Health AI Memory Architecture For Persistent Patient Context

AI therapy assistants can retain extensive session material but may struggle to retrieve older, specific details as context accumulates, while referral letters often compress clinically relevant history into incomplete summaries and prose notes remain difficult to query. The proposed solution is a persistent memory layer that extracts structured facts after each session, such as diagnoses, medication reactions, coping-strategy outcomes, symptoms, goals, and trends, then retrieves only information relevant to a provider’s question across sessions and care settings. Mem0 is presented as a platform for this approach, using patient-level identifiers to share appropriate context among therapists, psychiatrists, and journaling tools while distinguishing provider roles and individual encounters. The discussion emphasizes mental-health-specific privacy obligations, including separate protections for psychotherapy notes, minimum-necessary access, consent controls for certain records, encryption, and audit logging, and recommends storing extracted facts rather than raw transcripts. It also describes managed, self-hosted, private-cloud, and air-gapped deployment options, and cites company-reported benchmarks suggesting lower latency, token use, and improved retrieval accuracy compared with full-context approaches.

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