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.”
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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:
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