Healthcare AI Implementation Standards
Practical, open-access guidance for secure and compliant healthcare AI implementation
A vendor-neutral framework to help healthcare organizations implement AI with stronger compliance, governance, security, and risk management.
Open-access • Compliance-by-design • Informed by work in regulated life science environments
Healthcare and Life Sciences organizations run into the same wall: they understand the requirements, but have no concrete technical patterns for putting them into practice in real-world environments. We aim to close that gap with reusable architecture, controls, and deployment guidance built from actual implementations in regulated settings.
The HAIIS Philosophy
The Problem
Why Healthcare AI Implementation Fails
Regulatory Complexity
HIPAA, GxP, and FDA requirements are well-documented in policy, in addition to more philosophical frameworks that provide high-level guidance, but rarely translated into actionable technical patterns. Teams know what to do. They struggle with how to do it.
Multicloud Security Gaps
Each cloud provider has different controls. Without a unified approach, AI workloads across AWS, Azure, and GCP accumulate inconsistencies that become liabilities.
Data Governance Uncertainty
Sensitive healthcare data needs special handling across training, inference, and monitoring, but existing guidance doesn't address the AI lifecycle practically.
Framework
The Core Components
Architecture Patterns
Reusable AI blueprints drawn from real life sciences deployments, with compliance considerations embedded from the start.
Review patterns →Security Control Mapping
Cross-cloud security control guidance for aligning AI workloads across AWS, Azure, and GCP.
See security controls →Data Governance Protocols
Reusable approaches for data handling, access, lineage, and oversight across the full AI lifecycle.
Review governance protocols →AI Risk Assessment
Healthcare-specific risk evaluation designed for the unique failure modes of AI in regulated environments.
Coming soonImplementation Playbooks
Step-by-step deployment guides that take teams from architecture decision to controlled rollout.
Preview playbooks →Get Involved
Where to go next
Documentation
Everything you need to start building
Architecture patterns, security controls, governance protocols, and implementation playbooks, all in one place.
Collaborate
Help shape what gets built next
HAIIS is built through community input. Share your implementation experience, flag gaps, or contribute patterns from your own work.
Practitioners
Who this framework is for
Hospitals & Health Systems
Teams evaluating or deploying AI in regulated clinical and operational environments who need architecture guidance, not just policy checklists.
Pharma & Life Sciences
Implementation patterns for compliant AI workflows in research, documentation, and regulated data environments where audit trails matter.
Healthtech & Medical Device Builders
Practical guidance for secure, governed AI deployment in healthcare products where regulatory scrutiny is part of the product lifecycle.