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Possibly—but the strongest evidence is an association, not proof that artificial intelligence caused every dollar. A September 24, 2026 analysis by the Blue Cross Blue Shield Association (BCBSA) estimated $942 million in additional Blue Cross Blue Shield healthcare spending from 2023 through 2025 at hospitals using AI-enabled coding tools. About 70% of that increase—roughly $650 million—came from secondary diagnoses that moved claims into higher-reimbursement categories, while treatment patterns did not show a corresponding change.
The finding describes insurer spending, not a universal increase on every patient’s bill. It does show how automated documentation and coding can create financial pressure: if software identifies more billable conditions than clinicians documented under older workflows, reimbursement can rise even when the care delivered does not. Whether those additional codes are accurate, clinically supported, or improperly aggressive requires claim-level review.
What does the nearly $1 billion figure actually measure?
The $942 million estimate covers additional spending in Blue Cross Blue Shield claims between 2023 and 2025 associated with hospitals that used AI-enabled coding tools. It is not a calculation of all U.S. healthcare spending, all insurers’ claims, or the amount added to one person’s bill. BCBSA compared spending patterns and identified a higher-severity coding pattern in which secondary diagnoses increased the reimbursement category of claims.
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|---|---|---|
| Total estimated increase | $942 million in additional BCBS spending during 2023–2025 | A national total or a guaranteed increase for every patient |
| Main coding pathway | About 70%, or roughly $650 million, was linked to secondary diagnoses that moved claims into higher-reimbursement categories | That every added diagnosis was false or unsupported |
| Clinical signal | Treatment patterns did not show a corresponding change | That no patient received clinically appropriate additional care |
| Evidence type | Observational insurer analysis | Proof that AI alone caused every dollar |
Because the estimate comes from one insurer association and an observational comparison, it should be read as a warning signal. Hospitals that adopted coding software may differ from other hospitals in size, patient mix, documentation practices, or contracts. Those differences can affect claims independently of AI. The result supports investigating a plausible mechanism; it does not settle causation.
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How can AI-assisted coding raise reimbursement?
AI can enter the revenue cycle at several points. A digital scribe may turn a conversation into a visit summary. Natural-language processing can search the record for diagnoses, procedures, complications, and services. Coding software can then suggest or assign standardized diagnosis and service codes for a human coder or billing system to submit.
Where the financial incentive appears
Many payment systems group claims by the severity and complexity of the patient’s conditions. A secondary diagnosis—one that is not the principal reason for admission—can still move a claim into a category with a higher payment. If a tool consistently finds additional conditions that qualify under coding rules, the hospital may receive more reimbursement for the same broad episode of care.
The concern is not that every additional code is wrong. A previously missed condition may be real and should be reported. The concern is a mismatch between what the record supports, what treatment occurred, and what the submitted code implies. The U.S. Government Accountability Office (GAO) explains that AI scribes create summaries and coding tools analyze records to suggest or assign codes. GAO warns that spending could increase if tools capture more diagnoses or services than non-AI workflows, particularly when that produces higher reimbursement.
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Why secondary diagnoses matter
Secondary diagnoses can affect payment without changing the principal procedure or the patient’s length of stay. An automated system that scans every note, laboratory result, and historical condition may surface more potentially billable items than a time-constrained human review. Each item still requires clinical support and compliance with coding rules; detection by software is not evidence by itself.
GAO identifies a practical obstacle: “The accuracy of these tools may be difficult to verify, and the overall effects on health care spending are uncertain.” In other words, a hospital can have a technically impressive model while lacking a reliable way to show that its suggestions are accurate, reproducible, and tied to documented care.
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What did the separate maternity analysis find?
In a March 5, 2026 release, BCBSA estimated approximately $2.3 billion in spending associated with aggressive AI-enabled coding in maternity admissions. The estimate included about $663 million in inpatient spending and at least $1.67 billion in outpatient spending. BCBSA highlighted cases coded for acute posthemorrhagic anemia without the transfusions that would ordinarily be expected for that diagnosis and quoted its Vice President of Clinical Affairs, Dr. Razia Hashmi, saying, “Something is disconnected.”
