When AI drafts clinical notes, triage summaries, or patient messages, a confident error is not a bug, it is a liability. Healthcare AI evaluation grades every output against your clinical rubric and compliance rules, sample by sample.
We grade outputs. We grade your AI's outputs against your rubric and your compliance rules. We do not provide doctors or licensed professionals, and our grading does not replace professional clinical review.
Healthcare AI output evaluation is the strict grading of AI-generated clinical and administrative outputs against a rubric built from your guidelines and compliance requirements. Health AI teams need it because the failure mode is a confident, fluent error in a chart, a summary, or a patient message. Good looks like outputs that match the source record exactly, stay inside approved scope, flag uncertainty instead of hiding it, and never invent clinical facts. To be plain about our role: we are graders of AI outputs, not a medical authority. Our work does not replace review by qualified clinicians.
In healthcare, a wrong output does not just annoy a user. We grade for the patterns that matter most.
The model adds a symptom, a dosage, or a diagnosis that the source record never contained, stated with total confidence. In a chart or handoff note, that invented line can travel. We grade every output against the source material and flag each addition.
Summarization loves brevity, and brevity drops things. A missing allergy, a missing contraindication, a missing follow-up date changes what the summary is safe to act on. We grade completeness against your required-fields list, not just fluency.
A documentation assistant that starts giving diagnostic opinions has left its lane. We grade whether the output stayed inside its approved scope: documenting, summarizing, and drafting, not diagnosing or prescribing.
Clinical language has honest hedges for a reason. When the model converts "possible" into "confirmed," it upgrades the reader's confidence without upgrading the evidence. We flag certainty inflation as a distinct, serious pattern.
You send 20 to 50 de-identified outputs plus your clinical rubric and compliance rules, or we help you write a rubric from your guidelines. We work only with de-identified samples.
Trained reviewers grade every sample against your rubric, backed by automated checks. Invented clinical facts and dropped criticals are treated as the P0s they are.
Sample-level grades, the failure patterns across the batch, recommended prompt and guardrail fixes, and a walkthrough call with your team.
The core of the grading is source faithfulness. Reviewers compare each output line by line against the source record: every symptom, dosage, date, and name. Anything in the output that was not in the source is listed as an invention. Anything in the source that should have survived into the output and did not is listed as a drop. This is slow, careful work, which is why every sample is graded by a person, not just a script. Our human evaluation approach explains why that matters.
Your required disclaimers, documentation standards, and scope boundaries become explicit pass/fail checks in the rubric. The report shows compliance gaps in their own section, separate from quality grades, so your compliance reviewers get a clean list instead of digging through prose to find what applies to them.
We also check what the output does not say. A summary that omits a required follow-up, a draft missing a standard disclaimer, a handoff note without the key dates: omissions get flagged with the same seriousness as inventions, because in clinical documentation what is missing can matter as much as what is wrong. The rubric carries your required-fields list explicitly, so completeness is graded rather than assumed. Fluency is cheap. Completeness is what makes a clinical output safe to act on, and it is the harder of the two to get right.
No, and we say that plainly. We grade AI outputs against the rubric and rules you give us. We are not doctors, we do not employ licensed clinicians as graders, and our reports do not replace professional clinical review. What we give you is a rigorous, documented read on where your AI's outputs break your own standards.
We work only with de-identified samples. You strip identifiers before sending, and we will tell you what we need to see to grade well. If a sample arrives with identifiers we should not have, we flag it and exclude it. Talk to us about your data handling requirements before the pilot starts.
Clinical note drafts, visit summaries, discharge summary drafts, prior-auth documentation, patient message drafts, and similar text outputs. If your AI produces it and your team can define what "right" looks like, we can grade it.
Yes. Your compliance rules become part of the rubric: required disclaimers, forbidden phrasing, documentation standards, scope boundaries. We grade each output against those rules explicitly, so the report shows compliance gaps as well as quality gaps.
Because clinician review is expensive and inconsistent as a measurement tool. We give you a systematic baseline: every sample graded the same way, failure patterns counted, severities assigned. That makes your clinicians' review time go to the cases that need judgment, not to catching the same invented dosage for the fiftieth time.
The method is our standard LLM evaluation, but the rubric is built for healthcare: source faithfulness, required-field completeness, scope boundaries, and certainty handling. The grading bar is deliberately stricter, because the cost of an error is higher.
Send de-identified samples. We will grade them against your clinical rubric.
Not sure where to start? Talk to us and we will point you at the right evaluation.