AI medical coding software: what small practices should look for
The useful suggestion is one a clinician or biller can check quickly, understand and confidently accept or reject. Here is how to test for that.
A coding suggestion appears beside the note. It looks plausible. The biller’s first question is still the right one: “Where in the encounter does that come from?” If answering it means opening three screens and rereading the whole chart, the software has moved the work around.
Start with a task your team wants to improve
AI medical coding software covers several jobs: suggesting diagnosis or procedure codes, identifying documentation gaps, reviewing claims and helping work payer responses. A product can be good at one and limited at another.
For a small practice, choose the first problem before shopping. Is a clinician spending too long looking up codes? Is billing repeatedly sending notes back for clarification? Are claims arriving without enough context to review? These require different demonstrations and different measures of success.
For note-based suggestions, the starting point is straightforward: the documentation must support what is billed. CMS’s E/M documentation guidance makes that connection explicit. A plausible suggestion or a high confidence score cannot replace it.
Ask to see the sentence behind the suggestion
Have the vendor show the supporting passage, its source and the encounter it belongs to. You should be able to tell whether the information came from the clinician’s current assessment, an older problem list or something the patient reported during intake.
Those sources carry different meanings. A patient saying “I think I tore something” is not the same as a clinician documenting an established diagnosis. A historical condition should not quietly become a problem addressed today.
Use a fictional outpatient note with uncertainty in it. Ask what the tool proposes and why. The ICD-10-CM outpatient guidelines, Section IV.H, distinguish uncertain diagnoses from established ones and direct reporting to the highest degree of certainty known for the encounter. Your coding team should review the applicable rules; the demo should show that the software preserves those distinctions.
A missing detail should stay visibly missing
Specificity is useful when the record supports it. It is a problem when the software supplies it on the clinician’s behalf.
In a test note, leave out a detail relevant to the proposed code, such as laterality. Ask whether the software flags the missing information, offers an appropriate less-specific option where allowed, or guesses. Then add the detail through the normal documentation process and watch the suggestion update.
The clinician should clarify what actually happened. Nobody should be encouraged to add unsupported facts just to reach a preferred code. A useful tool makes an unresolved question easy to see and leaves the decision with the people responsible for the record.
Use this six-part demo checklist
| Test | Ask the vendor to show | What a useful result looks like |
|---|---|---|
| Source evidence | A suggestion and the exact documentation supporting it. | The reviewer can check the source without reconstructing the visit. |
| Missing information | An incomplete note, then a properly clarified version. | The gap remains visible; unsupported detail is not invented. |
| Review control | Accept, dismiss and edit a suggestion; finish without using one. | The reviewer controls the final selection and can see its status. |
| Effective dates | How code versions are chosen for an older date of service. | The system can explain which edition and applicable rules it used. |
| Billing handoff | Move reviewed work into the claim workflow. | Codes, documentation and open questions remain connected. |
| Changes after review | Correct the note after a suggestion was accepted. | The team can identify affected work and follow its review policy. |
Include a clinician and the person who actually prepares claims. They will notice different problems. A smooth experience for the author can still create extra work for billing.
Check code-set dates and licensing
“We keep the codes current” needs a more precise answer. Ask which code sets are included, how updates are managed and how the system selects the version relevant to the service date. A correction to an older encounter may need a different edition from a visit today.
The CDC’s ICD-10-CM files page publishes releases and their effective periods. It is a useful reference when asking how a vendor handles updates. Ask separately about CPT content licensing and the payer-specific checks included in the product.
Access to a code set, an AI suggestion and a payer coverage decision are separate things. None should be presented as automatic proof that a claim will be paid.
Measure the review burden during a pilot
Pick a small, representative set of encounters your team is authorized to use. Include ordinary visits and examples with missing or conflicting information. Have your qualified reviewers assess the output against the documentation and applicable rules.
Record review time, unsupported suggestions, necessary corrections and questions returned to the clinician. Track omissions too: a tool that makes very few suggestions can look accurate while doing little useful work.
Keep a short issue log with the source problem and its resolution. If one template repeatedly produces ambiguity, fixing that template may save more time than tuning the AI. Assess whether the combined clinical and billing workload improves, rather than judging the pilot by how often someone clicks Accept.
What this looks like in Moxcares
Moxcares Coding Companion presents optional ICD-10-CM and CPT proposals with supporting note context. The clinician can accept or dismiss proposals before signing, while the documentation remains available for downstream claims review.
The platform includes CPT content under an AMA license. That license does not endorse an AI suggestion or guarantee reimbursement. Your clinicians and billing team still decide whether the selected codes accurately represent the documented care and meet the relevant requirements.
Bring a sample documentation workflow to the demo, including a case that usually gets sent back to the clinician. That is a more useful test than asking the software to generate a long list of codes.
Keep reading
See the note and the suggestion together.
We’ll show how a proposal is reviewed, what happens to missing information, and how accepted work reaches billing.
Explore Coding Companion