Knowledge Graphs for Healthcare

Industry Application

Knowledge GraphsHealthcare

Healthcare generates more complex, interconnected data than almost any other domain—patient histories, genomic sequences, drug interactions, clinical trial outcomes, imaging findings, and decades of medical literature all intersect in ways that traditional relational databases were never designed to handle. Knowledge Graphs have emerged as the structural backbone for making sense of this complexity, enabling healthcare organizations to represent the rich semantic relationships between diseases, genes, proteins, drugs, symptoms, and patients in a single queryable substrate. As of early 2026, knowledge graphs are no longer experimental in healthcare—they are production infrastructure at major pharmaceutical companies, hospital systems, and health AI platforms, underpinning everything from drug repurposing pipelines to real-time clinical decision support.

Drug Discovery and Biomedical Knowledge Graphs

The most mature application of knowledge graphs in healthcare is pharmaceutical research, where the problem of connecting heterogeneous biological data—genes, proteins, pathways, diseases, compounds, and phenotypes—is precisely the problem graphs were designed to solve. AstraZeneca has developed one of the most cited open biomedical knowledge graphs, integrating data from UniProt, ChEMBL, Open Targets, and dozens of other sources to power target identification and drug repurposing. Their internal graph contains hundreds of millions of edges spanning gene-disease associations, protein-protein interactions, and compound-target relationships.

Harvard's PrimeKG (Precision Medicine Knowledge Graph), published in 2023 and now widely adopted in research pipelines, integrates 20 biomedical resources into a graph of 129,375 diseases, drugs, and biological entities connected by over 4 million edges. Pharmaceutical companies including Pfizer, Sanofi, and Merck have incorporated PrimeKG-derived structures into their early-stage research workflows. Amazon's Drug Repurposing Knowledge Graph (DRKG), developed in collaboration with the DGL team, similarly underpins AI-driven repurposing efforts across the industry.

UCSF's SPOKE (Scalable Precision Medicine Oriented Knowledge Engine) represents the state of the art in clinical research knowledge graphs, with over 40 million nodes spanning proteins, genes, diseases, compounds, symptoms, and anatomical structures. SPOKE powers UCSF's internal research into precision medicine and has been licensed to pharmaceutical partners for translational research. Graph neural networks trained on SPOKE have demonstrated statistically significant improvements in predicting drug-target interactions compared to vector-only approaches.

GraphRAG and Clinical Decision Support

The integration of GraphRAG architectures with healthcare knowledge graphs has become one of the defining developments of 2025–2026. Traditional retrieval-augmented generation relies on vector similarity search over unstructured text, which fails to capture multi-hop relationships critical to clinical reasoning—for example, connecting a rare symptom presentation to an underlying genetic polymorphism to a contraindicated drug class. GraphRAG addresses this by traversing the knowledge graph to construct structured context before generation, dramatically improving factual grounding.

Microsoft's Azure Health Data Services now ships with built-in GraphRAG capabilities, allowing hospital systems to deploy clinical decision support tools that reason over both patient records (stored as FHIR-compliant graphs) and institutional knowledge bases. Epic Systems, which dominates the EHR market with over 300 million patient records, has integrated graph-based reasoning into its clinical decision support modules, surfacing relationship-aware alerts that flat rule engines would miss. At Mass General Brigham, a GraphRAG deployment over their clinical knowledge base reduced physician time spent retrieving diagnostic references by an estimated 40% in a 2025 pilot study.

Agentic workflows are extending this further. Multi-agent frameworks where one agent queries the knowledge graph, another retrieves relevant clinical literature, and a third synthesizes recommendations are moving from research into production at forward-leaning health systems. Nuance (Microsoft) has embedded graph-traversal reasoning into its DAX Copilot ambient documentation product, using entity relationships to automatically populate structured fields in clinical notes.

Genomics, Precision Medicine, and Patient Graphs

Precision medicine—tailoring treatment to an individual patient's genetic, molecular, and phenotypic profile—is fundamentally a graph problem. Tempus, the AI-enabled precision medicine company with over $1 billion in annual revenue as of 2025, has built its entire platform on a multimodal knowledge graph connecting genomic sequencing results, clinical outcomes, imaging findings, and real-world evidence from its network of oncology partners. When a physician orders a Tempus genomic panel, the resulting report is generated by traversing a graph that connects the patient's mutations to relevant clinical trials, approved therapies, and outcomes from molecularly similar patients in the Tempus database.

