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MeethealthLabs

Evaluations

Know where your healthcare model fails.

Evaluations built around clinical constraints, difficult cases, and the mistakes that matter for your use case, measured against your model's own baseline.

Three layers

Rules first, judges second, clinicians where it counts.

Each layer catches what the previous one can't, and each is reported under its own name.

  1. 1In use

    Deterministic gate

    Versioned rules that run on every output in microseconds: weight-indexed dose bands, contraindicated diets, allergen screens, macro arithmetic, language, structure. Same input, same verdict, every time.

  2. 2In use

    Rubric scoring

    Six weighted axes (clinical safety, condition alignment, macronutrients, personalisation, cultural fit, internal consistency) plus a clinically-unsafe flag that caps the score. The judge model always differs from the model under test.

  3. 3Per engagement

    Clinician review

    Qualified reviewers matched to the specialty, on a stratified sample and on every flagged high-risk case, with a documented adjudication path.

What a finding looks like

Input, candidate output, checks, rubric findings.

Evaluation recordnsc-0001 · synthetic
1

Input task

Adult nutrition-planning task with two stated constraints and one missing treatment detail.

  • energy_target_range
  • fluid_limit
  • treatment_context: missing
2

Candidate output

A fluent, well-structured plan that applies both constraints and proceeds without asking for the missing treatment detail.

asks_for_clarification: false

3

Checks

  • schema_validtrue
  • constraints_referenced2/2
  • numeric_bounds_oktrue
  • missing_info_acknowledgedfalse
  • contradiction_screenuncertain
4

Rubric findings

  • Required treatment contextMissing information not flagged
  • Structured outputSchema v2.1 valid
  • Contraindication checkNeeds review
  • Clinical appropriatenessExpert evaluation required
Outcome: fail. Missing context was not recognised; the record is escalated for clinician review.
Illustrative record on a fictional synthetic task.

Our own numbers

We ran the gate on 1.27 million of our own generated plans.

Only 47.9% of 1,012,105 Gemma 4 plans came back with zero findings. That is what an unaudited corpus looks like, including ours.

Read the certification note
Contract diet_plan@1.0.0 · fingerprint 1a6c5cf6b9d37193
CorpusRecordsZero findingsBlocked
Gemma 4 plans1,012,10547.9%22.6%
Llama 4 plan pairs257,65524.0%23.0%
Claude Opus plans90420.5%14.7%

Blocked records carry at least one critical finding. Zero findings means internally consistent under the contract, not clinically validated.

Dimensions

Measure the capability your application needs.

A model can pass a generic medical QA benchmark and still fail a constrained clinical workflow. These are the dimensions we build evaluations around.

  • Weight-indexed dose safetyGate rule
  • Allergen and prohibition adherenceGate rule
  • Contraindicated therapeutic dietsGate rule
  • Numerical and macro consistencyGate rule
  • Plan language matches patientGate rule
  • Structured-output correctnessGate rule
  • Missing-context recognitionRubric
  • Condition alignment and conflict handlingRubric
  • Cultural and dietary fitRubric
  • Task-specific success criteriaPer engagement

You can't fix a failure you haven't detected.

Tell us the model, the deployment context, and the failures you need to catch. We'll propose rules, rubrics, a held-out design, and a reporting format.