AI Governance Playbook for Large Provider Groups (2026)
AI can improve throughput, documentation, and decision support, but enterprise provider groups usually fail in governance before they fail in model quality. This playbook focuses on practical controls that prevent avoidable risk.
Why Governance Now
Federal policy and market adoption both accelerated in 2024-2026. HTI-1 established algorithm transparency requirements in certified health IT, including decision-support source attributes. HHS also launched department-wide AI strategy work in late 2025. Provider groups should assume AI oversight expectations will continue to tighten.
Step 1: Build an Enterprise AI Inventory
Inventory every production and pilot AI use case, including:
- Clinical vs operational purpose
- User role and point-in-workflow exposure
- Patient-impact level and potential harm mode
- Vendor/model owner and update cadence
- Data lineage and retention profile
Step 2: Risk-Tier Every Use Case
Use a three-tier model:
- Tier 1: Administrative automation with limited patient-impact risk
- Tier 2: Documentation and coding augmentation with indirect clinical/financial impact
- Tier 3: Predictive or decision support interventions influencing clinical action
Tier 3 models require the strongest pre-production validation, transparency documentation, and ongoing monitoring.
Step 3: Establish AI Governance Council Structure
- Executive council: CIO/CMIO/COO/CFO/CISO accountability for policy and escalation.
- Model review board: clinical, analytics, compliance, privacy, and security reviewers.
- Operational owners: service-line owners responsible for local deployment outcomes.
Step 4: Define Evidence and Validation Standards
Every model should have documented intended use, performance metrics, known limitations, and rollback criteria. For predictive interventions in certified health IT, align your internal review packet to HTI-1 source-attribute categories so end users can assess appropriateness and safety.
Step 5: Security and Privacy Controls
Even where AI policy is evolving, HIPAA obligations are not optional. Build AI workflows to satisfy existing Security Rule controls and monitor OCR cybersecurity enforcement trends. If you use third-party AI services, enforce BAA and data-use boundary checks in legal and technical review.
Step 6: Production Monitoring and Change Management
- Track drift and alert thresholds by workflow
- Log override rates and adverse event proxies
- Review model updates through formal change board
- Use kill-switch criteria for safety/performance exceptions
90-Day AI Governance Launch Plan
- Days 1-30: build AI inventory and risk tiers.
- Days 31-60: stand up governance council and review templates.
- Days 61-90: run first full review cycle and monitoring dashboard.
Frequently Asked Questions
What should enterprise AI governance include in healthcare?
Decision rights, risk tiers, validation standards, incident response, and monitoring obligations tied to accountable owners.
How does HTI-1 affect AI governance for provider organizations?
HTI-1 requires algorithm transparency categories in certified health IT decision-support contexts, which should be reflected in procurement and governance review.
What is the first operational step to reduce AI risk?
Build an enterprise inventory and risk-tiering baseline before approving new AI deployments.