Veterinary AI Scribe Accuracy: What the Percentages Do and Do Not Prove

Which veterinary AI scribe is the most accurate?

Today, no vendor can answer that question with a scientifically comparable percentage. The veterinary AI scribe market does not have a shared test set, a shared scoring rubric, an independent evaluator, or an agreed definition of “accuracy.”

That does not mean note quality cannot be measured. It means the measurement must be defined before the number is trusted.

This distinction matters because a veterinary medical record is not a transcription exercise. A polished note can still omit a medication change, assign a finding to the wrong patient, move owner-reported information into the objective section, or add a plausible detail that was never stated. Each error has a different clinical significance, and none is captured well by one undefined percentage.

A feedback rate is not the same as clinical accuracy

One current example is HappyDoc's advertised 99.8% accuracy figure. Its public explanation ties that percentage to the share of generated notes that do not receive negative user feedback.

That is a product-feedback metric. It may indicate that most users do not click a negative-feedback control. It does not establish the percentage of clinical facts that are correct, the percentage of notes needing no edits, or the result of an independent clinical review.

Several factors can separate feedback rate from clinical accuracy:

• Users may edit a note without submitting negative feedback.

• Users may notice only the errors that affect their immediate workflow.

• A note can contain one serious error and several correct facts, but a thumbs-up or thumbs-down control records only one binary signal.

• Users who stop using a product may no longer contribute feedback.

• The denominator may include encounters, notes, documents, or feedback events. Those are not interchangeable.

This does not establish that the notes are inaccurate. It means the figure should be understood as a non-negative-feedback rate unless a fuller validation method is made public.

The same standard should apply to every vendor, including CoVet.

The six dimensions of clinical note quality

An evaluation should score at least six dimensions separately.

1. Factual correctness

Are species, signalment, symptoms, findings, medications, dosages, diagnostics, and plans represented correctly?

A spelling difference may be minor. A wrong dose, duration, laterality, patient, or test result is not.

2. Completeness

Does the note include the clinically material facts that were stated or present in the source material?

Completeness should not reward verbosity. The question is whether a future clinician would have the material information needed to understand the encounter.

3. Unsupported additions

Does the note add diagnoses, findings, recommendations, client decisions, or other facts that were not supported by the conversation or supplied record?

This is often called hallucination, but the practical test is simpler: can each clinical statement be traced to the encounter or an approved source?

4. Attribution and context

Does the note distinguish what the client reported, what the veterinarian observed, what the team recommended, and what the client accepted or declined?

The words can all be correct while the meaning is wrong if attribution changes.

5. Structure and usability

Does information land in the right SOAP section, template field, patient chart, and document type? Is the note concise enough to review and complete enough to support continuity of care?

6. Workflow reliability

Did the recording complete? Was the note generated? How long did it take? Did the correct patient and appointment context transfer? How many clicks and editing minutes were required?

Reliability is not the same as accuracy, but buyers experience both together.

A practical 40-encounter benchmark

A clinic can run a meaningful comparison without building an academic study. Use the following protocol during a controlled trial.

Build a representative case set

Select 40 encounters that reflect the practice's real mix:

• 10 routine wellness or preventive-care visits

• 8 sick visits with multiple differentials

• 5 rechecks or chronic-disease visits

• 5 urgent or emergency encounters

• 4 dental or procedural encounters

• 3 multi-patient encounters

• 3 noisy, interrupted, or heavily accented encounters

• 2 complex referrals with prior records or PDFs

Adjust the mix for specialty, equine, exotics, shelter, large-animal, or academic settings. The benchmark should resemble the work the clinic actually performs.

Control the source material

Where consent, privacy obligations, and vendor terms allow, provide the same source encounter to each shortlisted product. If exact reuse is not permitted, use scripted simulations created from de-identified cases and present them consistently.

Do not compare one product on easy wellness visits and another on live emergency cases.

Blind the review

Remove vendor names and interface cues from the outputs. Ask two clinicians to review each note independently. A third reviewer can resolve material disagreements.

Score errors by severity

Use a simple severity model:

Report the rate of encounters with at least one critical or major error. Do not bury a small number of serious errors inside a large count of correctly transcribed words.

Track effort separately

For every note, record:

• Review and editing minutes

• Number of material corrections

• Number of formatting-only changes

• Clicks or steps from recording to finalized PIMS entry

• Failed, lost, or duplicate sessions

• Need to reopen the audio or transcript

The best system is not necessarily the one with the fewest character edits. It is the one that produces clinically acceptable output with the least total burden and the fewest material failures.

A sample scorecard

This table is an example format, not a claim about any product. Publish actual results only when the clinic can explain the case set, scoring method, reviewers, and denominator.

Questions every vendor should answer

1. What exactly is the denominator in your accuracy claim?

2. Is the percentage based on transcription, user feedback, edits, clinician review, or factual comparison against a reference?

3. Are users who make edits but do not submit feedback counted as accurate?

4. How are omissions and unsupported additions measured?

5. Are medication, dosage, patient-attribution, and client-consent errors scored separately?

6. Was the test performed on live veterinary encounters, scripted cases, or internal examples?

7. Were reviewers blinded to the vendor?

8. Was the evaluation independent, and is the protocol public?

9. How does performance vary by species, specialty, language, audio quality, and appointment type?

10. What percentage of sessions fail to generate a usable note?

A vendor that cannot answer these questions should not describe its percentage as clinical accuracy.

CoVet's position on accuracy claims

CoVet does not publish a universal clinical-accuracy percentage. Note quality depends on the source audio, case complexity, chosen template, language, clinician style, available patient context, and the definition used to score the result.

CoVet's in-house medical team, which is part of a broader group of more than 50 veterinarians and veterinary technicians or nurses across the company, contributes to templates, workflows, testing, and clinical review. That structure is relevant evidence about how the product is built. It is not permission to skip clinician review.

Every CoVet output should be treated as a draft until a qualified veterinary professional verifies and approves it.

The honest answer

There is no independently proven “most accurate veterinary AI scribe” across all practices and case types today.

There are products that may perform better for a specific clinic, specialty, language, PIMS, or documentation style. The only defensible way to identify that product is to use the same representative cases, blind the reviewers, score clinically meaningful errors, and track workflow effort separately.

Ask vendors to show their method. Then test the product, not the adjective.

The best way to evaluate an AI scribe is to try it in your own clinical workflow. See CoVet in action and compare the results for yourself.

Frequently asked questions

Can one percentage establish veterinary AI scribe accuracy?

Not without a definition and validation method. A vendor can report a feedback rate, word accuracy rate, editing rate, or another metric, but those do not automatically equal clinical factual accuracy.

What is the most important AI scribe accuracy metric?

Track encounters containing critical or major clinical errors, including wrong patients, medications, dosages, findings, decisions, and unsupported additions. Pair that with material omissions, editing time, and generation failures.

How many encounters should a veterinary clinic test?

Forty representative encounters can produce a useful operational comparison. Larger groups should use a bigger stratified sample across hospitals, clinicians, specialties, languages, devices, and PIMS workflows.

Can an AI scribe note be signed without review?

No. AI-generated documentation should be reviewed and approved by the responsible veterinary professional before it becomes part of the medical record.