Complete AI Training
Sign inGet my AI kit

Your job's AI kit

Get your AI kit

Tell us who you are and what you do. We show you your kit right away and email you the link: skills, prompts, AI agents, MCP servers and courses for your job.

500+ jobs ready, and we make a kit for any other job. No payment needed to look.

Share

AI agent for medical coders

Coding Accuracy Audit Agent

Measure coding accuracy each month and reduce repeated errors

Coding Accuracy Audit Agent: what goes in, what the agent does and what you get

What it does

Coding teams must audit a sample of their own work each month and turn the findings into training. Doing it by hand is slow. This agent pulls a random sample of coded charts per coder, rereads the documentation, and checks diagnosis and procedure codes, modifiers and sequencing against the record and coding rules. It records agreements and differences with the reason for each. Every difference goes to a human auditor, who confirms or overturns it. It then calculates accuracy per coder, finds repeated error types, and drafts short education notes. Next month it checks whether those errors dropped. If they did not, it widens the sample for that error type and flags the coder for one-to-one training. You approve confirmed errors and any rebilling. Edge case: a chart corrected after billing is audited in its final version.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
ApprovedYes, continueNo 1 STARTS WHEN Month ends 2 USES A TOOL Pull a random sample per coder 3 DOES Recheck codes against documentation and guidelines 4 YOU APPROVE Auditor confirms or overturns each difference 5 DOES Calculate accuracy and group error types 6 DOES Draft education notes for repeated errors 7 CHECKS THE RESULT Did repeated errors fall compared with last month? If not: widen the next sample for that error type andflag for one-to-one training. Back to step 5. 8 RESULT Audit report and education plan
Read the steps as a list
  1. Month ends
  2. Pull a random sample per coder
  3. Recheck codes against documentation and guidelines
  4. Auditor confirms or overturns each differenceThe agent waits here for your OK.
  5. Calculate accuracy and group error types
  6. Draft education notes for repeated errors
  7. Did repeated errors fall compared with last month?If not: widen the next sample for that error type and flag for one-to-one training. Back to step 5.
  8. Audit report and education plan

How it decides

Each code is compared with documentation and guidelines. Differences are classified by type and confirmed by an auditor before counting.

  • At least 10 charts per coder per month
  • Accuracy under 95% triggers a focused review
  • Billing-impact errors go to rebilling review

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Sample size
  • Accuracy target
  • Error categories
  • Report format

What keeps you in control

It always asks you first

  • Confirming errors
  • Rebilling corrected claims

Hard limits

  • Results are not used alone for discipline
  • Corrections go through normal billing review

It stops when

  • Done: report issued
  • Stop: sample too small for a coder, carry over

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensFor September the agent sampled 10 charts per coder for 8 coders. It found 14 differences. The auditor upheld 11 and overturned 3. Accuracy was 95.2% overall, but one coder's modifier 25 errors made up 4 of the 11. The check showed modifier errors had not fallen since August, so the agent drafted a focused education note with two chart examples. The coding manager approved the education plan.

More agents for medical coders