The popularity of artificial intelligence (AI) shows no indication of easing anytime soon. Approximately two-thirds of physicians in the United States report employing the technology, and one in four U.S. adults says they’ve used it to gather healthcare information or advice.
Although the adoption of AI has increased throughout healthcare, governance has not kept up. More than 60 percent of organizations lack AI governance policies, and only 16 percent of health systems currently have a systemwide governance policy specifically intended to address AI usage and data access.
What Exactly is AI Governance in Healthcare?
AI governance is defined as the practice of reviewing, assessing and evaluating individual artificial intelligence tools to ensure that they can be used safely, responsibly, fairly and effectively with a healthcare provider’s patient population and in compliance with applicable laws. Core principles of AI governance consist of fairness and bias mitigation, transparency and explainability, accountability and oversight, privacy and data protection and safety and security.
| Pillar | Description |
| Multidisciplinary Oversight | Integration of clinical, technical, legal and ethical perspectives in governance decisions |
| Lifecycle Management | End-to-end governance from intake through post-deployment monitoring |
| Regulatory Compliance | Alignment with HIPAA, FDA, ONC and emerging AI regulations |
| Risk Mitigation | Proactive and systematic identification, assessment and management of AI-related risks |
| Organizational Readiness | Training, culture and infrastructure to support responsible AI adoption |
Successful governance of new technologies in healthcare requires a structured approach to manage their implementation as well as to foresee, measure and mitigate the consequences of the new technology. As the Institute for Healthcare Improvement (IHI) notes, as AI becomes increasingly embedded in the healthcare ecosystem, governance structures must evolve to ensure that its use is safe, effective and responsible.
As we mentioned in a previous blog, AI technology becomes a security vulnerability if implemented without encryption, access controls, audit logging and governance. When AI tools access electronic health records and process protected health information (PHI) without proper safeguards, medical groups and other healthcare providers face HIPAA violations and potential patient harm from biased outputs.
Advantages of AI Governance in Healthcare
A comprehensive governance system has multiple advantages, such as ensuring patient safety, maintaining ethical standards and regulatory compliance, fostering trust through transparency and accountability and managing privacy concerns. Additional benefits of a good governance system include visibility into the AI tools being used, predictability of the information needed to review, implement and monitor an AI tool, standardization of the procedures for evaluation, risk assessment and risk mitigation, clear lines of accountability and centralized and standardized documentation on each tool’s assessment and testing.
The American Medical Association (AMA) notes that AI governance enables healthcare providers to:
- Manage tool identification and deployment
- Standardize risk assessment and risk mitigation strategies
- Maintain comprehensive documentation
- Ensure safe applications with robust oversight
- Decrease clinician burnout
- Promote collaboration and alignment across the institution
5 Best Practices for Effective Healthcare AI Governance
According to the Joint Commission and Coalition for Health AI (CHAI) guidance on the responsible use of AI in healthcare, an AI governance structure and policies provide a systematic approach to implementation, evaluation and use of AI tools, and governance creates accountability which will help to drive the safe use of AI tools. That means that healthcare providers should proactively implement structured and systemwide governance frameworks or risk losing patient trust.
Best Practice #1: Establish a Central AI Governance Body
Accountability in AI governance requires establishing distinct internal ownership over AI outcomes and responsibilities for model decisions and impacts. Medical groups should create an AI governance committee with the authority to approve or reject AI use cases, set enterprise AI policy and prioritize projects based on organizational strategy and risk.
Key elements include:
- Executive sponsorship: A CMO, CMIO or CIO with clear decision-making authority
- Defined scope: What counts as “governable AI” (any system using PHI, affecting diagnosis or treatment or influencing patient access)
- Decision rights: Who can approve pilot deployments, full rollouts or decommissioning
- Regular meeting cadence: Monthly or quarterly governance meetings with standing agenda items
Best Practice #2: Build a Multidisciplinary Governance Structure
Effective AI governance requires cross-functional governance integrated with existing organizational structures. Establishing clear ethical guidelines for AI is essential for medical groups to ensure that AI technologies align with organizational principles, fostering trust and mitigating risks.
Integration strategies include:
- Quality and Safety Committees: Align AI governance with existing harm reduction, root cause analysis and sentinel event monitoring processes
- Multidisciplinary AI Governance Oversight: Ensure clinical, operational, compliance, patient safety, health equity, legal and IT stakeholders are represented within the steering committee structure so AI oversight is treated as an enterprise-wide governance responsibility rather than solely an IT function
- Privacy and Security Councils: Embed PHI protection assessments into AI review workflows
- Clinical Decision Support Governance: Extend existing CDS approval processes with AI-specific evaluation criteria
Best Practice #3: Implement a Risk Management Framework Across the AI Lifecycle
The purpose of AI governance is to maximize AI’s innovation potential while mitigating risks, data breaches and human harms. A structured risk management framework provides the scaffolding for consistent, defensible decisions across diverse AI applications.
The United States’ approach to AI regulation combines federal executive actions with state-level legislation, with notable laws emerging in states like California and Colorado. Medical Groups must track these evolving regulations and incorporate compliance into their risk frameworks.
Best Practice #4: Integrate Regulatory Compliance, Data Governance and PHI Protection
Governance is necessary for strict compliance with privacy and data protection regulations because it ensures data minimization and prevents unauthorized data leaks. Medical groups must embed HIPAA requirements and ONC transparency expectations directly into their governance processes.
Compliance integration points include:
- HIPAA-Aligned Controls: Minimum necessary access, role-based access controls, encryption in transit and at rest, audit logging, breach detection and response
- Vendor Due Diligence: Security questionnaires, BAA verification, data handling practices and model transparency documentation
- FDA Pathway Verification: Determine whether tools meet the definition of a medical device and require premarket review
- ONC HTI-1 Alignment: Verify vendors provide required transparency information about algorithms, training data, intended use, limitations and performance
Best Practice #5: Conduct Continuous Monitoring, Learning and Improvement
Governance for healthcare AI necessitates ongoing monitoring and improvement mechanisms, including:
- Performance Dashboards: Tracking of accuracy, calibration and fairness metrics over time
- Bias and Safety Monitoring: Periodic subgroup analysis and outcome tracking to detect disparities
- Clinician Feedback Loops: Structured channels for reporting concerns, confusion or potential harm
- Patient Feedback Integration: Mechanisms for capturing patient experience and concerns
- Scheduled Reviews: Quarterly or semiannual reassessment of the AI tool’s performance and alignment with current guidelines
AI governance frameworks should include mechanisms for oversight that ensure accountability persists after deployment. Medical groups should implement monitoring and compliance controls to ensure that AI systems are continuously evaluated for performance and adherence to governance standards.
Providertech.ai: Built-In Governance for Trustworthy Healthcare AI
At Providertech, we build governance capabilities directly into our platform architecture. We support HIPAA-compliant AI through:
- Encryption of PHI in transit and at rest
- Strict role-based access controls limiting data access to authorized personnel
- Detailed audit logging for every AI-driven interaction, including patient outreach, care coordination and conversational AI exchanges
- Secure integration with existing EHR systems
With Providertech.ai, you’ll get happy patients every single call, every single time. Listen to a sample recording. If you’re ready to advance your medical group’s AI initiatives without compromising safety or compliance, schedule a demo today!
