Rural Hospital Leaders Say AI Governance Matters More Than Adoption Speed
The real challenge with artificial intelligence in healthcare isn't whether to use it-hospitals are already doing that. It's ensuring the technology works as intended and actually helps patients and staff, says Darrell Bodnar, chief information officer at North Country Healthcare, a three-hospital alliance serving northern New Hampshire.
Many health systems are implementing AI tools and seeing returns on investment. But uncertainty remains: Do leaders truly understand how their generative AI models reach conclusions? Can they trust those conclusions?
New Risks Require New Safeguards
AI can reduce administrative burden and improve clinical decisions. It also introduces risks if deployed without strong governance and oversight, Bodnar said.
"In rural healthcare especially, technology decisions carry significant weight because we operate with limited resources and smaller teams," he said. "AI tools must be evaluated carefully for safety, data integrity and clinical impact."
The stakes are high. Poor governance decisions now could create problems for years, he added.
A Structured Evaluation Process
North Country Healthcare evaluates every AI tool through multiple lenses: data security, clinical workflow integration, measurable outcomes, and strategic alignment. The organization uses a submission process modeled on nursing's SBAR framework-Situation, Background, Assessment, Recommendation-to vet requests.
A multidisciplinary team then decides whether to pilot the technology. This approach has led to practical implementations: ambient voice documentation now lets providers focus on patient conversations while the system generates structured clinical notes in the background.
The organization is also exploring AI that summarizes clinical documentation while citing the source material, letting clinicians verify information and maintain trust. Beyond clinical work, North Country is assessing AI for revenue cycle tasks like coding review and claims preparation-areas where rural teams often struggle with complexity and accuracy.
Three Priorities for Hospital Leaders
Bodnar outlined what other health systems should focus on:
- Governance. Define how tools are evaluated, who oversees them, and how outcomes are measured after launch.
- Collaboration. Include technology leaders, clinicians, compliance teams, and operational staff. Each perspective matters.
- Pragmatism. Target technologies that solve everyday problems for staff and patients. Focus on improving workflows and supporting clinicians while maintaining safety.
He added one more step: recurring evaluation after implementation to confirm expected outcomes are actually occurring.
For more on AI for Healthcare and responsible deployment frameworks, see how organizations are structuring their approach to Generative AI and LLM tools.
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