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Healthcare Forward Deployed Engineer

Built the forward-deployed handoff layer for hospital-safe AI triage — 21 acceptance tests, forced human review on high-risk cases, and a deployment package built to hand off.

🛡️ 21
Contract tests — all pass
⚡ 39 ms
/v1/ask p50
🛑 HITL gate
Stop before harm
📦 Handoff ready
Runbook + deploy plan
Healthcare Forward Deployed Engineer — demo
PythonFastAPILangGraphBM25PydanticpytestOpenTelemetryDockerCloud RunGitHub ActionsEpic FHIR (mock)OAuthasciinema
Before

Before

Code with no handoff
Case arrives
AI predicts ESI
Nurse carries risk alone
No proof loop
×

Model can predict the triage level, but there is no proof dangerous cases were blocked.

×

Nurse has no clean moment to sign off or override — she just gets a number.

×

Hospital cannot easily review who approved what decision, or when.

×

Customer gets a model and a README, not an adoption package.

After

After

Hospital-ready handoff
Case arrives
AI checks safety
Nurse signs or overrides
Audit proves it
Hospital can hand off

Safety Gate21 contract tests: pediatric floor, chest-pain routing, suicidal ideation forced-review, sepsis SIRS shape, weak-evidence override, p95 latency cap. All 21 must pass — or the deployment is not ready.

Human DecisionHigh-risk cases pause. The AI does not just flag — it stops and waits for a nurse to sign or override before anything is written back to the patient record.

Audit ProofEach decision stamps what the model decided, confidence score, and latency. PHI goes to a restricted archive, metadata to the cloud index. If the archive write fails, the system screams.

Handoff PackageRunbook, deployment plan, smoke-test suite, ownership guide. What the hospital actually needs to keep this running without the engineer on call.

Business Target (90-day)Target, not achieved outcome: reduce triage-to-first-clinician contact by 15 minutes. Keep down-triage rate below 2%. These are the acceptance criteria in the customer contract — not measured results from a live deployment.