AI is operating inside the healthcare billing system. Patients may already be paying the price

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AI is moving deeper into an already expensive and administratively complex U.S. healthcare system, not just to help doctors diagnose and treat patients but to determine what hospitals bill, what insurance pays, and which claims get denied. 

And for all the talk of AI as a productivity tool that will lead to efficiencies and serve as a deflationary force, some of the early evidence is raising questions about whether these tools will make an already costly system more expensive. 

Blue Cross Blue Shield Association recently estimated that hospitals' use of AI-assisted medical coding contributed to close to $1 billion ($942 million) in additional costs for its health plans between 2023 and 2025. BCBSA said much of that increase came from secondary diagnoses that moved patients into higher-paying reimbursement categories, types of diagnoses that it said "may be derived from single laboratory values, making it particularly well suited for detection by AI tools." 

The finding comes as hospitals, insurers, and other parts of the healthcare system increasingly use AI in billing, coding, and claims review, posing the question of whether reducing administrative burden can also intensify the financial incentives already built into the system. 

According to Christopher Whaley, a health economist at Brown University who studies hospital coding, AI appears to be "accelerating, and in some sense making it easier to capture, the existing and underlying billing incentives that are in the system."  

It is not that additional diagnoses identified through AI are necessarily inappropriate. "In many cases, the diagnoses are legitimate and weren't captured," he said.

But there are also conditions that "clinically just don't really matter and don't influence the patient's care," while still allowing another billing code to be applied and increasing payment, he said.