The knowledge layer for evidence-based medicine and clinical intelligence
Healthcare and life sciences organizations generate enormous volumes of clinical evidence, research data, and regulatory documentation. It lives in disconnected systems. Data Graphs connects it, governs it, and makes it queryable by both people and AI, grounded in your own evidence, not the open web.
The missing layer in your clinical knowledge ecosystem
Healthcare organizations sit on decades of clinical evidence, systematic reviews, terminology databases, and regulatory documentation, but it is scattered across disconnected systems. Evidence databases in one place. Clinical ontologies in another. Research outputs in a third. Regulatory submissions somewhere else entirely. Each system knows its own domain. None understands the connections between them.
Data Graphs provides the context layer that ties it together. Every clinical concept, study, intervention, outcome, and evidence relationship lives in a single governed knowledge graph. A systematic review is not just a document in a repository. It is linked to the clinical question it answers, the interventions it evaluates, the populations it covers, the evidence quality assessments it contains, and the downstream guidelines that reference it. This connected context is what turns raw clinical data into intelligence that clinicians, researchers, and AI systems can actually use.
Ask any clinical question. Get a sourced, auditable answer.
Clinical teams spend significant time searching for evidence to support decision-making, querying multiple databases, cross-referencing publications, and manually assessing evidence quality. With Data Graphs, all of that evidence lives in a single connected graph. Clinicians and researchers can ask questions in natural language, such as "What interventions show the strongest evidence for reducing hospital readmission in heart failure patients?", and receive answers cited to specific systematic reviews within their own governed evidence base. The AI does not hallucinate from the open web. It reasons over your data.
Connect the evidence to the guideline to the clinical question
A systematic review has limited value in isolation. Its real value lies in the relationships: which clinical question prompted the review, which interventions it evaluates, the quality of the evidence, which guidelines cite it, and which subsequent studies have updated it. Data Graphs makes these relationships explicit and traversable. A researcher can navigate from a clinical question all the way through the evidence chain in a single query, something that is impossible when evidence data lives in disconnected repositories. This connected view is also what enables AI-powered guideline development and evidence synthesis tools that are genuinely grounded in the literature.
One reference graph for the entire organization
Large healthcare organizations often run multiple clinical systems, research platforms, and regulatory tools that each maintain their own version of the same clinical concepts. The same intervention gets recorded with different codes in different systems. The same drug appears under different names. Data Graphs acts as the governed master reference: a single semantic model for clinical entities, terminologies, and relationships that all downstream systems draw from. When a term changes or a new intervention is added, it propagates from the reference graph to every connected system. Consistency without manual coordination.
“Cochrane uses Data Graphs as the reference data knowledge graph for evidence-based clinical analysis, supporting the WHO and medical practitioners globally.”
How Cochrane uses Data Graphs for evidence-based clinical analysis.
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