About the Framework
How we're bridging the gap between policy and practice in healthcare AI
The Problem
The Problem We're Solving
Healthcare AI Implementation Standards (HAIIS) was created to address a recurring implementation gap: many organizations understand the policy and compliance requirements, but lack concrete technical guidance for putting them into practice across real systems and cloud environments.
The Regulatory Gap
HIPAA, GxP, and FDA requirements were documented in policy documents but rarely translated into actionable technical patterns. Organizations knew what to comply with, but not how to implement it.
Security Inconsistency
Each cloud platform had different security controls, creating gaps when organizations used multiple providers. There was no unified approach to securing AI workloads across AWS, Azure, and GCP.
Data Governance Challenges
Sensitive healthcare data required special handling throughout the AI lifecycle, but existing frameworks didn't address the unique needs of training, inference, and monitoring AI models.
Lack of Actionable Guidance
Most available resources were high-level principles without concrete implementation steps. Organizations needed playbooks, not just policy documents.
Our Approach
This initiative is informed by implementation challenges observed in regulated healthcare and life sciences environments, including issues related to compliance architecture, multicloud security, data governance, and AI risk management. HAIIS aims to translate those recurring challenges into practical, reusable patterns and documentation.
Scope
What HAIIS is (and is not)
HAIIS is not a regulatory authority, certification body, or substitute for legal or compliance review. It is an open-access implementation framework intended to help organizations operationalize healthcare AI more consistently and responsibly.
Principles
Our Guiding Principles
Problem-First Approach
Every component starts with a concrete healthcare AI implementation challenge
Regulatory by Design
Compliance requirements are embedded into technical patterns from the start
Vendor Neutral
Patterns work across clouds and other platforms with consistent security
Open and Accessible
Freely available to all healthcare organizations, with no licensing barriers
Status
Project Status
HAIIS is an open-access framework focused on practical implementation guidance for healthcare AI. The core framework is live, including architecture patterns, security controls, data governance protocols, risk methodology, and implementation playbooks. The project continues to evolve through community feedback and real-world implementation experience.
Community
Built for the Healthcare Community
The framework is intended to evolve through practical feedback, implementation experience, and collaboration across the healthcare ecosystem.
Our approach is: identify common challenges, develop practical solutions, document them clearly, and make them available to everyone. The framework grows through real-world implementation and community feedback.
Join and Collaborate →