Semantic search on medical records and documentation · GDPR + medical secrecy
We design specialised RAG (Retrieval-Augmented Generation) for the healthcare sector: on clinical history, internal protocols, practice guidelines, medical-legal jurisprudence and scientific literature. With on-premise architecture, health data pseudonymisation and clinical validation at every iteration.
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Documentary corpus audit (clinical history, protocols, guides, literature). On-premise architecture definition (vLLM/Ollama + pgvector/Qdrant + gateway). Initial DPIA.
Ingestion pipeline with automatic health data pseudonymisation (names, IDs, specific dates when applicable). Specialised embeddings (BioBERT, ClinicalBERT or multilingual). Clinical-aware chunking.
On-premise LLM model (Llama Med, Meditron 70B) or commercial in European cloud with contract. Clinical prompt design, guardrails, always-cite-source. Validation with doctors: 3 blind test rounds.
Deployment in the centre's infrastructure. Access audit, complete logs. Clinical training for user team. Continuous improvement plan with structured feedback.
Invented cases with metrics consistent with our real ranges.
Specialists query aggregated history summarised by RAG before each consultation. All responses with source document citation. Increased 20% quality time with patient.
Semantic search on PubMed + internal repository. System proposes relevant articles, extracts methodologies and compares results. Human reviewer validates.
Medical team queries clinical protocols + scales + relevant jurisprudence. Reduces analysis time on complex cases without compromising clinical quality.
Health data is special category under GDPR art. 9. Our standard architecture for healthcare RAG: (1) the entire pipeline (ingestion, embeddings, vector DB, LLM) deploys on-premise at the hospital or in European cloud with Spain residency (Azure Spain, GCP Madrid, OVH), (2) data never leaves the centre's perimeter, (3) the responding LLM is local open-source (Meditron, Llama Med) or commercial with enterprise contract without retention.
Additionally: pseudonymisation of direct identifiers (name, ID, phone, address) before indexing, DPIA as mandatory project step, processing agreements with any provider touched by pipeline, exportable audit of each query.
No. And it shouldn't. A well-designed medical RAG is an information search and structuring tool for the clinician, not a decision system. All responses include source document citation so the doctor can verify and contrast.
Any system making automated clinical decisions falls under medical device category under European MDR regulation and requires specific certification. We don't work in that perimeter — we limit ourselves to clinical support tools.
Yes. We support integration via HL7 FHIR (international standard), FHIR STU3/R4 and R5. For proprietary HIS without native FHIR we work with periodic exports or provider's custom API.
Integration is designed in project phase 1 (initial audit). We confirm what information can be indexed, in what format and with what update frequency.
Typical medical RAG project: 10-14 weeks, €60-150k depending on corpus complexity, integration requirements and volume. On-premise monthly operating cost: €800-3,000/month (GPU infra + maintenance + support).
Typical ROI: reduction of clinical review time + improvement in decision quality + capacity to absorb more patients without expanding team. In medium/large hospitals ROI materialises in 6-12 months.
Free 30-min diagnosis with architecture, timing and budget estimate for your specific case.