TITAN ScribeGuard — Clinical AI Documentation QA & Prompt Evaluation Framework
Technical documentation and structured evaluation checklists for engineering teams testing clinical note summarization prompts and LLM output accuracy using synthetic encounter transcripts.
/llms.txt, Schema.org JSON-LD & WCAG indicator check — or qualify your domain fleet for turnkey engineering remediation:Clinical LLM Prompt Evaluation Checklist
When benchmarking ambient clinical documentation or SOAP-note generation prompts in pre-production environments, engineering teams should evaluate outputs across four reproducible dimensions using synthetic (non-patient) transcripts:
1. Factual Grounding & Omission Checks
- Verify that every medication dosage, frequency, and route in the generated Assessment & Plan appears explicitly in the source transcript.
- Flag ungrounded clinical assertions (hallucinated pertinent negatives or unstated physical exam findings).
2. Negation & Laterality Preservation
- Test edge cases where a speaker corrects lateralities (e.g., left vs. right extremity) mid-conversation.
- Confirm that denied symptoms are recorded as pertinent negatives rather than active diagnoses.
3. Structured Schema Validation
- Enforce deterministic JSON or structured SOAP section headings (Subjective, Objective, Assessment, Plan).
- Validate output against strict JSON Schema rules before rendering in downstream clinician review interfaces.
Example Deterministic QA Rubric Snippet
{
"encounter_id": "SYNTH-0042",
"synthetic_only": true,
"checks": {
"ungrounded_medications": 0,
"laterality_mismatch": false,
"negation_inversion": false,
"soap_sections_complete": true
}
}
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