Originally published at news.skila.ai
Hospitals spent $2.3 billion more on healthcare claims over three years because of AI scribes. Not despite AI scribes. Because of them.
Blue Health Intelligence, the data analytics arm of the Blue Cross Blue Shield Association, analyzed commercial claims data from Q2 2022 through Q1 2025. The finding: hospitals using AI-powered coding and documentation tools generated $663 million in additional inpatient spending and at least $1.67 billion in additional outpatient spending. That is not a rounding error. That is a systemic cost shift paid by insurers, employers, and ultimately by you through higher premiums.
What AI Scribes Actually Do
AI scribes are ambient listening tools that sit in the exam room during doctor-patient conversations. They record everything, generate clinical notes, and suggest billing codes. Products like Microsoft's DAX Copilot, Abridge, and Nuance dominate the market. Together, they cover most major hospital systems in the United States.
The pitch is simple: doctors spend 2 hours on paperwork for every 1 hour with patients. AI scribes handle the documentation. Doctors get their evenings back. Provider burnout drops from 51.9% to 38.8% within 30 days of adoption.
That part is real. Burnout reduction is measurable and significant.
But here is what nobody put in the marketing brochure: when you record every word a patient says and feed it through an AI trained to maximize documentation completeness, the resulting clinical notes capture more diagnoses, more complexity, and more billable detail than a tired doctor typing notes at 9 PM ever would. More documentation means higher-intensity billing codes. Higher-intensity codes mean bigger insurance claims.
The Numbers
Trilliant Health's analysis tracked billing codes at six health systems from 2018 to 2024. The results show a consistent pattern across every single system.
High-intensity new patient codes (CPT 99204-99205) increased at every system studied:
- Health System C: 60.5% to 80.0% (+19.5 points) — the most dramatic shift
- Health System E: 47.5% to 67.0% (+19.5 points)
- Health System D: 44.5% to 63.7% (+19.2 points)
- Health System B: 42.9% to 57.6% (+14.7 points)
Before AI scribes, roughly 40-60% of new patient visits were coded as high-intensity. After adoption, that number jumped to 57-80%.
The Key Disconnect
The Blue Health Intelligence analysis found that acute posthemorrhagic anemia coding in maternity admissions rose from 4% to 12.3% at hospitals with the fastest AI adoption. But transfusion rates barely moved (0.7-0.9% increase).
If more patients actually had severe anemia requiring treatment, you would expect more blood transfusions. The transfusions did not follow the diagnoses.
Dr. Razia Hashmi, BCBSA's Vice President of Clinical Affairs: "Something is disconnected. Among hospitals showing the fastest rise in diagnoses of post-partum anemia, the rise in patients coded with this condition wasn't paired with the level of care we would have expected."
The Private Consensus
Caroline Pearson, Executive Director of the Peterson Health Technology Institute, summarized what everyone privately agrees on: "The investors, the health plans, and the providers, in private, were like, 'OK, well, it's quite clear scribes are increasing coding intensity. One hundred percent.'"
Everyone knows. Nobody agrees on what to do about it.
Key Takeaways
- Blue Health Intelligence found $2.3 billion in additional healthcare spending ($663M inpatient, $1.67B outpatient) linked to AI-enabled coding tools from Q2 2022 to Q1 2025
- Trilliant Health analysis of 6 health systems shows high-intensity new patient coding jumped 12-20 percentage points, reaching 80% at one system
- Post-partum anemia diagnosis coding tripled at AI-heavy hospitals (4% to 12.3%) while transfusion rates barely moved — a key disconnect
- AI scribe market valued at $600M in 2025, projected to hit $27.8B by 2034 at 48.2% CAGR
- Both insurers and providers privately agree AI scribes increase coding intensity, but publicly blame each other for the cost impact
Full analysis with the complete data tables and regulatory implications is at news.skila.ai
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