> 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/4.2-industry-use-cases/pattern.md).

# Pattern: clinical decision support

Healthcare — clinical decision support pattern. Faithfulness, drug-interaction safety, reasoning soundness, audit-ready evidence.

A clinical-AI assistant answers physician questions: differential diagnosis, treatment options, drug-interaction checks based on patient context. Used at the point of care, the system reads guidelines, returns a recommendation, and cites the evidence.

This pattern shows how to evaluate that system end-to-end on Stratix.

## What's at stake

| Risk dimension                                 | Magnitude                                          | Framework                        |
| ---------------------------------------------- | -------------------------------------------------- | -------------------------------- |
| Patient harm from contraindicated combinations | Adverse drug events at scale of patient encounters | Clinical incident reporting      |
| Per-incident malpractice exposure              | $250K–$5M                                          | Industry settlement bands        |
| FDA Software as a Medical Device action        | Loss of clearance, market withdrawal               | FDA SaMD guidance                |
| Hospital accreditation impact                  | Joint Commission inquiry, conditional status       | TJC sentinel-event protocol      |
| HIPAA exposure on PHI in trace bodies          | Per-violation civil penalties + breach reporting   | HIPAA Privacy and Security Rules |

## The evaluation pattern

A pre- and post-deployment **agentic evaluation** runs over a 50-100 trace curated set drawn from representative encounter shapes (rare-disease cases, multi-morbidity, pediatric vs. geriatric weight-based dosing, contraindicated drug combinations, common edge cases).

**Criteria mix (the 70/20/10 healthcare ratio):**

1. **Deterministic rules (\~70%)**

* Every drug pair the AI mentions is checked against an authoritative interaction database. Missing checks = CRITICAL.
* Every dose mention extracts cleanly and matches the prescribing reference exactly.
* PHI-redaction regex confirms no patient identifiers appear in any logged span.

2. **Natural-language assertions (\~20%)** — the AI's recommendation matches the ground-truth label from the case file.
3. **LLM judges (\~10%)**

* **Faithfulness judge** (GEPA-tuned against ≥50 clinician-labeled examples — multi-class severity output) — every clinical claim is grounded in retrieved guideline content.
* **Reasoning soundness judge** (GEPA-tuned against ≥50 examples) — penalize correct answers via flawed reasoning.

> Don't have labels yet? See [Bootstrap a judge before GEPA](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/guides/bootstrap-judges.md) for the week-1 setup.

**Trace shape captured:**

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

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

**Continuous trace evaluation:** the same configuration runs daily on a 5% sample of production traffic. Thresholds wired to in-app and Slack notifications. The `dose-mention` and `drug-interaction-coverage` rules drop to a per-trace alert, not a daily summary.

## Configuration in code

```python
# Python (SDK)
from layerlens import Stratix
client = Stratix()

faithfulness = client.judges.create(
 name="clinical-faithfulness",
 evaluation_goal="Every clinical claim in OUTPUT must be supported by chunks in the retrieved guideline content.",
)

drug_interaction_rule = client.scorers.create_code(
 name="drug-interaction-coverage",
 code="result = check_all_drug_pairs(output, db='rxnorm')",
)

trace_eval = client.trace_evaluations.create(
 trace_set={"tags": {"env": "production", "feature": "clinical-qa"}, "sample_rate": 0.05},
 judges=[faithfulness.id],
 scorers=[drug_interaction_rule.id],
 schedule="daily",
)
result = client.trace_evaluations.wait_for_completion(trace_eval.id)
```

```typescript
// TypeScript (REST)
const r = await fetch("https://stratix.layerlens.ai/api/v1/trace-evaluations", {
 method: "POST",
 headers: {
 "X-API-Key": process.env.LAYERLENS_STRATIX_API_KEY!,
 "Content-Type": "application/json",
 },
 body: JSON.stringify({
 trace_set: { tags: { env: "production", feature: "clinical-qa" }, sample_rate: 0.05 },
 judges: [faithfulnessJudgeId],
 scorers: [drugInteractionScorerId],
 schedule: "daily",
 }),
});
const traceEval = await r.json();
```

## What you get

* Pre- and post-deployment evaluations surface contraindicated-combination patterns before patient impact, not 6 weeks into production.
* Each release pin cites the evaluation IDs that gated it — a self-contained audit packet for FDA SaMD and Joint Commission inquiries.
* Continuous trace evaluation catches drift when formulary or guideline updates shift effective knowledge.
* Auditor-ready evaluation evidence is a byproduct, not a separate engineering project.

## Stratix capabilities used

* [Agentic evaluation](/8.-evaluate-score-the-outputs/agentic-evaluation.md) — three criteria types in one configuration
* [Judges with GEPA optimization](/8.-evaluate-score-the-outputs/judges-1.md) — faithfulness and reasoning soundness
* [Custom code graders](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/cookbook/custom-code-scorer.md) — drug-interaction database lookup, dose-mention extraction
* [Span-level scoring](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/cookbook/span-level-scoring.md) — retrieval and synthesis scored separately
* [Trace evaluations](/8.-evaluate-score-the-outputs/trace-evaluations.md) — continuous on production
* [Notifications](https://github.com/LayerLens/gitbook-full/blob/main/13-reference/sdk-python/notifications.md) — Slack and in-app routing

## Replicate this

* [Industry → Healthcare](/4.2-industry-use-cases/healthcare.md)
* [Healthcare scenarios](https://github.com/LayerLens/gitbook-full/blob/main/04-use-cases/industry/healthcare/scenarios.md) — six clinical-AI scenarios with full eval criteria
* [Healthcare evaluation patterns](https://github.com/LayerLens/gitbook-full/blob/main/04-use-cases/industry/healthcare/eval-patterns.md) — the 70/20/10 rule + GEPA labeling guide
* [Healthcare compliance](https://github.com/LayerLens/gitbook-full/blob/main/04-use-cases/industry/healthcare/compliance.md) — HIPAA / BAA, FDA SaMD, state regs
* [Cookbook: clinical Q\&A safety judge](https://github.com/LayerLens/gitbook-full/blob/main/04-use-cases/industry/healthcare/cookbook/industry-healthcare-clinical-qa.md)