AI in Rural and Critical Access Healthcare: Closing the Technology Gap

The Rural Healthcare AI Gap: Rural and Critical Access Hospitals (CHAs) Start From Behind

Budget issues are among the primary reasons rural hospitals have been slower to integrate generative and agentic AI. Half of rural hospitals are operating at a deficit, according to the American Medical Association, forcing many to reduce critical services such as labor and delivery and cancer care. 

This reality leaves less room to experiment with emerging technologies. Before fully implementing an AI-powered tool, hospitals typically evaluate it for several months. That timeline involves risk analysis, testing and training staff to use it.

Alex Sushko, a solutions engineer at Glean who specializes in AI integration, says this is a major reason that the AI adoption curve has been uneven. “Larger organizations can afford to run an 18-month pilot and slowly onboard and tweak an AI tool to their specification because they have more room in the business case to absorb that investment. Rural hospitals may not be able to.”

EXPLORE: Find the right technology to elevate care coordination and support innovation.

Where AI Can Make an Immediate Impact in Rural Health Settings

With all of the headlines about how AI can accelerate medical research, enhance imaging, make surgeries more precise and automate administrative tasks, figuring out where to start can feel overwhelming. Rhew advises hospitals with slim IT teams to start small. 

“Rather than trying to deploy AI broadly, teams tend to see the most success when they start with one well-defined problem and then choose tools designed to work within existing workflows,” Rhew says.

To get value quickly, Sushko recommends focusing on processes within revenue cycle management, such as the hospital’s clean claims rate. “It’s a perfect use case for agentic capabilities, because if you have limited people within the administrative back office, AI can assemble information fast and add 10 times the productivity,” says Sushko.

Insurance appeals are another revenue-driving focus, as denied insurance claims cost hospitals nearly $20 billion per year. Although more than 80% of appeals are successful, fewer than 1% of denied claims are ever appealed at all. 

AI-powered systems can help hospital administrative teams investigate and resolve denied claims, potentially leading to financially impactful reimbursements. “An AI tool could handle a much greater volume of claims, with a person verifying critical details, and lead to a much higher payoff,” Sushko says.

HIPAA-compliant options to bolster revenue cycle management systems include: 

• Microsoft’s claims denial navigator
• The Claims Acceleration Suite from Google Cloud
• The watsonx platform from IBM
• Cloud-based solutions from Oracle Health

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Agentic AI in Rural Healthcare: Early Use Cases and What Hospitals Are Learning

“For rural hospitals and their IT teams, the easiest tools to adopt tend to be those that address a clear pain point and don’t demand significant new infrastructure or expertise,” says Rhew.

An early successful use case is the adoption of clinical ambient listening tools. Research shows these tools can reduce the amount of time clinicians spend on documentation and allow providers to engage more with their patients. 

One solution available is Microsoft Dragon Copilot. As part of its Rural Health Resiliency efforts, Microsoft recently announced it was offering a 60% discount to eligible rural hospitals. Amazon also recently unveiled its Amazon Connect Health solution, built to manage tasks including clinical documentation and appointment scheduling.

Using AI to bolster cybersecurity measures is another early use case, Rhew says. “AI can play an important role behind the scenes by strengthening operational resilience and helping small teams manage complexity more effectively.”  

Cybersecurity solutions tailored for hospitals include:

• The Falcon platform from CrowdStrike
• The Security Cloud platform from Cisco
• Cortex XSIAM from Palo Alto Networks

READ MORE: Rural healthcare organizations navigate uncertainty amid budgetary concerns.

A Practical AI Implementation Roadmap for Rural and Critical Access Hospitals

Adopting tools designed and developed by outside experts is the most efficient strategy for implementing AI in rural healthcare settings.  

Data shows that self-developed AI isn’t the standard practice for hospitals of any size. As of 2024, 80% of hospitals using predictive AI solutions accessed it via their electronic health record developer. And an estimated 52% of hospitals also used tools developed by other third-party vendors.

One of the benefits to this approach is that these platforms, including those referenced above, are designed with security in mind. “There are rigorous standards in healthcare for evaluating code,” Sushko says. “Enterprise solutions are already HIPAA-compliant because they were built for these settings.” 

When getting started with AI, Rhew suggests, readiness support is an important step. “This can include assessments that help IT teams understand how existing workflows, infrastructure and security practices align with the AI tools they’re considering, along with basic AI training for nontechnical staff.”

To help fund new technology, healthcare leaders have access to several federal programs created to support rural health AI initiatives, including:

• The Rural Health Transformation Program is allocating $50 billion to help states modernize rural health facilities and technologies.
• USDA Rural Development Community Facilities Programs offer grants and loans to rural communities for equipment including digital health tools and AI diagnostics.
• The FCC Rural Health Care Program Healthcare Connect Fund offers a 65% discount on broadband expenses to rural healthcare providers.

“Hospitals don’t need a dedicated AI team to get started,” says Rhew. “With the right priorities, guardrails and partners, even very small IT teams can adopt AI in a way that strengthens operations and supports clinicians without adding unnecessary complexity.”