What if AI could turn chaotic clinical notes into clean, billable codes—without sacrificing accuracy or trust?

Every shift, emergency physicians face the same grind: time-crunched documentation, symptom-first note-taking, and the constant lure of the “unspecified” box just to move on. But what if a system could read between the lines—and suggest precise, payer-accepted codes grounded in real guidelines?

In this episode, we explore how retrieval-augmented generation (RAG) is reshaping medical coding. Laura, an emergency physician, shares what it’s really like to code in the middle of clinical chaos. Vasan, an AI engineer, explains why standard large language models often hallucinate ICD-10 and CPT codes—and how RAG brings the conversation back to solid ground with verifiable sources, official codebooks, and audit-ready citations.

We unpack a recent study comparing clinician-assigned codes to RAG-augmented outputs on actual emergency department charts. The results? When reviewers didn’t know which was which, they often chose the AI-generated codes—ones that captured true clinical meaning, like “alcoholic gastritis without bleeding” instead of the vague “epigastric pain.”

Beyond accuracy, we dive into the ripple effects: cleaner claims, fewer denials, stronger datasets for research—and the essential guardrails that keep things safe and ethical, from privacy safeguards to human review and confidence scoring.

If documentation has ever pulled you away from patient care, this episode offers a hopeful shift. Learn where retrieval-based coding tools fit into your EHR workflow, how clinicians can stay in the loop, and which high-volume complaints to tackle first for maximum impact.

Subscribe for more deep dives into clinician-centered AI, share this with the colleague who always codes “unspecified,” and leave us your biggest documentation headache—we’ll decode it next.

Reference:

Assessing Retrieval-Augmented Large Language Models for Medical Coding
Eyal Klang et al.
New England Journal of Medicine (NEJM) AI, 2025

Credits:

Theme music: Nowhere Land, Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0
https://creativecommons.org/licenses/by/4.0/