specific AI risk assessment tools
- Risk assessment worksheets
- Mitigation strategy templates
- Healthcare risk catalogs
- Compliance validation checklists
Implementation Playbooks
Implementation and validation guides
- Deployment guides
- Security assessment
- Performance testing
Glossary
Overview of key concepts and terms
- Healthcare regulations
- AI/ML terminology
- Cloud computing terms
- Security concepts