Training a Large Language Model for Medical Coding Using Privacy-Preserving Synthetic Clinical Data
Improving the accuracy and reliability of medical coding reduces clinician burnout and supports revenue cycle processes, freeing providers to focus more on patient care. However, automating the assignment of ICD-10-CM and CPT codes from clinical documentation remains a challenge due to heterogeneous records, nuanced coding guidelines, and long-tail distributions. Large language models have been proposed to help or automate specific medical coding tasks. However, foundation models are not explicitly trained for medical coding and zero-shot coding has yielded poor results. We investigate whether a modern open-weight foundation model can be adapted for an expert-level medical coding task usi...
| Authors | John Cook, Michael Wyatt, Peng Wei et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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