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BCBS Links AI Coding Tools to $942 Million in Added Costs
The Blue Cross Blue Shield Association estimates that rising hospital coding intensity added $942 million in spending for its plans from 2023 to 2025. The association says secondary diagnoses accounted for about 70% of the increase, while i
AI.info Team · Written with AI · fact-checked ·
The Blue Cross Blue Shield Association says rising hospital coding intensity added an estimated $942 million in healthcare spending for its plans between 2023 and 2025. The association links the increase to wider use of AI coding tools, but its own analysis finds no corresponding change in care delivered—a distinction that matters when more complex codes lead to higher reimbursements.
In a statement published September 24, BCBSA said more patients were being documented as medically complex as hospitals adopted tools that can review lab results and electronic records for additional diagnoses. Its claims analysis found that coding changed while care remained largely unchanged. The association presents AI as a contributor to the trend, not as proof that every added diagnosis or dollar resulted from an AI system.
Secondary diagnoses account for most of the increase
About 70% of the added spending—more than $650 million—was tied to secondary diagnoses, conditions recorded alongside a patient’s primary reason for hospital care. Those diagnoses can move a claim into a higher-cost billing category. BCBSA said some may be identified from individual laboratory values, a type of information its release describes as well suited to automated review.
The association’s example involved anemia diagnoses following major bowel surgery. BCBSA senior vice president of product and data science Luke Chalker said hospitals recorded significantly more anemia diagnoses without a corresponding increase in blood transfusions. “The disconnect between diagnoses and treatment suggests that AI is identifying more billable conditions, not sicker patients,” Chalker said.
What the claims analysis can—and cannot—show
BCBSA based its analysis on de-identified claims data from its member plans. Those plans cover one in three Americans, the association said. The findings describe changes in submitted and paid claims; they do not establish that AI tools caused every change in coding or that every higher-severity code was inaccurate.
That caveat sits alongside the association’s central concern: when documentation moves a hospital stay into a higher-reimbursement category without evidence of additional care, spending can rise even if treatment does not. BCBSA says the added spending can place pressure on premiums and patients’ out-of-pocket costs, as well as costs borne by employers and taxpayers.
Hospitals’ AI adoption puts coding under scrutiny
More than 60% of hospital systems have begun using AI-enabled technologies that can document visits and analyze clinical notes and lab reports, according to BCBSA. Those tools can identify conditions that might otherwise go unrecorded, but the association argues that more diagnoses do not necessarily mean patients received more treatment.
The $942 million estimate is therefore a measure of added spending associated with more intensive coding, not a calculation of the software industry’s revenue or the cost of buying AI tools. BCBSA’s public findings leave the central dispute focused on the claims themselves: whether the higher-coded cases reflect more complete documentation or diagnoses that raise reimbursement without a matching change in care.