TL;DR: You might have expected AI to cut healthcare costs, whether it’s by reducing paperwork, automating the doctor’s notes, or thinning out hospital staff. But a new 60-page PwC report suggests the reverse: So far, one of its most widespread uses is making medical bills bigger. It’s an example of how AI isn’t only good at making tasks more efficient—it’s also very good at finding more granular ways to boost a sector’s bottom line.
What happened: AI is one of five potential drivers of health costs climbing up to 9% in 2027—matching this year’s rate, the highest since 2010–11—per PwC. The key reason: AI note-taking tools are documenting more specifics about diagnoses and medical complications that a rushed human clinician might have lumped into one broad “code”—a standardized billing label that tells insurers what to pay. Those extra details can justify a higher severity (read: higher paying) code, even if the actual care a patient receives is the same as before.
The devil is in the billing details: One Blue Cross Blue Shield analysis found that some hospitals saw the billing code for acute posthemorrhagic anemia in new mothers jump from 4% to 12.3% of maternity admissions between 2022 and 2025. The number of blood transfusions (a common treatment for this condition), meanwhile, barely budged. An audit of the hospital system with the steepest rise in this code found that fewer than 20% of the cases actually met the clinical criteria for a diagnosis. The rise in higher-intensity coding coincides with hospitals’ growing use of AI for billing. According to BCBS, “coding intensity” added $22 million to maternity spending at the hospitals studied in three years.
