HAIIS Core Pillars
The HAIIS framework (HAIF) is organized around five draft components intended to address common implementation barriers in healthcare AI.
Built across AWS, Azure, GCP, and other platforms
HIPAA, GxP, and FDA requirements embedded from the start
Intended to be made publicly available without restrictive licensing barriers
Patterns and playbooks, not just principles
Training-based approach for widespread adoption
The HAIIS framework (HAIF) is organized around five draft components intended to address common implementation barriers in healthcare AI.
Healthcare organizations struggle to implement AI while meeting regulatory requirements like HIPAA, GxP, and FDA standards.
Draft implementation patterns designed to help teams embed HIPAA-, GxP-, and FDA-relevant considerations into system architecture from the outset.
Cloud architects, IT leaders, compliance teams
Inconsistent security controls across different cloud providers create gaps and compliance risks.
Cross-cloud guidance for aligning security controls across AI workloads and healthcare data environments.
Security architects, cloud engineers, compliance officers
Managing sensitive healthcare data across AI training, inference, and monitoring lacks standardized approaches.
Reusable approaches for data handling, access, lineage, and oversight across the AI lifecycle.
Data governance teams, AI engineers, privacy officers
AI introduces unique risks in healthcare that traditional risk frameworks don't adequately address.
A structured approach for identifying and mitigating healthcare-specific AI implementation risks.
Risk managers, compliance teams, AI project leads
Healthcare organizations need concrete, step-by-step guidance for implementing AI solutions.
Step-by-step guides intended to help teams move from concept to controlled deployment.
Project managers, implementation teams, technical leads
Define the initial framework structure, publish draft concepts, and open collaboration channels
Review and refine draft framework components through feedback and early implementation discussions
Publish implementation guides, templates, and practical examples
Support education, partnerships, and broader grams, and continuous refinement based on community input