This maternity estimate is a separate analysis with its own population and methods. It should not automatically be added to the $942 million figure: the available findings do not establish whether the analyses overlap, use the same hospitals, or cover distinct claims. Both analyses point to a similar question—whether higher-severity diagnoses are appearing without a corresponding change in treatment—but neither proves that all such codes were generated by an AI system or were invalid.
Does the evidence prove that AI caused every extra dollar?
No. The BCBSA findings are observational. They identify an increase in spending associated with hospitals using AI-enabled coding and a pattern involving secondary diagnoses, but they cannot isolate AI from every other change in hospitals, contracts, patient populations, or documentation rules during 2023–2025.
Several explanations can coexist:
- AI may have helped coders find clinically supported conditions that older workflows missed.
- Software may have suggested conditions that were documented ambiguously or did not meet coding requirements.
- Human reviewers may have accepted aggressive suggestions because payment incentives favor higher-severity categories.
- Hospitals adopting AI may also have changed staffing, documentation templates, or billing policies.
Determining which explanation applies requires reviewing the underlying medical record, the code-selection logic, the human approval process, and the treatment delivered. The headline number is therefore best understood as an estimate of excess insurer spending associated with a coding pattern, not a fraud finding against every hospital that uses automation.
How could coding-driven increases reach your premiums or taxes?
Insurers pay claims from premiums collected from individuals, employers, and public programs. When average claims costs rise, an insurer may seek higher premiums or adjust benefits at a later renewal. Employers can respond by increasing payroll deductions, deductibles, or other cost sharing. Public-program spending can affect taxpayers. These are possible transmission routes, not a prediction that the BCBSA estimate will produce a specific premium increase.
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Your own bill can rise in more direct ways when a higher-severity code increases a deductible or coinsurance amount, when an out-of-network claim is priced from a larger allowed charge, or when an insurer denies part of a claim and leaves a balance subject to the plan’s rules. A claim can also increase the insurer’s spending without changing what you owe if you have already reached a deductible or have fixed copayments.
Administrative costs create pressure to automate
The American Hospital Association reported in 2024 that administrative costs exceed 40% of hospital expenses. The association has also described costly prior-authorization and denial-appeal work. Those burdens help explain why hospitals and insurers adopt automation. They also create a risk: a tool introduced to reduce paperwork can increase total spending if its coding suggestions systematically move claims into higher-payment categories.
Could well-governed AI reduce healthcare spending instead?
Yes, in theory. A 2026 NEJM Catalyst analysis by Nikhil R. Sahni, Kate O’Gorman, Rahul Agarwal, Heather Bello Thornhill, and David Cutler modeled broad adoption of machine learning, natural-language processing, and generative AI across U.S. healthcare. It estimated $438.9 billion to $810.7 billion in annual net value, equivalent to 5.7% to 10.6% of the $7.7 trillion spent on U.S. healthcare in 2024.
Those figures are modeled potential value, not observed savings. The analysis assumes broad, successful adoption and excludes one-time implementation costs. Savings might come from fewer administrative hours, earlier detection, more efficient scheduling, reduced errors, or better treatment decisions. They will not automatically appear in premiums or patient prices: hospitals, insurers, vendors, and employers decide how efficiency gains are allocated.
The practical comparison is not simply “AI good” versus “AI bad.” A system that reduces clerical work but increases unsupported reimbursement may raise total costs. A system that improves documentation accuracy, prevents duplicate services, and passes savings to patients could lower them. Governance determines which outcome is more likely.
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How should AI coding systems be judged?
Organizations evaluating a coding tool should test four questions before connecting it to payment:
| Test | What to verify |
|---|---|
| Clinical match | Do submitted diagnoses and services match the clinician’s documentation and the treatment actually delivered? |
| Independent accuracy | Has an auditor who is not paid according to reimbursement reviewed precision, false positives, and missed conditions? |
| Traceability | Can the organization show which record passages triggered a code, which model version produced the suggestion, and who approved it? |
| Distribution of costs and savings | Do efficiency gains reach patients through lower premiums, prices, or out-of-pocket costs, or do only provider and insurer revenues change? |
Controls should include sampling of high-severity claims, review of unusual diagnosis-and-treatment combinations, versioned audit logs, and a process for correcting claims after an error is found. Independent review matters because an internal dashboard that reports more captured diagnoses can look successful while masking inaccurate coding.