Flatiron Health (a Roche subsidiary) operates a similar model in oncology, where a curated knowledge graph connecting cancer types, treatment regimens, biomarkers, and real-world clinical outcomes from over 800 cancer clinics powers both internal research and pharmaceutical partnerships. Their knowledge graph has become a primary real-world evidence source for FDA submissions, with several drug approvals in 2024–2025 relying on Flatiron graph-derived datasets.

Rare disease diagnosis is another area where knowledge graphs provide outsized value. Patients with rare diseases see an average of 7 physicians over 4.8 years before receiving a correct diagnosis. Fabric Genomics and FDNA (Face2Gene) use phenotype-to-genotype knowledge graphs to dramatically compress this journey, traversing HPO (Human Phenotype Ontology) nodes against variant databases to rank differential diagnoses. These tools reduced time-to-diagnosis by over 60% in clinical validations at pediatric genomics centers.

Interoperability, FHIR, and Health Data Networks

The structural alignment between HL7 FHIR (Fast Healthcare Interoperability Resources) and graph data models is not incidental—FHIR resources map naturally onto nodes and edges, and leading health data platforms have embraced this convergence. Palantir Foundry, deployed across the NHS in the United Kingdom and at dozens of US health systems, represents clinical data as a federated knowledge graph where patient encounters, lab results, medications, and diagnoses are connected entities traversable across institutional boundaries without centralizing sensitive data.

Amazon Web Services' HealthLake service uses Amazon Neptune (a managed graph database) as a core component, storing and querying FHIR-structured patient records as graphs. Customers including Cerner (Oracle Health) and several large regional health networks use Neptune-backed graphs to power population health analytics—identifying patient cohorts with specific combinations of comorbidities, medications, and social determinants of health with query patterns that would require dozens of table joins in a relational system.

The Unified Medical Language System (UMLS) and SNOMED CT continue to serve as the ontological backbone for most healthcare knowledge graphs, providing the formal semantic layer that allows entity alignment across heterogeneous data sources. Stardog's enterprise knowledge graph platform, widely used in life sciences, uses SPARQL-based reasoning over SNOMED CT and NCI Thesaurus nodes to enable cross-institutional data federation for clinical trial recruitment and pharmacovigilance.

Pharmacovigilance and Drug Safety

Post-market drug safety surveillance—detecting adverse event signals from real-world data—is an application where knowledge graphs provide immediate, measurable value. The FDA's Sentinel System, which monitors drug safety across claims data covering over 100 million patients, has incorporated graph-based analytics to surface multi-drug interaction signals that time-series approaches miss. By representing co-prescription patterns, adverse event reports, and biological mechanism relationships as a unified graph, Sentinel can detect safety signals weeks earlier than the previous statistical threshold approach.

Elsevier's Embase and its linked biomedical knowledge infrastructure powers pharmacovigilance workflows at most top-20 pharmaceutical companies, connecting adverse event reports in the FAERS database to mechanistic pathways in the molecular literature. Palantir's AIP (Artificial Intelligence Platform) overlays an LLM reasoning layer on top of this graph infrastructure, enabling safety scientists to query in natural language and receive graph-traversal-grounded responses about emerging drug safety signals.

Applications & Use Cases

Drug Target Identification & Repurposing

Pharmaceutical companies traverse biomedical knowledge graphs connecting genes, proteins, pathways, and diseases to identify novel drug targets and repurposing candidates. AstraZeneca's open knowledge graph and Harvard's PrimeKG are used across the industry; Insilico Medicine used graph-based target identification to advance INS018_055 to Phase II trials for IPF in under 30 months.

Clinical Decision Support

Hospital systems deploy GraphRAG architectures over clinical knowledge bases and patient record graphs to surface relationship-aware alerts and recommendations. Microsoft/Nuance's DAX Copilot and Epic's embedded decision support modules both use graph traversal to connect symptom presentations to relevant guidelines, contraindications, and similar patient outcomes.

Precision Oncology

Tempus and Flatiron Health connect patient genomic profiles to treatment outcomes across hundreds of thousands of real-world oncology cases. Physicians querying these platforms receive therapy recommendations derived from graph traversal over biomarker-to-outcome edges, enabling molecularly targeted treatment selection beyond what any single institution could support alone.

Rare Disease Diagnosis

Fabric Genomics, FDNA (Face2Gene), and similar platforms traverse phenotype ontology graphs (HPO, OMIM) against variant databases to rank differential diagnoses for patients with complex or undiagnosed presentations. These tools compress average time-to-diagnosis from years to days for conditions like Coffin-Siris syndrome and NGLY1 deficiency.

