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This work improves fail-closed behavior and traceability but does not self-certify clinical validity, regulatory compliance, or production approval; clinician-owned content and quantitative thresholds remain external release gates. ## 1. Enforce deterministic safety screening before AI - Refactor `backend/ai_processing/services.py` so a deterministic pre-screen executes before any gateway request. - Return an emergency-only response immediately when governed red flags match; do not invoke the LLM or generate a differential in that path. - Expand rule categories for cardiac, respiratory, neurologic, sepsis, hemorrhage, overdose, mental-health, abuse, obstetric/postpartum, anaphylaxis, and pediatric danger signs while clearly labeling the rules as unvalidated defaults pending clinician approval. - Make provider failure fail closed. Keep heuristic differential generation restricted to explicit non-production configuration and never allow it to override emergency screening. ## 2. Add strict versioned input/output contracts - Add DRF serializers in `backend/ai_processing/serializers.py` for symptom entries, bounded context, diagnosis requests, intake responses, differential entries, and complete pipeline responses. - Constrain list sizes, text lengths, enums, likelihood ranges, and context keys; reject unknown or malformed clinical context instead of passing arbitrary JSON to prompts. - Validate both model responses server-side in `services.py`; reject malformed or unsafe output rather than coercing it into apparently valid clinical content. - Add deterministic post-generation policy checks requiring uncertainty/limitations language and rejecting definitive-diagnosis or prescribing language. ## 3. Build trusted encounter context and tighten clinical privileges - Update `backend/ai_processing/views.py` to validate requests before running the pipeline and, for persisted encounters, build demographics, allergies, active medications, relevant history, and pregnancy context from trusted server-side records rather than caller-supplied identity data. - Keep stateless patient preview conservative: require the defined minimal context, expose missing-information warnings, and never persist preview output. - Restrict diagnosis feedback/modification and medication create/update/delete/approve actions in `backend/diagnoses/views.py` to verified clinician accounts with a populated medical-license profile; administrative roles retain operational visibility but no clinical-signoff authority. - Add reusable clinician-credential permission helpers to avoid inconsistent role checks. ## 4. Make safety output explicit and auditable - Add response fields for safety-screen version, limitations, missing information, intended-use boundary, and whether AI was invoked. - Persist safety metadata and model/source identifiers with generated diagnoses without storing more free-form PHI than required. - Ensure emergency guidance uses a conservative locale-neutral fallback and does not claim automatic emergency contact or localization. ## 5. Add requirement and conformance artifacts - Create a versioned clinical requirements document under `docs/enterprise/` covering intended use, excluded populations, supported locale/care setting, emergency taxonomy ownership, medication-safety limits, quantitative acceptance placeholders, downtime behavior, and release ownership. - Add a FHIR conformance document defining the currently supported read-only SMART foundation, version/scope assumptions, provenance expectations, and partner-test gaps. - Explicitly mark GraphRAG/Neo4j/Qdrant as out of current implemented scope until retrieval governance, poisoning controls, evaluation metrics, and source provenance are approved. - Update `PRODUCTION_READINESS.md` and safety/risk documents to reflect actual behavior and preserve clinician/regulatory approval as blocking gates. ## 6. Verification - Expand `backend/ai_processing/tests.py` with unit and API tests proving emergency screening occurs before `_call_llm`, malformed inputs/outputs fail closed, missing context is disclosed, provider failure is controlled, and tenant access remains isolated. - Add diagnosis permission tests proving admins and uncredentialed clinicians cannot provide clinical approval while verified licensed clinicians can. - Add representative safety cases for pediatric, pregnancy/postpartum, sepsis, overdose, indirect suicidality, severe allergy, stroke, and cardiac presentations. These are regression cases, not sensitivity/specificity validation. - Run Django checks/migration checks/tests plus frontend lint, typecheck, tests, and build; document any environment dependency that prevents a suite from running. ## Key constraints - Do not introduce medication interaction recommendations without an approved drug knowledge source. - Do not claim validated emergency sensitivity, clinical efficacy, HIPAA certification, FDA status, or FHIR conformance from code tests alone. - Do not implement GraphRAG as a placeholder or silently send unrestricted PHI to AI providers. - Preserve organization scoping, auditability, and fail-closed production defaults 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