aceArtificial Intelligence-Assisted Current Procedural Terminology Coding in Pediatric Orthopaedic Surgery: Promise or Premature?
Background: Accurate assignment of Current Procedural Terminology (CPT) codes is essential for reimbursement, compliance, and quality reporting in pediatric orthopaedic surgery. Manual coding remains resource-intensive and prone to error and variability. Large language models (LLMs) represent a potential solution for automating CPT assignment directly from operative documentation. This study evaluated the performance of ChatGPT-4o and ChatGPT-5 in generating accurate CPT codes from deidentified pediatric orthopa…
On 20 pediatric orthopaedic operative notes, ChatGPT-5 raised exact-match CPT coding accuracy to 60% from 30% for ChatGPT-4o, supporting use as a supervised first-pass coding aid.
Even the better model overcoded 25% and undercoded 30% of cases, leaving error rates above acceptable thresholds for unsupervised billing deployment.
The study used only 20 notes from one institution's case mix, and the authors state that larger-scale validation and integration studies are needed before broader implementation.
Evidence
- Peer-reviewedJournal of the Pediatric Orthopaedic Society of North America2026-08-03
How should this claim be treated?
Truvace Impact Record TRV-2026-1622, v1: “Artificial Intelligence-Assisted Current Procedural Terminology Coding in Pediatric Orthopaedic Surgery: Promise or Premature?.” Truvace, 2026-10-16. /record/TRV-2026-1622 (accessed at citation time). sha256 1b355f511ea0c856…
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