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Health insurer links AI billing tools to nearly $1 billion in suspect charges
The Blue Cross Blue Shield Association said AI contributed significantly to nearly $1 billion in questionable hospital charges. The dispute concerns tools used for medical coding and documentation, which influence insurance claims and patient bills. Billing vendors counter that AI-assisted coding can correct longstanding under-documentation rather than simply inflate costs.
Key points
- BCBSA linked AI-assisted coding to $942 million in additional costs between 2023 and 2025, but cited multiple contributing factors.
- Roughly 70% of questioned billing involved additional diagnoses without a corresponding change in care, BCBSA said.
- The American Hospital Association disputed the analysis, saying AI helps capture increasingly complex patient conditions.
- Blue Cross Blue Shield companies also use AI for claims review, with human clinicians reviewing clinical denials.
What happened: The Blue Cross Blue Shield Association estimates that AI-assisted hospital coding contributed to $942 million in additional costs for its health plans between 2023 and 2025, CNBC reported. The association questioned charges linked to additional diagnoses that increased reimbursement, often without a corresponding change in treatment. Its findings put billing automation at the center of a dispute over whether healthcare AI is improving documentation or increasing costs without improving care. The association did not attribute the entire increase to AI.
The details: Medical coding translates diagnoses and procedures into standardized codes used in insurance claims and patient billing. Additional diagnoses can move a patient into a higher-paying reimbursement category. BCBSA said some diagnoses may be detected from a single laboratory value, making them particularly suited to identification by AI tools. Luke Chalker, the association’s senior vice president of product and data science, told CNBC that roughly 70% of the questioned billing, or $653 million, involved additional diagnoses that were not accompanied by a change in care. The association said the growth in more complex coding coincided with a period when 60% of hospital systems began using AI coding tools.
Background: A higher bill does not necessarily mean an incorrect diagnosis. Christopher Whaley, a Brown University health economist who studies hospital coding, told CNBC that AI can identify legitimate conditions that previously went unrecorded. Other conditions may increase payment without influencing a patient’s care, he said. The American Hospital Association challenged BCBSA’s analysis, arguing that patients are older and more clinically complex and that AI helps providers accurately capture their conditions. It said the analysis lacked the context needed to assess effects on quality, access or spending.
Who it affects: Hospitals and insurers are both deploying AI in administrative work. The hospital association criticized insurers’ automated downcoding and denial practices, while Chalker said Blue Cross Blue Shield companies use AI in claims review but have qualified human clinicians review every clinical denial. For patients and employers paying for coverage, the concern is where additional costs ultimately land. Chalker said higher reimbursement without more care can eventually translate into higher premiums and out-of-pocket expenses. At the same time, Oliver Wyman partner Marisa Greenwald told CNBC that AI is reducing documentation workloads and helping physicians spend more time with patients.
What to watch: For healthcare teams choosing billing tools, the dispute makes coding accuracy, auditability and transparent documentation important checks alongside revenue gains. Greenwald said it remains difficult to separate better documentation from misuse, user error and overcoding. How much of the $942 million was attributable specifically to AI was not reported. Whaley warned that competing hospital and insurer systems could create an administrative arms race, adding technology costs without contributing to patient care.
Our take
Healthcare teams should assess billing AI for coding accuracy and auditability, not just revenue gains. The disagreement makes transparent documentation of AI-assisted decisions especially important.