> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/more-in-this-section-2/scenarios-4.md).

# Healthcare scenarios

Healthcare AI has the highest stakes on the platform. The scenarios below are anchored on real workflows and the specific failure modes Stratix is shaped to catch.

## Clinical decision-support Q\&A <a href="#clinical-qa" id="clinical-qa"></a>

**Setup:** AI retrieves guideline content (UpToDate, internal protocols, PubMed abstracts) and answers a clinician's question. Used at point-of-care to surface the right evidence quickly.

**Why evaluation is hard:** clinicians have low tolerance for fabricated citations and zero tolerance for incorrect medication dosing. The system must cite — not just answer.

**Eval criteria:**

* **Citation existence rule** — every cited guideline section must exist in the retrieved corpus. Fabricated section IDs = CRITICAL fail.
* **Faithfulness judge** (GEPA-tuned against clinician labels) — every clinical claim in the answer must be directly supported by the retrieved chunks.
* **Dose-mention rule** — surface every dosage figure for human review; never present a dose without a citation to a real prescribing reference.
* **Reasoning soundness judge** — penalize correct answers that arrived via flawed reasoning.

**Trace shape:**

```
trace
├── span: retrieval (guideline_corpus)
│ └── outputs: chunks[]
├── span: llm (answer-synthesis)
│ ├── inputs: question, chunks
│ └── outputs: answer, citations
└── span: post-process (citation-validation)
```

Score both retrieval (precision\@k against ground-truth chunks) and synthesis (faithfulness against retrieved chunks) separately.

**Cookbook:** [healthcare clinical Q\&A safety judge](/more-in-this-section-2/industry-healthcare-clinical-qa.md).

## Patient triage chatbot <a href="#patient-triage" id="patient-triage"></a>

**Setup:** Patient describes symptoms; chatbot triages and recommends care level (emergency / urgent / routine / self-care).

**Why evaluation is hard:** missing an emergency is catastrophic; over-triaging routine cases overwhelms clinical staff. Tone matters — patients in distress need clear, empathetic guidance.

**Eval criteria:**

* **Emergency escalation rule** — if the trace contains symptom phrases matching emergency patterns (chest pain + shortness of breath, stroke FAST symptoms, anaphylaxis indicators, etc.), the response MUST contain "call 911" or "go to ER." CRITICAL fail otherwise.
* **No-diagnosis rule** — the chatbot may triage but must not assert specific diagnoses (no "you have X").
* **Care-level appropriateness judge** (GEPA-tuned) — across edge cases, the recommendation matches a clinician's verdict.
* **Empathy / tone judge** — appropriately warm without minimizing or alarming.

**Trace shape:**

```
trace
├── span: symptom-extraction
├── span: emergency-pattern-match (deterministic)
├── span: llm (triage-decision)
└── span: response-formatting
```

The deterministic emergency-pattern-match span is critical: it gives you a hard floor below the LLM's judgment.

## Medical document summarization <a href="#medical-summarization" id="medical-summarization"></a>

**Setup:** AI summarizes a chart, discharge notes, or prior-authorization request for downstream review.

**Why evaluation is hard:** subtle omissions can mislead the next clinician. Required structured fields (diagnosis codes, medications, allergies) must be preserved.

**Eval criteria:**

* **Faithfulness judge** — every claim in the summary appears in the source document.
* **Required-field scorer** (deterministic) — diagnosis codes, medications, allergies, vitals — required-field presence.
* **Length scorer** — summaries within configured bounds.
* **Omission-risk judge** — secondary check: is anything *important* in the source missing from the summary?

## Medication safety: prescribing assistance <a href="#medication-safety" id="medication-safety"></a>

**Setup:** AI assists clinicians with prescribing — drug-drug interactions, dosing for renal impairment, allergy cross-reactivity.

**Why evaluation is hard:** medication errors are the highest-risk class of clinical-AI mistake. Off-by-one decimal places matter.

**Eval criteria:**

* **Interaction-coverage rule** — every drug-pair in the input must be checked against an authoritative interaction database. Missing checks = CRITICAL.
* **Dosing-precision rule** — numeric extractions of doses must match the source exactly (no rounding, no unit substitution).
* **Allergy-cross-check rule** — patient's documented allergies must be reflected in any prescribing recommendation.
* **Prescribing-explanation judge** — explanation cites the rule that triggered any warning.

## Prior authorization <a href="#prior-auth" id="prior-auth"></a>

**Setup:** AI drafts a prior-authorization request based on patient chart + insurer formulary.

**Why evaluation is hard:** wrong drafts get rejected, costing clinician time and patient delay. Insurer-specific formulary rules vary.

**Eval criteria:**

* **Formulary-rule rule** — every requirement from the insurer's formulary must be addressed in the draft.
* **Faithfulness judge** — every clinical claim cites the chart accurately.
* **Completeness scorer** (deterministic) — required form fields all populated.

## Patient-facing Q\&A (post-discharge) <a href="#patient-faq" id="patient-faq"></a>

**Setup:** Patient asks questions after discharge — "what should I do if I miss a dose," "when should I call the office."

**Why evaluation is hard:** patient-facing tone differs from clinician-facing; complex instructions need plain-language clarity.

**Eval criteria:**

* **Reading-level scorer** — outputs at or below configured grade level.
* **Plain-language judge** — avoids jargon; uses concrete examples.
* **Escalation rule** — when symptoms suggest urgent follow-up, response includes the office contact path.

## See also

* [Industry pattern: clinical decision support](/4.2-industry-use-cases/pattern.md) — these scenarios composed into one runnable evaluation
* [Evaluation patterns](/more-in-this-section-2/eval-patterns-4.md)
* [Compliance](/more-in-this-section-2/compliance-4.md)
* [Cookbook: clinical Q\&A safety judge](/more-in-this-section-2/industry-healthcare-clinical-qa.md)
* [Concept: Agentic evaluation](/8.-evaluate-score-the-outputs/agentic-evaluation.md)
* [Use case: RAG evaluation](/4.1-general-use-cases/rag-evaluation.md)


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