Closing the Digital Divide: AI Governance for Rural Hospitals

AHA Knowledge Exchange Closing the Digital Divide: AI Governance for Rural Hospitals

AHA Knowledge Exchange

Realizing the promise of AI in rural communities

As artificial intelligence (AI) becomes a standard tool in health care, rural hospitals risk falling further behind in the digital divide. Many rural facilities lack the patient volume, technical expertise and resources needed to adapt externally developed AI models, build their own, or maintain them over time. New data shows that smaller, independent and critical access hospitals are struggling to keep pace with their peers in adopting AI technologies. Without intentional governance and implementation strategies tailored to rural realities, these communities may miss out on AI’s potential to improve clinical outcomes and operational efficiency. This Knowledge Exchange e‑book examines how rural health care leaders can design AI governance frameworks that reflect the unique challenges of rural care, ensuring equitable access, sustainable deployment and long‑term impact.

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7 high‑impact AI governance best practices for rural health care leaders

Multidisciplinary Team: Build multidisciplinary governance that includes clinical, operational, legal, cybersecurity, informatics and patient voices to strengthen trust and decision‑making.
Focus: Start with a governance model focused on safety, privacy, risk‑tiering and clear vendor expectations and iterate over time as needed.
Guidance: Shift from restrictive “don’ts” to clear safe‑use pathways that guide staff on how to use AI responsibly in daily workflows.
Vendor Oversight: Pressure-test vendor claims, understand how models were trained, ensure data‑use agreements protect rural populations and pilot before scaling. Require annual revalidation of all AI tools.
Check Data: Address rural data underrepresentation by assessing model fit, monitoring for bias and ensuring community benefit in partnerships.
Education: Prioritize education for clinicians, leaders, and patients, including how tools work, their limitations, how to communicate AI use to patients and how to avoid unsafe or unapproved tools.
Trust and Transparency: Lead with trust and transparency, especially as early wins, such as ambient documentation, improve workflows; clearly communicate to patients how and why AI is used in their care.