What can you do if a medical bill looks wrong?
A questionable code is a reason to request documentation, not proof that a hospital acted improperly. Keep the explanation of benefits (EOB), the itemized provider bill, and relevant clinical records together. Then work from the exact line item rather than disputing the entire claim.
- Compare the EOB with the bill. Check the service date, diagnosis or procedure description, billed amount, allowed amount, insurer payment, and your stated responsibility.
- Ask the provider’s billing office for the submitted codes. Request a plain-language explanation of any secondary diagnosis or service that changed the claim’s payment category.
- Compare the code with your records. Look for discharge summaries, operative notes, laboratory results, and other documentation that supports—or does not support—the diagnosis. Do not infer a diagnosis from a code alone; ask a clinician if the medical meaning is unclear.
- Request a corrected claim or formal reconsideration. State the line in dispute, the documentation you believe is inconsistent, and the correction you are seeking. Keep copies and note submission dates.
- Use the plan’s appeal process if needed. Follow the deadline and filing method on the EOB or member handbook. If the issue involves suspected improper billing, ask the insurer which compliance or fraud-reporting channel handles it.
- Seek local assistance for a large or unresolved balance. A state insurance regulator, an employer benefits administrator, or a qualified medical-bill advocate may explain available appeal rights. Availability and authority vary by location and plan type.
Do not withhold payment indiscriminately while an appeal is pending. Ask the provider whether it will pause collection activity or place the disputed amount on hold, and get any agreement in writing.
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Insurers and regulators can require stronger evidence for codes that materially increase payment, especially when the diagnosis is not reflected in treatment, monitoring, or expected interventions. Audits should examine both false positives and missed diagnoses so that oversight does not punish accurate documentation.
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Hospitals can separate clinical documentation support from payment optimization, require a qualified human to approve high-impact codes, and monitor differences between AI-assisted and manual coding. Vendors should disclose model versions, known failure modes, and the record evidence used to generate each suggestion. Patients and employers need clearer explanations of how coding affects allowed charges and cost sharing.
Those safeguards also make legitimate savings easier to identify. If an automated workflow reduces staff time or prevents duplicate services, organizations can measure the change separately from any increase in reimbursement. That separation is essential for knowing whether AI is improving care or merely finding new ways to bill it.
What should you take away from the two BCBSA estimates?
The $942 million estimate is a significant warning about AI-assisted coding and higher-severity secondary diagnoses, but it is limited to an observational analysis of BCBS spending from 2023 through 2025. The separate $2.3 billion maternity estimate suggests the issue may extend beyond one category of care, while the GAO’s findings explain why accuracy and oversight are difficult to verify. At the same time, the NEJM Catalyst model shows that responsibly deployed AI could create large net value. The deciding factors are documentation quality, independent auditing, transparent incentives, and whether savings reach patients.
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Is the $2.3 billion maternity estimate included in the $942 million total?
Not based on the published descriptions. BCBSA presented them as separate analyses, and the available information does not show whether their hospitals or claims overlap. Treating them as additive would overstate what the evidence supports.
Does a higher-severity code automatically mean a hospital overbilled?
No. A higher-severity code can be accurate when the medical record supports it and coding rules are met. The concern is a pattern of unsupported or weakly supported diagnoses, which can only be determined by reviewing the record and the claim.
Can an insurer reverse a claim after it has paid?
Plans and providers can generally process corrected claims or payment adjustments, but the procedure and deadlines depend on the contract and jurisdiction. Ask for the correction policy in writing and keep the original EOB, revised EOB, and correspondence.
What evidence would show that AI, rather than another factor, caused the spending increase?
Researchers would need claim-level records linking tool use to specific code suggestions, independent validation of those suggestions, and comparisons that account for hospital mix, documentation changes, and patient severity. Audits that follow claims from the source note through payment would provide stronger evidence than spending trends alone.
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Are the modeled AI savings guaranteed to lower premiums?
No. The NEJM Catalyst figures are projections under broad-adoption assumptions and exclude one-time implementation costs. Even if efficiency improves, insurers, providers, employers, and public programs must decide whether to pass the value through to premiums, prices, or patient cost sharing.
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