AI Clinical Operations Assistant
Build a hospital-grade clinical operations system: ingest FHIR and HL7, de-identify PHI to HIPAA Safe Harbor, resolve one patient across three MRNs, detect clinical risk, and draft summaries behind a clinician-approval gate.
Modules
6
Verified
0
In Progress
0
What you walk away with
- ✓A shareable portfolio URL with your project walkthrough
- ✓Module-by-module timeline of everything you built
- ✓All the code — pipelines, guardrails, deployment configs
- ✓Production patterns documented on your profile
A patient seen at a hospital, an urgent care and a family practice exists as three different people in three databases — and the dangerous drug interaction sitting across two of those records is invisible until they resolve. In this course you build an AI Clinical Operations Assistant end to end: ingest FHIR R4 bundles, HL7 v2 messages and free-text clinical notes with validation that treats a unit error as the safety issue it is; de-identify protected health information to the HIPAA Safe Harbor standard, shifting dates so intervals survive; build a master patient index that merges three medical record numbers into one person while leaving an unrelated namesake alone; detect drug interactions, care gaps and early deterioration, scored recall-first against labeled ground truth with a documented decoy that teaches alert fatigue; and ship a LangGraph agent that drafts a cited clinical summary behind a mandatory clinician-approval gate, with a full audit trail. Ends as a clinician review queue with gated FHIR write-back and honest monitoring. All patient data is fictional; nothing built here is validated for clinical use.