Clinical Development
Clinical development runs on data scattered across trial management systems, EDC, patient records, regulatory submissions, and a growing body of external knowledge. DISQOVER connects it into a single, searchable knowledge layer, so clinical teams and their AI systems work with concepts, not source-specific labels.
The data you already have
Clinical data integration means connecting clinical trial systems, patient records, and public research data into one place a research team can query, without migrating or restructuring the source systems themselves.
You do not need new data to get value from DISQOVER: clinical development teams already work with a recognizable set of public, proprietary, and licensed sources, and DISQOVER connects them.
How it works
DISQOVER resolves two problems at once: differences in terminology across systems, and the relationships between concepts that span domains, adding what a regulated environment needs on top.
Terminology differences across systems, disease classifications, compound identifiers, and endpoint names resolve to a single concept automatically. A search does not need to know which system used which label.
Relationships between trials, indications, biomarkers, and compounds are modeled as a knowledge graph, so a user or an AI agent can move from one concept to a connected one directly, instead of assembling the connection by hand.
Every result links back to its source, version, and context, which matters for audit trails in a GxP environment and for citation when the same answer is used by an AI system.
Customer story
Boehringer Ingelheim uses DISQOVER to link, enrich, and harmonize decades of historical trial data for fast, ontology-driven cohort selection. The result: researchers define cohorts across trials in a few clicks, with consent and eligibility built in from the start.
DISQOVER harmonizes patient-level data, including demographics, CRFs, adverse events, lab results, subject assignments to study arms and treatments received, and metadata on bio samples and NGS experiments, integrated with all consent information from study- and country-level Informed Consent Forms.
Outcomes
Planning any trial means looking at what has already been done, both in the field and inside your own organization: which designs were used, which endpoints were measured, and what the data showed. When that information sits across separate systems and public registries, every review starts with a data request. DISQOVER connects your study, cohort, and subject data with real-world data and public trial registries, so clinical teams can answer those questions themselves.
Before finalizing a protocol, see which trials have already run in the indication, with their designs, endpoints, and comparators, across public registries and your own study records. The return is protocol decisions grounded in what has already been tested.
Trial data is harmonized into one concept model, so a search finds relevant trials however each source names the disease.
Checking what earlier studies measured, or whether data already exists, usually means waiting in a request queue. Clinical teams can answer these questions themselves, within the access rights they already hold. The return is faster study planning and no data requested twice.
Access controls apply the same way in the interface and the API, so restricted data stays restricted.
DISQOVER supports natural language search directly in DISQOVER Explore, and through its MCP interface, AI agents and LLM applications can query the same knowledge layer in natural language, getting grounded, provenance-backed answers back either way.
DISQOVER ships with industry-standard ontologies and domain-specific entity models out of the box, so clinical concepts are understood from day one, without months of configuration. For clinical development, that includes:
CDISC standards, such as SDTM and ADaM.
DISQOVER tracks which sources contributed to every data point, so visibility can be restricted by user group without separate access-management tooling, consistent across the UI and the API.
The same harmonized knowledge layer is available through DISQOVER Explore for interactive discovery, and through API and MCP for downstream applications and AI systems.
Governance
Reusing patient data for research purposes has to clear an ethics or data governance review first. Four DISQOVER capabilities make that review fast without loosening what it protects.
Every query carries a record of its source, transformations, and applicable governance rules, giving reviewers a ready-made audit trail instead of one assembled after the fact.
Users see population-level summaries, counts, indications, age ranges, without touching individual records, enough to know whether it's worth moving forward, turning a broad request ("all oncology trial data") into a scoped one ("400 subjects across three studies").
A fixed threshold (commonly set at 10) automatically suppresses any query returning fewer subjects than that, at both the individual and aggregate level, enforced in the semantic layer itself so it can't be bypassed through a different tool or downstream via an LLM.
If the end user would still like to access the restricted data, a request can be submitted directly from the platform, carrying its filters, consent rules, and lineage into whatever governance process the organization already runs. The platform scopes and relays the request; the decision to grant access stays within the enterprise workflows.
Together, this turns a request that traditionally takes weeks to assemble into one an ethics or governance reviewer can clear in hours.
FAQ
DISQOVER connects internal clinical trial data, such as protocols, patient records, and EDC data, with public sources like ClinicalTrials.gov and PubMed through its semantic mediation layer, which not only ingests data but also resolves terminology differences automatically. This step is made possible by the knowledge graph backbone, which models relationships between concepts like indications, biomarkers, and compounds. Internal and public data are queried through the same interface, without moving data out of the systems where it already lives.
A semantic layer resolves terminology differences across clinical systems, for example recognizing "EGFR," "ErbB1," and "HER1" as the same target. DISQOVER goes further than a standard semantic layer by also modeling relationships between concepts like trials, indications, and compounds as a connected knowledge graph. For clinical development, that combination means a team can search across trial systems, patient records, and public data as one connected body of knowledge, without needing to know how each source originally structured or labeled its data.
Yes. Through DISQOVER's MCP interface, AI agents and LLM applications can query the harmonized knowledge layer directly, in natural language, and receive grounded, provenance-backed answers. Role-based access rules are enforced consistently whether data is reached through the interface, API, or MCP. Compliance-sensitive processes involving patient consent still benefit from human oversight; agentic access extends what a governed layer already enforces, rather than replacing that governance.
DISQOVER harmonizes legacy trial data into CDISC SDTM in two steps: structural harmonization, mapping source data onto standard SDTM domains, followed by content harmonization, resolving inconsistent coding (for example, gender recorded as "male," "female," "F," "M," "0," or "1") into one standard value. Content harmonization is typically the more resource-intensive step, especially across decades of non-standardized historical studies.
Related resources
Talk to us about connecting your trial data systems, patient records, and public data into one searchable knowledge layer.
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