HealthTech: PHI’s Mei Wa Kwong on Using AI to Support Rural Health Systems

“Across the industry, health systems are laying the foundation for artificial intelligence success, ensuring they have the infrastructure, governance and technical expertise to make the most of the technology. Organizations that get this right are well positioned to increase efficiency, enhance care quality and take advantage of predictive models.

For rural, independent and community health systems, the conversation about AI is a bit more nuanced. Though the benefits are there, unique challenges exist as well. As a 2025 paper from researchers at Texas State University notes, there are practical obstacles (such as the limitations of technology infrastructure and the cost of upgrading it) as well as “hypothesized barriers” (including beliefs that AI offers little benefit to the practice of medicine and that its use will hurt a health system’s reputation in the community).

With that in mind, rural health systems would be best served to focus on use cases that are both accessible and acceptable to providers, patients and policymakers, says Mei Wa Kwong, executive director of the Center for Connected Health Policy.

AI Use Cases for Clinical and Financial Operations in Rural Healthcare

National Rural Health Association CEO Alan Morgan says three common use cases for AI come up in his conversations with hospitals and health systems.

The first is deploying ambient AI to document patient appointments. This can help practitioners pay more attention to patients’ needs while alleviating the burden of note taking. “It’s amazing how much time this is freeing up,” Morgan says. “I think this is potentially the greatest benefit we may see coming from AI.”

Using AI to take notes improves the patient experience, Kwong explains, as practitioners no longer focus exclusively on their computer keyboard. Beyond the appointment, AI models can assess a patient’s records and flag issues worth a follow-up. For example, if a patient mentions in many visits that they’re having difficulty falling asleep, an AI model trained to detect patterns may flag that issue and prompt the health system to follow up.

“AI can help identify patterns that a doctor may not see at first, or that they may initially think is an offhand comment,” Kwong says. This scenario tends to come with little resistance, as it provides additional information to providers without explicitly telling them what to do.”

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Originally published by HealthTech