How to automate
ICD-10 coding.
Coders spend four to six minutes pulling codes from every encounter, and manual accuracy still hovers near 80%. This guide covers where manual coding leaks time and revenue, the three ways to automate it, and how to roll out AI coding without breaking your revenue cycle.
- 4 to 6
- minutes coding per encounter
- ~80%
- median manual coding accuracy
- 8 to 12%
- denials from coding errors
From clinical note to billable code.
Every automated coding system runs the same four steps. The difference between tools is whether they stop at a suggestion or finish the job by writing codes back into the chart.
- 01
Read the documentation
The system ingests the clinical note from your EHR, the same note the visit already produced.
- 02
Extract the clinical concepts
A language model identifies diagnoses, symptoms, and procedures, understanding that SOB, dyspnea, and difficulty breathing all mean the same thing.
- 03
Map to codes
Concepts are matched to specific ICD-10-CM and CPT codes, applying specificity, sequencing, bundling, and payer rules.
- 04
Write back or surface for review
Codes either post to the billing workflow automatically or appear ranked by confidence for a coder to approve. Most tools stop at the suggestion; write-back to discrete fields is where the time comes back.
Three ways to automate coding.
Most teams weigh three paths: keep coding assistive with a human on every claim, outsource it, or run autonomous coding native to the EHR. Here is how they compare.
| Approach | How it works | Best for | Trade-off |
|---|---|---|---|
| Assistive (suggest only) | Software suggests codes; a coder reviews and posts every one. | Practices that want a human on every claim. | Still manual posting, so savings are capped by review time. |
| Outsourced coding (BPO) | An external team codes your charts off-platform. | Groups with staffing gaps needing fast capacity. | Variable turnaround, less visibility, cost scales with volume. |
| Autonomous, EHR-native | AI reads the note and writes codes back into the EHR, flagging only low-confidence cases. | Practices on a supported EHR wanting throughput without rekeying. | Documentation quality gates the automation rate. |
Assistive is the safe entry point
Most AI coding tools live here. It cuts lookup time, but a coder still posts every code, so the savings are capped.
Outsourcing buys capacity fast
You add coders without hiring, but trade process visibility and turnaround, and cost rises linearly with volume.
EHR-native is where time comes back
Codes land in discrete fields inside the EHR with no copy-paste. The catch: the tool must truly integrate with your EHR and your notes must support specificity.
Live in 30 to 60 days.
A focused four-phase plan gets you to measurable impact without straining operations. Validate before you trust, and start where the documentation is cleanest.
- Week 1 to 2
Assess and baseline
Pull baseline metrics: coding time per encounter, denial rate, and common error types. Review around 500 recent encounters to learn your documentation patterns and top diagnoses.
- Week 3 to 4
Configure and integrate
Set confidence thresholds, for example auto-post above 95% confidence and flag below 85%, and confirm the tool reads notes from and writes codes back to your EHR.
- Week 5 to 6
Validate in parallel
Run the AI alongside your current process. Compare its output to a gold-standard coded sample and track agreement rate, time saved, and false positives.
- Week 7+
Pilot, then scale
Start with two or three providers or high-volume, low-variability visit types. Set clear override protocols, then expand by specialty and payer.
Vague documentation, like missing laterality, acute versus chronic, or anatomic site, stalls automation. Mixing too many rare edge cases into the pilot blurs the win signal. And skipping change control means annual code-set updates quietly erode accuracy.
How to vet an EHR-native coding tool.
The gap between tools that save time and tools that create busywork comes down to integration depth and accountability. Use this checklist when you evaluate vendors.
- Real integration, not a workaroundIt should read notes from and write codes back into your EHR. On athenahealth, that means a verified Marketplace integration, not manual export.
- An LLM trained on your specialtyGeneric models miss specificity. Specialty-tuned models hold up against payer documentation standards.
- 100% encounter coverageThe tool should review every clinical encounter, not a sample, so no revenue or quality insight slips through.
- Payer-specific editsIt should flag likely denials before submission using NCCI edits, LCD/NCD coverage, and modifier logic.
- A complete audit trailEvery code suggested, modified, and by whom should be logged, with HIPAA-grade controls.
- Accountable accuracyAsk for a measurable accuracy number and how it is verified, not a vague promise of better coding.
Coding that finishes inside athenahealth.
Most coding tools stop at a suggestion. CarePilot reads the visit, codes it to 98% accuracy, and writes ICD-10 and CPT codes back into discrete fields inside athenaOne, so clean claims go out without rekeying. It is part of the work CarePilot completes from a single visit, alongside documentation, order entry, and the inbox.
- 98%
- coding accuracy
- 78
- minutes back per day
- 1 to 2
- business days to go live
Coding automation questions.
Can ICD-10 coding be fully automated?
For high-volume, well-documented visits, autonomous coding can post most codes without review, flagging only low-confidence or complex cases for a coder. Documentation quality sets the ceiling: the more specific the note, the more the system can finish on its own.
How accurate is automated medical coding?
Leading AI coding reports accuracy in the mid-90s and above, versus a roughly 80% median for manual coding. CarePilot codes to 98% accuracy, measured against the finalized encounter.
Does AI coding replace medical coders?
No. It absorbs repetitive, rules-heavy work so coders focus on complex encounters, documentation quality, and audits. The goal is augmentation, not replacement.
Does automated coding work with athenahealth?
Yes, when the tool has a real integration rather than a manual export workaround. CarePilot reads from and writes ICD-10 and CPT codes back into athenaOne's discrete fields without leaving the platform.
How long does it take to roll out?
Plan for 30 to 60 days: assess and baseline, configure and integrate, validate in parallel against a gold-standard sample, then pilot with a few providers before scaling by specialty and payer.
One visit in, codes out.
ICD-10 and CPT, written back into athenaOne. No copy-paste. Book a 30-minute demo.