Artificial Intelligence (AI) is transforming the healthcare industry, offering powerful tools for diagnosis, treatment planning, and operational efficiency. However, as AI becomes more embedded in healthcare systems, regulators are stepping in to ensure responsible use.
California's recent January 2025 legal advisory underscores how existing state laws apply to AI, particularly in areas like data privacy, discrimination, and professional licensing. Healthcare organizations must navigate these evolving regulations while maintaining high standards of care and compliance.
A robust Information Governance (IG) framework is essential for aligning AI implementations with California’s legal requirements. By applying IG best practices, healthcare organizations can ensure compliance, reduce risks, and optimize the benefits of AI while safeguarding patient trust and data security.
More importantly, integrating IG by design into the development of AI systems ensures that records management principles are embedded from the outset, rather than treated as an afterthought. A forward-thinking approach to AI governance proactively addresses compliance risks, streamlines regulatory reporting, and enhances the reliability of AI-driven healthcare solutions. This includes the critical role of information governance professionals in shaping AI compliance strategies through structured gap analyses, the creation of retention schedules, and the development of policies and procedures that support regulatory adherence. Training initiatives led by IG professionals further strengthen compliance by ensuring that staff understand the nuances of AI governance and data stewardship.
Key AI Compliance Challenges in Healthcare
California’s AI legal advisory highlights several compliance challenges for healthcare organizations. AI applications must comply with California's privacy laws, including the California Consumer Privacy Act (CCPA) and the Confidentiality of Medical Information Act (CMIA). Mishandling patient data or unauthorized AI-driven decision-making can lead to regulatory penalties. AI models trained on biased data may result in discriminatory healthcare outcomes, violating civil rights laws. AI cannot replace licensed healthcare professionals, and AI-driven diagnoses and treatment plans must comply with scope-of-practice laws. AI-generated patient records and decision-making logs must align with legal retention requirements and be discoverable in legal proceedings.
How Information Governance Supports AI Compliance in Healthcare
Ensuring AI transparency and auditability is critical to compliance. AI-generated decisions must be explainable and traceable. An effective IG program establishes data lineage tracking to document AI training datasets, models, and decision-making rationale. It also requires audit trails that log AI-generated recommendations and their clinical validation by licensed professionals. Retention schedules for AI-generated patient records ensure compliance with state-mandated recordkeeping laws.
Proactively embedding IG principles in the design phase of AI systems ensures that compliance is not merely reactive but anticipatory. Structuring AI development with IG considerations at the forefront creates AI tools that are built for compliance, rather than requiring costly retroactive adjustments when regulators intervene. This approach ensures that access controls, retention policies, and data classification measures are integrated into AI workflows from the beginning.
The role of information governance professionals in this process is indispensable. These experts conduct gap analyses to identify areas where AI compliance frameworks may be lacking and ensure that retention schedules align with state and federal laws. Their expertise in policy development establishes a strong foundation for AI data stewardship, defining clear guidelines for data handling, storage, and deletion. Moreover, ongoing training initiatives led by IG professionals ensure that healthcare staff understand and adhere to AI governance best practices, reinforcing a culture of compliance across the organization.
Protecting patient privacy and security is another key aspect of IG. IG policies ensure that AI applications adhere to strict privacy and security standards. These policies include data classification frameworks that distinguish protected health information (PHI) from non-sensitive data, access controls to restrict AI system access based on role-based permissions, and automated data minimization strategies that remove redundant, obsolete, and trivial (ROT) data, reducing breach risks.
Mitigating AI bias through governance controls is essential to prevent discriminatory healthcare outcomes. IG helps prevent these outcomes by implementing governance policies that ensure diverse, representative training datasets, requiring bias audits for AI models before deployment, and documenting AI decision-making frameworks to identify and address potential inequities in patient care.
Reducing legal and compliance risks through defensible data practices is another fundamental benefit of IG. An IG-driven approach strengthens compliance with California’s legal framework by aligning AI recordkeeping with retention laws, ensuring that AI-generated patient records are maintained appropriately. It also includes the defensible deletion of obsolete AI-related data to mitigate liability and reduce litigation discovery costs. Automating compliance reporting demonstrates adherence to AI regulations and simplifies regulatory audits.
By incorporating IG by design, healthcare organizations can ensure that AI applications are natively compliant, reducing the risk of regulatory missteps while enabling smoother operational scalability. AI that is developed with built-in compliance features—such as automated data retention policies and bias detection mechanisms—demonstrates a proactive commitment to responsible innovation.
Information Governance professionals do not just help with managing data!
They are crucial in helping healthcare organizations create and roll out a structured and proactive approach that integrates AI responsibly into healthcare operations while safeguarding regulatory compliance, maintaining patient trust, and minimizing waste.
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