Clinical data that connects across studies.
Trial data, safety signals, operational metrics — scattered across EDC, CTMS, safety databases, and labs with no shared context. Fluree connects them into a governed clinical knowledge graph where entities resolve across studies, quality travels with the data, and institutional knowledge compounds instead of disappearing when the database locks.
Read the Pharma R&D Whitepaper
What Is Clinical Data Intelligence
Every study is an island. Every cross-study question is a project.
Clinical trials generate enormous volumes of structured and unstructured data: demographics, adverse events, lab results, concomitant meds, efficacy endpoints, site performance metrics, safety signals. Each study captures it with its own CRF design, coding conventions, and metadata — and when the database locks, that institutional knowledge disappears.
Fluree creates a clinical knowledge graph that connects trial, safety, and operational data across your entire portfolio — with governed vocabularies, cross-study entity resolution, and quality-aware lineage.
Every mapping decision, every entity resolution, every quality annotation compounds — so each subsequent study starts from accumulated knowledge, not a blank page.
Connect any source.
CSV, API, Postgres, Snowflake, Salesforce — Fluree ingests it as-is. No schema migration. No pipelines to maintain.
The graph builds itself.
Entities resolve, duplicates merge, and relationships infer in place — no modeling marathon, no manual ontology.
Answers, with receipts.
Ask in plain language. Every answer traces back to the exact row it came from — for humans, agents, and apps alike.
What Fluree Unlocks For Clinical Data
Six capabilities no single EDC, CTMS, or platform gives you on its own.
Subjects, investigators, AEs — resolved across your entire portfolio
Dr. Chen runs sites in three of your trials. An adverse event is coded differently across programs. Fluree Sense resolves these to golden records automatically — with confidence scores and full lineage. Cross-study analysis goes from months of manual reconciliation to a query.
- Subjects, sites, investigators, AEs, meds all resolve
- Persist across studies — Phase I signals inform Phase III
- Confidence scores + audit trail on every resolution
Reusable mapping assets, not from-scratch each study
SDTM mapping decisions from completed studies become templates for new ones. AI suggests variable-to-domain mappings from your historical patterns. Controlled terminology from ITM keeps coding consistent. Each new therapeutic-area study starts from accumulated knowledge, not a blank page.
- Mappings accumulate as reusable knowledge assets
- 40–60% faster for subsequent studies in the same TA
- Pinnacle 21 errors drop — same governed logic applies
Signals weak in one study, significant across three
The knowledge graph connects adverse events across your portfolio with resolved entities — same MedDRA term, same WHO Drug code, same patient population characteristics. Query hepatotoxicity signals across your oncology portfolio, normalized by exposure duration — the aggregate answer lands in seconds.
- Aggregate analyses across the whole portfolio
- Earlier signal detection than single-study analysis
- Accelerates PBRER, PSUR, and signal detection workflows
MedDRA, WHO Drug, SNOMED, CDISC CT — one managed system
ITM governs clinical vocabularies alongside your organization’s custom therapeutic-area terms. When CDISC CT releases a new version, ITM manages the transition with cross-vocabulary alignment — historical data stays connected and legacy coded terms map forward cleanly.
- MedDRA, WHO Drug, SNOMED, CDISC CT + custom terms
- Changes propagate across studies, not per-study forks
- Cross-vocabulary alignment for legacy term reconciliation
Immutable, source-to-submission, time-travel
Every data change logged with timestamps, user identity, and before/after state. Time-travel queries reconstruct any data point at any historical moment. Cryptographic provenance ensures integrity — quality annotations travel with the data, not in a separate audit spreadsheet.
- Every change auditable; every data point traceable
- Time-travel across any study state, any point in time
- Quality annotations travel with the data itself
Semantic architecture that adapts with the standard
Clinical data stored as semantic triples with governed vocabularies maps naturally to evolving CDISC, eCTD, and FHIR standards. When SDTM IG v4.0 introduces Non-Standard Variables, the semantic model adapts — no data re-processing. FHIR interoperability is native because RDF and FHIR share the same linked-data principles.
| Capability | Traditional CDM & platforms |
Fluree Clinical knowledge graph |
|---|---|---|
| Cross-study entity resolution | Manual, months of reconciliation | Automatic — golden records across every study |
| SDTM transformation | From scratch each study | Reusable assets — 40–60% faster in the same TA |
| Cross-study safety analysis | Manual aggregate, after the fact | Graph traversal across the whole portfolio |
| Vocabulary governance | Spreadsheets + per-study conventions | ITM — MedDRA, WHO Drug, SNOMED, CDISC CT in one system |
| Data lineage | Partial, platform-bound | Immutable, source-to-submission, time-travel |
| Unstructured clinical content | Not handled or limited | Protocols, SAEs, narratives extracted via CAM |
| Standards future-proofing | Manual rework per version change | Semantic architecture adapts natively |
| Data model | Proprietary formats and APIs | W3C (RDF / JSON-LD / SPARQL) + FHIR-ready |
Global pharma teams — live on Fluree today.
We’ve seen faster time-to-market as the result of enabling data-driven collaboration — enabling us to capture upwards of $150M in opportunity cost per month saved.
Gabriel Aviles · Co-Founder, Vitality TechNet