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Blue Cross Blue Shield Association (BCBSA) estimated that increasingly complex hospital coding associated with AI-enabled tools added $942 million in spending for BCBS companies from 2023 through 2025. That is the association’s estimate and interpretation—not proof that AI alone caused the added payments, that the charges were fraudulent, or that the diagnoses were medically unjustified.
What BCBSA says the analysis found
In an analysis published September 24, 2026, BCBSA examined claims data and connected changes in hospital coding to an estimated $942 million in additional spending for BCBS companies across 2023–2025. BCBSA attributed approximately $653 million—about 70% of its estimate—to secondary diagnoses that shifted claims into higher-reimbursement categories.
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A secondary diagnosis is a condition recorded in addition to the primary reason for a hospital admission. Depending on the claim and payment system, those additional diagnoses can affect the reimbursement category assigned to a stay. A higher-paying category alone, however, does not establish that a diagnosis was inaccurate or that a hospital acted improperly.
Why BCBSA questioned the coding changes
BCBSA’s concern was that some increases in coded conditions did not appear alongside corresponding changes in treatment. One example highlighted in Healthcare Finance News’ account of the analysis was anemia recorded after major bowel surgery. BCBSA compared diagnosis patterns with transfusion patterns and said the anemia diagnoses did not correspond to an increase in transfusions.
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Luke Chalker, BCBSA’s senior vice president of product and data science, described the association’s interpretation this way: “The disconnect between diagnoses and treatment suggests that AI is identifying more billable conditions, not sicker patients.” That is Chalker’s interpretation of the claims and treatment patterns, not an independent determination that individual anemia diagnoses were invalid.
Why hospitals and health systems see AI differently
Hospital and health-system representatives cited in Healthcare Finance News argue that AI-assisted coding can bring together information from lab reports, medications, orders, and physician notes to produce a more complete record. From that perspective, a newly captured secondary diagnosis may reflect a condition that was present but previously omitted from the coded claim—not a newly invented condition or an attempt to inflate payment.
Healthcare Finance News reported BCBSA’s figure that more than 60% of hospitals and health systems use AI-enabled technology able to scan lab reports and patient-visit documentation for secondary diagnoses. BCBSA attributed that figure to BAM.ai; it should be understood as a reported estimate, not an independently verified census.
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What the competing interpretations do—and do not—show
| Question | BCBSA’s concern | Provider-side explanation |
|---|---|---|
| What changed? | BCBSA says more secondary diagnoses moved claims into higher-reimbursement categories. | Providers say AI can identify relevant information spread across records and prevent diagnoses from being left off claims. |
| What evidence is emphasized? | BCBSA points to claims and coding trends, including diagnosis patterns that did not track with treatment indicators such as transfusions. | Providers point to the breadth of information in clinical records that coding systems can assemble. |
| What can that establish? | The reported analysis supports BCBSA’s estimate and its concern about coding patterns; it does not determine the clinical validity of every diagnosis. | The possibility of previously omitted diagnoses explains why coding counts might rise; it does not prove that every additional code was appropriate. |
| Where do incentives enter? | Higher reimbursement gives insurers reason to scrutinize coding that changes payment. | Complete documentation can help providers avoid undercoding conditions that were actually present. |
The available reporting establishes BCBSA’s estimate and the competing explanations. It does not establish that AI caused each added payment, that any named hospital committed wrongdoing, or that the secondary diagnoses lacked clinical justification. A diagnosis without a matching treatment signal can warrant scrutiny, but it is not by itself proof of fraud or an incorrect code.
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AI is part of the dispute on both sides
The reported conflict is not simply hospitals using AI while insurers stand outside the technology. AI-assisted tools can be used to identify or assign codes in provider workflows, while automated systems can also be used to review claims and flag payment patterns. Inc. quoted Abridge founder and cardiologist Shiv Rao describing the broader automation contest as “bots fighting bots, agents fighting agents, a horrible dystopic future nobody wants to live in.” That is contextual commentary about insurer-provider automation, not a finding from BCBSA’s analysis.
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