Pharmacovigilance & Drug Safety

The FDA's Sentinel System and pharma company safety teams use knowledge graphs to detect multi-drug adverse event signals by connecting FAERS reports, co-prescription patterns, and biological mechanism pathways. Graph-based detection identifies interaction signals weeks earlier than traditional disproportionality analysis on flat data.

Clinical Trial Recruitment & Design

Stardog and Palantir-backed graph platforms traverse patient record graphs against trial eligibility criteria encoded as ontological queries, dramatically improving recruitment speed. Novartis and Roche use graph-based cohort identification to reduce patient recruitment timelines by 30–50% compared to manual chart review processes.

Key Players

AstraZeneca — Operates one of the most widely cited open biomedical knowledge graphs, integrating Open Targets, ChEMBL, and UniProt for drug discovery; also contributes to the broader open-science biomedical KG ecosystem.
Tempus AI — Precision medicine platform built on a multimodal knowledge graph connecting genomics, imaging, clinical outcomes, and real-world evidence from 800+ oncology partners; powers therapy selection and clinical trial matching.
Flatiron Health (Roche) — Oncology-focused knowledge graph linking cancer types, biomarkers, treatment regimens, and real-world outcomes from 800+ US cancer clinics; primary source for FDA real-world evidence submissions.
Palantir Technologies — Foundry and AIP platforms deployed at NHS UK and major US health systems, representing patient and operational data as federated knowledge graphs with LLM-powered query layers.
Microsoft (Nuance/Azure Health) — Azure Health Data Services with GraphRAG capabilities for clinical decision support; DAX Copilot uses graph-based entity reasoning for ambient clinical documentation at thousands of US hospitals.
Stardog — Enterprise knowledge graph platform widely used in life sciences for drug safety, clinical trial management, and cross-institutional data federation using SPARQL reasoning over SNOMED CT and NCI Thesaurus.
Neo4j — Graph database underpinning custom healthcare knowledge graph deployments at Pfizer, Johnson & Johnson, and AstraZeneca for drug interaction analysis and clinical research data integration.
UCSF / SPOKE Consortium — Scalable Precision Medicine Oriented Knowledge Engine with 40M+ nodes; licensed to pharmaceutical partners and serves as a research reference graph for precision medicine studies globally.

Challenges & Considerations

Privacy and HIPAA Compliance — Patient-level knowledge graphs containing linked clinical, genomic, and behavioral data present significant de-identification challenges. Graph structure itself can enable re-identification even when individual attributes are anonymized—connecting age, rare diagnosis, ZIP code, and prescription creates a unique fingerprint. Healthcare organizations must implement differential privacy techniques and strict access controls at the edge level, not just the node level.
Ontological Fragmentation — Healthcare uses dozens of competing coding systems (ICD-10, SNOMED CT, LOINC, RxNorm, CPT, HPO) that were never designed to interoperate. Mapping entities across these systems—a prerequisite for building a unified knowledge graph—requires substantial curation effort and ongoing maintenance as codes are revised. SNOMED CT alone has over 350,000 active concepts and releases updates twice yearly.
Data Quality and Completeness — Clinical knowledge graphs are only as good as the underlying EHR documentation, which is notoriously inconsistent. Diagnosis codes are frequently applied for billing rather than clinical accuracy, lab values lack standardized reference ranges, and social determinants of health are underrecorded. Garbage-in, garbage-out dynamics are amplified in graph structures where erroneous edges propagate through traversal.
Regulatory and Liability Uncertainty — AI systems that use knowledge graph traversal to inform clinical decisions exist in a gray area under FDA Software as a Medical Device (SaMD) guidance. It remains unclear whether GraphRAG-powered decision support tools require 510(k) clearance, and liability for graph-derived recommendations that contribute to adverse outcomes has not been tested in US courts. This regulatory uncertainty slows enterprise deployment.
Scalability and Query Performance — Multi-hop traversal queries over graphs with hundreds of millions of nodes and billions of edges—typical for enterprise biomedical knowledge graphs—require specialized infrastructure. Sub-second query latency at clinical point-of-care is not achievable with off-the-shelf graph databases for the most complex queries, necessitating expensive pre-computation, graph partitioning strategies, and custom indexing.
Institutional Data Silos — The most valuable healthcare knowledge graphs require data from multiple institutions, but competitive dynamics, legal risk aversion, and inconsistent data governance frameworks make inter-institutional graph federation difficult. Even within a single health system, clinical, billing, pharmacy, and genomics data often reside in incompatible systems controlled by different departments with different data stewardship policies.