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Memory for healthcare AI.
Keep patient-scoped context current while preserving the sources, access, policy, and review behind each action.
$ mem.recall_at(subject="patient-8821", query="anticoagulant", as_of="2025-06-01") # → "Apixaban 5mg" (the active therapy then, not the discontinued Warfarin)
Proof points
Built for the chart.
AES-256-GCM keyed by patient or member identifier, so memory is segregated by subject at rest.
Patient-level deletion that's provable: destroy the key, keep the audit trail.
Department and care-team isolation enforced at the database layer via row-level security.
Reconstruct the chart context that was active on any date, for a decision or a review.
Technical safeguards mapped to controls. See compliance →
Patient, encounter, provider, condition, medication, ICD-10, NPI, CPT, and HCPCS normalization.
See it: as-of recall
Recall any chart state.
A first production boundary
Start with one reviewable clinical or operational workflow.
Map the facts, retrievals, model output, policy checks, care-team identity, human review, and downstream action for one workflow. Then test present recall, historical reconstruction, access boundaries, and subject-level erasure against synthetic or approved data.
Connector priorities
Where the records live.
FHIR, HL7, Epic / Cerner export paths, claims systems, provider directories, clinical document stores, and identity gateways.