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Insurers claim AI is already increasing healthcare costs

Blue Cross Blue Shield says hospital use of AI tools led to an additional $942M in healthcare spending over a two-year period.

Anthony Ha

Publisher TechCrunch AI

Sep 26, 2026 at 9:02 PM UTC · 1 分で読める

Insurers claim AI is already increasing healthcare costs
Image via TechCrunch AI

Key Signal

$942M additional healthcare spending

Last Updated

13日前

翻訳中…

Hospitals’ use of artificial intelligence tools as they submit insurance claims led to an additional $942 million in healthcare spending over a two-year period, according to an analysis by the Blue Cross Blue Shield Association.

The BCBSA analysis found “a sharp increase in patients being documented as having complex conditions,” but argued there is a “clear disconnect between [medical] coding and treatment,” as there’s “no evidence of corresponding change in care delivered.”

The New York Times pointed the analysis as just the latest sign that AI is contributing to an increase in healthcare costs. While battles between hospitals and insurers over treatments and payments are nothing new, the NYT said the use of AI on both sides seems to be making it worse.

Dr. Shiv Rao, founder of AI startup Abridge, acknowledged that the use of AI could lead to “a horrible dystopic future nobody wants to live in,” with “bots fighting bots, agents fighting agents.” But Rao said it might also reduce tensions and cut costs.

And the BCBSA’s senior vice president Luke Chalker resisted characterizing the situation as a battle, claiming, “It’s not a war. It’s a completely one-sided blood bath,” with insurers on the losing side.

Why this matters

The reported spending increase suggests AI-assisted medical coding could materially affect healthcare costs and insurer reimbursement practices. It could also intensify disputes over whether AI-generated documentation reflects patients’ treatment needs, potentially drawing greater scrutiny from payers and regulators.

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Originally reported by TechCrunch AI

NewsLayer coverage based on externally reported material.

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