Built for healthcare innovation

Q: Is SurrealDB suitable for handling protected health information (PHI)?

SurrealDB is built in Rust for memory safety and provides fine-grained access control at the table, record, and field level. Combined with encryption at rest and in transit, it supports the security requirements of healthcare environments handling PHI.

Q: Can SurrealDB run on medical devices and edge gateways?

Yes. SurrealDB compiles to a single binary and runs embedded on resource-constrained hardware. Bedside monitors, wearables, and rural telehealth kits can store and query data locally, then sync when connectivity is available.

Q: How does SurrealDB support clinical AI workloads?

SurrealDB stores vector embeddings alongside structured patient records and supports native vector indexing. You can combine similarity search with graph traversals over biomedical relationships to build clinical decision support and diagnostic co-pilots.

Q: Does SurrealDB support audit trails and historical queries?

Yes. SurrealDB supports versioned temporal tables that let you query data as it existed at any previous point in time. This simplifies compliance audits, post-incident investigations, and pharmacovigilance reporting without maintaining separate audit databases.

Q: Can SurrealDB model complex patient-provider relationships?

Yes. SurrealDB's native graph edges model relationships between patients, providers, diagnoses, medications, and procedures. You can traverse these networks to detect fraud patterns, optimise care pathways, or power clinical knowledge graphs.

Unify patient records, sensor streams, diagnostic models, and regulatory reporting into one scalable, secure platform - deployed anywhere.

CAPABILITIES

Built with granular security, by default

Memory-safe, security-first language

Built in Rust to protect PHI and meet stringent regulatory demands such as GDPR. Ships effortlessly to browsers (WASM), wearables, and bedside IoT gateways.

Runs everywhere - embedded, in-browser, on the edge

Keep latency-sensitive AI inference close to the patient. Perfect for rural telehealth kits that store data locally and sync when connectivity returns.

Unified OLTP / BaaS architecture

Real-time ingest and API server in one component. Mobile nursing apps can write vitals, schedule medication, and trigger AI triage rules through the same endpoint.

Native vector search + knowledge graph

Store embeddings alongside structured patient data with graphs capturing complex biomedical relationships for generative AI clinical co-pilots.

SEE IT IN ACTION

Find similar patient cases with vector search

Combine structured EHR data with vector similarity to surface relevant diagnoses and treatment pathways.

1-- Find patients with similar symptom profiles
2-- using vector embeddings from clinical notes
3LET $embedding = SELECT VALUE embedding FROM ONLY patient:current;
4
5SELECT
6    id, name, diagnosis,
7    vector::similarity::cosine($embedding, embedding) AS similarity
8FROM patient
9WHERE similarity > 0.85
10ORDER BY similarity DESC
11LIMIT 5;

USE CASES

Core use cases for healthcare

Precision medicine and clinical decision support

Fuse vector search with longitudinal EHR, genomics, and imaging data to surface differential diagnoses and personalised care pathways.

Edge-ready medical telemetry and IoT

Rust-built binaries run embedded or in-browser (WASM) so bedside monitors and wearables can ingest time-series vitals locally.

AI-enhanced imaging and diagnostics

Store embeddings for X-ray, CT or pathology slides alongside metadata. Native vector index retrieves visually similar cases.

Patient-facing chatbots

Knowledge graphs pull lab results, appointments, and literature into a single semantic layer for clinically grounded conversational agents.

Fraud detection and billing integrity

Embedded graph analytics traverse provider-patient-procedure networks in real time, flagging anomalous claim patterns.

Continuous remote monitoring

High-frequency sensor streams feed the time-series engine; anomaly detectors alert clinicians to arrhythmias or sepsis risk in sub-second latency.

TRUSTED BY

Powering innovation across industries

Unlocking insights with knowledge graphs

Samsung uses SurrealDB to power knowledge graphs for real-time audience insights and ad targeting in its ad division.

Learn more

Unified infrastructure monitoring

Tencent uses SurrealDB to consolidate nine backend tools into one real-time monitoring platform.

Learn more

WHY SURREALDB

Why healthcare organisations choose SurrealDB

Horizontal scalability

Handle national health-service-scale datasets and hundreds of simultaneous AI agents without re-architecting.

Flexible and versatile data model

Switch between document, graph, and time-series views with one engine - ideal for evolving research schemas.

Graph queries for complex patterns

Detect multi-hop patterns for insurance fraud detection, care pathway optimisation, and zero-trust security graphs.

Time-series analytics

Sub-second aggregation over high-frequency sensor feeds for cardiac telemetry anomaly detection and risk-scoring wearables.

Historical querying with SurrealKV

Immutable time-travel queries simplify compliance audits, post-incident investigations, and pharmacovigilance back-testing.

Is SurrealDB suitable for handling protected health information (PHI)?

Can SurrealDB run on medical devices and edge gateways?

How does SurrealDB support clinical AI workloads?

Does SurrealDB support audit trails and historical queries?

Can SurrealDB model complex patient-provider relationships?

GET STARTED

Start building with SurrealDB

The multi-model database for AI agents. Unify data. Unlock intelligence. Scale anywhere.

CONTEXT

MEMORY

STORAGE

CLIENT LIBRARIES

TOOLS

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