The Coalition for Health AI — known as CHAI — released comprehensive AI governance guidance for health systems on May 27, 2026. The playbook addresses one of the most pressing challenges facing healthcare organizations today: translating good intentions around responsible AI into structured, measurable and sustained practice. CHAI, which includes health systems, universities and technology companies among its members, designed the guidance to give organizations a shared operational language for AI governance while preserving flexibility for each system to adapt the framework to its own mission, workflows, maturity level and risk tolerance.
What the CHAI Governance Playbook Covers
From Intent to Governed Practice
Most health systems recognize the need for responsible AI deployment. However, many struggle to move from high-level principles to concrete operational structures. The CHAI playbook directly addresses this gap. It provides a structured set of elements that health systems can use to build, assess and strengthen their AI governance programs. Furthermore, the guidance is designed to be practical — not aspirational. The goal is to help organizations implement governance that is measurable and sustainable over time, not simply compliant on paper.
Taylor Rhodes on the Value of Shared Language
Taylor Rhodes, responsible AI program director at Mercy — the Chesterfield, Missouri-based health system — offered a pointed assessment of what the playbook delivers. She noted that these resources bring much-needed structure to one of the most important challenges in healthcare AI. She added that the guidance gives health systems a common operating language for responsible AI while still allowing each organization to adapt governance to its own context. This balance between standardization and flexibility is central to the playbook’s design philosophy.
The Eight Critical Elements of AI Governance
CHAI identifies eight critical elements that together form a comprehensive AI governance framework for health systems. Each element addresses a distinct dimension of responsible AI management.
Organizational AI Policy
Every health system deploying AI needs a clear, documented policy that defines the organization’s approach to AI use, acceptable applications, prohibited uses and accountability structures. Without a formal policy, governance efforts lack a foundation. Consequently, organizational AI policy is the first and most fundamental element in the CHAI framework.
Organizational Structure
Effective AI governance requires clear ownership. Health systems need designated roles, committees or offices responsible for overseeing AI programs. Moreover, those structures must have real authority — not just advisory functions. Clear organizational structure ensures that governance decisions are made by the right people with appropriate accountability.
Organizational Resources
Governance programs require dedicated investment. That means budget, staffing and tools allocated specifically to AI oversight functions. Additionally, resource allocation signals organizational commitment to responsible AI — it distinguishes systems that treat governance as a priority from those treating it as a compliance checkbox.
Responsible AI Lifecycle Management
AI tools must be governed throughout their entire lifecycle — from selection and procurement through deployment, monitoring and eventual retirement. Furthermore, lifecycle management ensures that governance does not end at go-live. Ongoing oversight of model performance, use cases and outcomes is essential for responsible deployment at scale.
Risk and Impact Assessments
Before deploying any AI tool, health systems should conduct structured risk and impact assessments. These evaluations identify potential harms, biases and unintended consequences before they affect patients or clinical workflows. Therefore, risk assessment is a proactive governance mechanism — not a reactive response to problems after they occur.
Responsible Data Management and Use
AI systems depend on data. Consequently, how an organization manages, protects and uses data directly affects the quality and fairness of AI outputs. Responsible data management includes data governance policies, privacy protections and processes to ensure training data is representative and appropriate for clinical use.
Third-Party Management
Most health systems source AI tools from external vendors rather than building them in-house. Third-party management governs how organizations evaluate, contract with and monitor those vendors. It ensures that external AI products meet the same responsible AI standards the health system applies to internally developed tools.
Education, Training and Feedback
Finally, governance requires ongoing education for clinical and operational staff who interact with AI tools. Training helps users understand both the capabilities and the limitations of AI systems. Furthermore, structured feedback mechanisms allow frontline users to surface concerns, errors and unexpected behaviors — feeding continuous improvement into the governance cycle.
Why a Common Governance Framework Matters
The healthcare industry has deployed AI tools at an accelerating pace. However, governance infrastructure has not kept up. Many health systems operate with inconsistent or informal AI oversight — varying by department, vendor relationship or individual leader. A common framework like the CHAI playbook creates a shared baseline. It also enables benchmarking across organizations and facilitates meaningful dialogue between health systems, regulators and technology partners. Additionally, standardized governance language simplifies collaboration between systems that increasingly share AI tools, patient populations and clinical workflows.
Who CHAI Represents and Why It Carries Weight
CHAI’s membership spans health systems, academic institutions and technology companies — giving its guidance cross-sector credibility. The coalition does not represent a single commercial interest. Instead, it brings together diverse perspectives to develop standards that serve the broader healthcare ecosystem. This broad membership base also increases the likelihood that the playbook’s eight elements reflect real-world implementation experience — not only theoretical frameworks developed in isolation from clinical practice.
What Health Systems Should Do With This Guidance
Health systems at every stage of AI maturity can use the CHAI playbook as a practical starting point. Organizations early in their AI journey can use it to build governance infrastructure from the ground up. More advanced systems can use it to audit existing programs and identify gaps. In either case, the eight elements provide a clear, actionable structure. The playbook does not demand a single prescribed model. Instead, it offers a framework that each organization can adapt to its own operational reality — making responsible AI governance achievable regardless of system size, resources or technical sophistication.
