memsolus
Usecase — Healthcare

AI that remembers
every patient,
every visit

Health assistants that know each patient's history, allergies, and clinical progress — and use that context to deliver better care every session.

Explore for Healthcare →View Documentation
SOC-2
HIPAA
Isolated memory
Care Timeline — MemSolus
Active memory
M
Maria Santos
Hypertension · 18 sessions · last 2 days ago
Active
18
Sessions
120/78
Blood pressure
94%
Adherence
Care timeline
!
Mon, 09:14
Adverse effect reported — dizziness after taking anti-hypertensive on empty stomach.
Adverse effect
◆
Mon, 09:16
Memory saved — metformin 500mg, empty stomach, dizziness.
Memory used
✓
Thu, 10:02
Goal reached — dizziness resolved after adjusting medication schedule.
Goal reached
Next AI action
Proactively ask about dizziness recurrence. Context from Monday automatically loaded — no repetition needed.
Problem

The invisible cost of AI
in healthcare without memory

01

Patient repeats their history

Every new session, the assistant forgets everything. The patient must repeat medications, allergies and symptoms — creating frustration and clinical risk.

02

Risk from missing context

Without persistent memory, the agent doesn't know about allergies or drug interactions reported in previous sessions — a real risk in digital health platforms.

03

Engagement that doesn't sustain

When the assistant doesn't remember the patient's journey, the experience feels impersonal and careless. Trust drops and platform engagement declines.

Features

Three ways memory transforms care

What the assistant knows
Metformin 500mg — twice dailyAspirin — allergic reactionHypertension — controlledDizziness on empty stomachPrefers morning appointments
01

Intelligent Care Assistant

Remembers the patient's history, allergies and medications between visits. The agent delivers personalized care without asking for the same information repeatedly.

Use case scenarioMaria, 58, reports dizziness after taking anti-hypertensive on an empty stomach. On Thursday, the assistant proactively asks about the dizziness — without Maria needing to re-explain anything.
Treatment adherence — 4 weeks
Week 185%
Week 272%
Week 391%
Week 494%
Pattern detected: adherence drops during travel weeks.
02

Chronic Condition Companion

Learns what works and what doesn't for each patient over time. Suggests treatment adjustments based on real clinical progression data.

Use case scenarioCarlos, 44, with type 2 diabetes — the assistant detected he drops exercise goals during travel weeks and now offers an alternative plan before departure.
Therapy progress — Stress reduction
Session 1 — Goal setting100%
Session 2 — Anxiety triggers100%
Session 3 — Coping strategies72%
Session 4 — Consolidation—
03

Therapeutic Progress Tracker

Builds on previous sessions to track patient goals and milestones. Each interaction starts exactly where the last left off, with full context.

Use case scenarioAna, 31, has completed 12 meditation sessions. The assistant presents a progress summary and proposes focusing on specific triggers mentioned in the last three sessions.
Measurable impact

Results that
improve care

Digital health platforms implementing persistent memory report significant improvements in adherence, engagement and perceived quality of care.

View Documentation →
−40–55%

Reduction in information collection time per session with persistent memory.

+35–45%

Increase in adherence to chronic condition treatments over 90 days.

−80–90%

Reduction in sessions started without adequate patient context.

+30–40%

Increase in completion of digital therapeutic programs with continuous context.

−70–85%

Reduction in patient reports of needing to repeat information to the assistant.

+15–25 pts

Increase in Net Promoter Score for platforms with persistent AI memory.

TypeScript
Technical integration

Works with your current stack

REST API

Complete endpoints to create, search and manage memories per patient, session and agent. Compatible with Node.js, Python, PHP and Go.

Any stack
Native MCP

MCP server to connect Memsolus directly to compatible agents without additional code. Integration in minutes.

Claude · GPT-4 · Gemini
Webhooks

Real-time notifications when memories are created or updated — useful for syncing data with EHRs and clinical management systems.

Real time
SDKs

TypeScript for React/Next.js and Node.js. Python for FastAPI/Django and pipelines with LangChain and LlamaIndex.

HIPAA · SOC-2
Why MemSolus

Memory that turns every
interaction into genuine care

Real semantic search

"Morning dizziness" is retrieved when asking about morning adverse effects — context by intent, not exact keyword.

Isolated per-patient memory

Each patient has their own isolated memory space, with granular access control and HIPAA and SOC-2 compliance.

Under 150ms

Real-time memory latency for frictionless care experiences, even at scale of thousands of patients.

Any framework

REST API, TypeScript SDK, Python SDK and native MCP. Integrates with LangChain, LlamaIndex, CrewAI and any AI agent platform.

Longitudinal memory

Stores patterns over time: what worked, what was abandoned, which interventions caused adverse effects.

HIPAA & SOC-2 ready

Secure and isolated storage per patient. Compliance with data protection regulations essential for digital health platforms.

The assistant that knows every patient — every visit.

Persistent memory for digital health platforms — more adherence, more trust, less risk.